lmg-anon commited on
Commit
fe6945d
·
verified ·
1 Parent(s): 404fbba

Upload folder using huggingface_hub

Browse files
Files changed (48) hide show
  1. checkpoint-100/README.md +204 -0
  2. checkpoint-100/adapter_config.json +31 -0
  3. checkpoint-100/adapter_model.safetensors +3 -0
  4. checkpoint-100/optimizer.pt +3 -0
  5. checkpoint-100/rng_state.pth +3 -0
  6. checkpoint-100/scheduler.pt +3 -0
  7. checkpoint-100/trainer_state.json +621 -0
  8. checkpoint-100/training_args.bin +3 -0
  9. checkpoint-150/README.md +204 -0
  10. checkpoint-150/adapter_config.json +31 -0
  11. checkpoint-150/adapter_model.safetensors +3 -0
  12. checkpoint-150/optimizer.pt +3 -0
  13. checkpoint-150/rng_state.pth +3 -0
  14. checkpoint-150/scheduler.pt +3 -0
  15. checkpoint-150/trainer_state.json +921 -0
  16. checkpoint-150/training_args.bin +3 -0
  17. checkpoint-200/README.md +204 -0
  18. checkpoint-200/adapter_config.json +31 -0
  19. checkpoint-200/adapter_model.safetensors +3 -0
  20. checkpoint-200/optimizer.pt +3 -0
  21. checkpoint-200/rng_state.pth +3 -0
  22. checkpoint-200/scheduler.pt +3 -0
  23. checkpoint-200/trainer_state.json +1221 -0
  24. checkpoint-200/training_args.bin +3 -0
  25. checkpoint-250/README.md +204 -0
  26. checkpoint-250/adapter_config.json +31 -0
  27. checkpoint-250/adapter_model.safetensors +3 -0
  28. checkpoint-250/optimizer.pt +3 -0
  29. checkpoint-250/rng_state.pth +3 -0
  30. checkpoint-250/scheduler.pt +3 -0
  31. checkpoint-250/trainer_state.json +1521 -0
  32. checkpoint-250/training_args.bin +3 -0
  33. checkpoint-300/README.md +204 -0
  34. checkpoint-300/adapter_config.json +31 -0
  35. checkpoint-300/adapter_model.safetensors +3 -0
  36. checkpoint-300/optimizer.pt +3 -0
  37. checkpoint-300/rng_state.pth +3 -0
  38. checkpoint-300/scheduler.pt +3 -0
  39. checkpoint-300/trainer_state.json +1821 -0
  40. checkpoint-300/training_args.bin +3 -0
  41. checkpoint-50/README.md +204 -0
  42. checkpoint-50/adapter_config.json +31 -0
  43. checkpoint-50/adapter_model.safetensors +3 -0
  44. checkpoint-50/optimizer.pt +3 -0
  45. checkpoint-50/rng_state.pth +3 -0
  46. checkpoint-50/scheduler.pt +3 -0
  47. checkpoint-50/trainer_state.json +321 -0
  48. checkpoint-50/training_args.bin +3 -0
checkpoint-100/README.md ADDED
@@ -0,0 +1,204 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ library_name: peft
3
+ base_model: unsloth/llama-2-7b
4
+ ---
5
+
6
+ # Model Card for Model ID
7
+
8
+ <!-- Provide a quick summary of what the model is/does. -->
9
+
10
+
11
+
12
+ ## Model Details
13
+
14
+ ### Model Description
15
+
16
+ <!-- Provide a longer summary of what this model is. -->
17
+
18
+
19
+
20
+ - **Developed by:** [More Information Needed]
21
+ - **Funded by [optional]:** [More Information Needed]
22
+ - **Shared by [optional]:** [More Information Needed]
23
+ - **Model type:** [More Information Needed]
24
+ - **Language(s) (NLP):** [More Information Needed]
25
+ - **License:** [More Information Needed]
26
+ - **Finetuned from model [optional]:** [More Information Needed]
27
+
28
+ ### Model Sources [optional]
29
+
30
+ <!-- Provide the basic links for the model. -->
31
+
32
+ - **Repository:** [More Information Needed]
33
+ - **Paper [optional]:** [More Information Needed]
34
+ - **Demo [optional]:** [More Information Needed]
35
+
36
+ ## Uses
37
+
38
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
39
+
40
+ ### Direct Use
41
+
42
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
43
+
44
+ [More Information Needed]
45
+
46
+ ### Downstream Use [optional]
47
+
48
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
49
+
50
+ [More Information Needed]
51
+
52
+ ### Out-of-Scope Use
53
+
54
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
55
+
56
+ [More Information Needed]
57
+
58
+ ## Bias, Risks, and Limitations
59
+
60
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
61
+
62
+ [More Information Needed]
63
+
64
+ ### Recommendations
65
+
66
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
67
+
68
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
69
+
70
+ ## How to Get Started with the Model
71
+
72
+ Use the code below to get started with the model.
73
+
74
+ [More Information Needed]
75
+
76
+ ## Training Details
77
+
78
+ ### Training Data
79
+
80
+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
81
+
82
+ [More Information Needed]
83
+
84
+ ### Training Procedure
85
+
86
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
87
+
88
+ #### Preprocessing [optional]
89
+
90
+ [More Information Needed]
91
+
92
+
93
+ #### Training Hyperparameters
94
+
95
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
96
+
97
+ #### Speeds, Sizes, Times [optional]
98
+
99
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
100
+
101
+ [More Information Needed]
102
+
103
+ ## Evaluation
104
+
105
+ <!-- This section describes the evaluation protocols and provides the results. -->
106
+
107
+ ### Testing Data, Factors & Metrics
108
+
109
+ #### Testing Data
110
+
111
+ <!-- This should link to a Dataset Card if possible. -->
112
+
113
+ [More Information Needed]
114
+
115
+ #### Factors
116
+
117
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
118
+
119
+ [More Information Needed]
120
+
121
+ #### Metrics
122
+
123
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
124
+
125
+ [More Information Needed]
126
+
127
+ ### Results
128
+
129
+ [More Information Needed]
130
+
131
+ #### Summary
132
+
133
+
134
+
135
+ ## Model Examination [optional]
136
+
137
+ <!-- Relevant interpretability work for the model goes here -->
138
+
139
+ [More Information Needed]
140
+
141
+ ## Environmental Impact
142
+
143
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
144
+
145
+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
146
+
147
+ - **Hardware Type:** [More Information Needed]
148
+ - **Hours used:** [More Information Needed]
149
+ - **Cloud Provider:** [More Information Needed]
150
+ - **Compute Region:** [More Information Needed]
151
+ - **Carbon Emitted:** [More Information Needed]
152
+
153
+ ## Technical Specifications [optional]
154
+
155
+ ### Model Architecture and Objective
156
+
157
+ [More Information Needed]
158
+
159
+ ### Compute Infrastructure
160
+
161
+ [More Information Needed]
162
+
163
+ #### Hardware
164
+
165
+ [More Information Needed]
166
+
167
+ #### Software
168
+
169
+ [More Information Needed]
170
+
171
+ ## Citation [optional]
172
+
173
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
174
+
175
+ **BibTeX:**
176
+
177
+ [More Information Needed]
178
+
179
+ **APA:**
180
+
181
+ [More Information Needed]
182
+
183
+ ## Glossary [optional]
184
+
185
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
186
+
187
+ [More Information Needed]
188
+
189
+ ## More Information [optional]
190
+
191
+ [More Information Needed]
192
+
193
+ ## Model Card Authors [optional]
194
+
195
+ [More Information Needed]
196
+
197
+ ## Model Card Contact
198
+
199
+ [More Information Needed]
200
+
201
+
202
+ ### Framework versions
203
+
204
+ - PEFT 0.7.1
checkpoint-100/adapter_config.json ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alpha_pattern": {},
3
+ "auto_mapping": null,
4
+ "base_model_name_or_path": "unsloth/llama-2-7b",
5
+ "bias": "none",
6
+ "fan_in_fan_out": false,
7
+ "inference_mode": true,
8
+ "init_lora_weights": true,
9
+ "layers_pattern": null,
10
+ "layers_to_transform": null,
11
+ "loftq_config": {},
12
+ "lora_alpha": 16,
13
+ "lora_dropout": 0,
14
+ "megatron_config": null,
15
+ "megatron_core": "megatron.core",
16
+ "modules_to_save": null,
17
+ "peft_type": "LORA",
18
+ "r": 128,
19
+ "rank_pattern": {},
20
+ "revision": "unsloth",
21
+ "target_modules": [
22
+ "up_proj",
23
+ "k_proj",
24
+ "v_proj",
25
+ "o_proj",
26
+ "q_proj",
27
+ "down_proj",
28
+ "gate_proj"
29
+ ],
30
+ "task_type": "CAUSAL_LM"
31
+ }
checkpoint-100/adapter_model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:bd1c2f62963986e49e3753e28056cb583e0cd17d25ad32940dea6a227b54c2d8
3
+ size 1279323952
checkpoint-100/optimizer.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:a815144a130af4bda100da4423da605db5afdb8d75410221314ebc6cdb5ddace
3
+ size 641407572
checkpoint-100/rng_state.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:fe2e145b09b2faab2c45440a9233e2700f8dca5428319c1eb306332f174e4af7
3
+ size 14244
checkpoint-100/scheduler.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:689f72113fdf41ea464c64f4bb4febd9d2183acf7aaaf679f0ca1b31cda595bd
3
+ size 1064
checkpoint-100/trainer_state.json ADDED
@@ -0,0 +1,621 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "best_metric": null,
3
+ "best_model_checkpoint": null,
4
+ "epoch": 0.297441998810232,
5
+ "eval_steps": 500,
6
+ "global_step": 100,
7
+ "is_hyper_param_search": false,
8
+ "is_local_process_zero": true,
9
+ "is_world_process_zero": true,
10
+ "log_history": [
11
+ {
12
+ "epoch": 0.0,
13
+ "learning_rate": 9.285714285714285e-05,
14
+ "loss": 2.4427,
15
+ "step": 1
16
+ },
17
+ {
18
+ "epoch": 0.01,
19
+ "learning_rate": 0.0001857142857142857,
20
+ "loss": 2.3973,
21
+ "step": 2
22
+ },
23
+ {
24
+ "epoch": 0.01,
25
+ "learning_rate": 0.00027857142857142854,
26
+ "loss": 2.341,
27
+ "step": 3
28
+ },
29
+ {
30
+ "epoch": 0.01,
31
+ "learning_rate": 0.0003714285714285714,
32
+ "loss": 2.1281,
33
+ "step": 4
34
+ },
35
+ {
36
+ "epoch": 0.01,
37
+ "learning_rate": 0.0004642857142857143,
38
+ "loss": 1.4346,
39
+ "step": 5
40
+ },
41
+ {
42
+ "epoch": 0.02,
43
+ "learning_rate": 0.0005571428571428571,
44
+ "loss": 1.1715,
45
+ "step": 6
46
+ },
47
+ {
48
+ "epoch": 0.02,
49
+ "learning_rate": 0.00065,
50
+ "loss": 1.086,
51
+ "step": 7
52
+ },
53
+ {
54
+ "epoch": 0.02,
55
+ "learning_rate": 0.0006499851830773117,
56
+ "loss": 0.9921,
57
+ "step": 8
58
+ },
59
+ {
60
+ "epoch": 0.03,
61
+ "learning_rate": 0.00064994073366027,
62
+ "loss": 0.9231,
63
+ "step": 9
64
+ },
65
+ {
66
+ "epoch": 0.03,
67
+ "learning_rate": 0.0006498666558018197,
68
+ "loss": 0.9343,
69
+ "step": 10
70
+ },
71
+ {
72
+ "epoch": 0.03,
73
+ "learning_rate": 0.0006497629562564588,
74
+ "loss": 0.9191,
75
+ "step": 11
76
+ },
77
+ {
78
+ "epoch": 0.04,
79
+ "learning_rate": 0.0006496296444796219,
80
+ "loss": 0.8791,
81
+ "step": 12
82
+ },
83
+ {
84
+ "epoch": 0.04,
85
+ "learning_rate": 0.0006494667326268186,
86
+ "loss": 0.8632,
87
+ "step": 13
88
+ },
89
+ {
90
+ "epoch": 0.04,
91
+ "learning_rate": 0.0006492742355525248,
92
+ "loss": 0.9267,
93
+ "step": 14
94
+ },
95
+ {
96
+ "epoch": 0.04,
97
+ "learning_rate": 0.0006490521708088281,
98
+ "loss": 0.8644,
99
+ "step": 15
100
+ },
101
+ {
102
+ "epoch": 0.05,
103
+ "learning_rate": 0.000648800558643828,
104
+ "loss": 0.8353,
105
+ "step": 16
106
+ },
107
+ {
108
+ "epoch": 0.05,
109
+ "learning_rate": 0.0006485194219997891,
110
+ "loss": 0.8482,
111
+ "step": 17
112
+ },
113
+ {
114
+ "epoch": 0.05,
115
+ "learning_rate": 0.0006482087865110493,
116
+ "loss": 0.8587,
117
+ "step": 18
118
+ },
119
+ {
120
+ "epoch": 0.06,
121
+ "learning_rate": 0.0006478686805016826,
122
+ "loss": 0.9134,
123
+ "step": 19
124
+ },
125
+ {
126
+ "epoch": 0.06,
127
+ "learning_rate": 0.0006474991349829163,
128
+ "loss": 0.8238,
129
+ "step": 20
130
+ },
131
+ {
132
+ "epoch": 0.06,
133
+ "learning_rate": 0.0006471001836503035,
134
+ "loss": 0.8329,
135
+ "step": 21
136
+ },
137
+ {
138
+ "epoch": 0.07,
139
+ "learning_rate": 0.0006466718628806508,
140
+ "loss": 0.7995,
141
+ "step": 22
142
+ },
143
+ {
144
+ "epoch": 0.07,
145
+ "learning_rate": 0.0006462142117287011,
146
+ "loss": 0.8363,
147
+ "step": 23
148
+ },
149
+ {
150
+ "epoch": 0.07,
151
+ "learning_rate": 0.0006457272719235728,
152
+ "loss": 0.7942,
153
+ "step": 24
154
+ },
155
+ {
156
+ "epoch": 0.07,
157
+ "learning_rate": 0.0006452110878649547,
158
+ "loss": 0.858,
159
+ "step": 25
160
+ },
161
+ {
162
+ "epoch": 0.08,
163
+ "learning_rate": 0.0006446657066190579,
164
+ "loss": 0.8474,
165
+ "step": 26
166
+ },
167
+ {
168
+ "epoch": 0.08,
169
+ "learning_rate": 0.000644091177914324,
170
+ "loss": 0.8175,
171
+ "step": 27
172
+ },
173
+ {
174
+ "epoch": 0.08,
175
+ "learning_rate": 0.0006434875541368907,
176
+ "loss": 0.7821,
177
+ "step": 28
178
+ },
179
+ {
180
+ "epoch": 0.09,
181
+ "learning_rate": 0.0006428548903258156,
182
+ "loss": 0.8583,
183
+ "step": 29
184
+ },
185
+ {
186
+ "epoch": 0.09,
187
+ "learning_rate": 0.0006421932441680574,
188
+ "loss": 0.8071,
189
+ "step": 30
190
+ },
191
+ {
192
+ "epoch": 0.09,
193
+ "learning_rate": 0.0006415026759932158,
194
+ "loss": 0.805,
195
+ "step": 31
196
+ },
197
+ {
198
+ "epoch": 0.1,
199
+ "learning_rate": 0.0006407832487680309,
200
+ "loss": 0.881,
201
+ "step": 32
202
+ },
203
+ {
204
+ "epoch": 0.1,
205
+ "learning_rate": 0.0006400350280906415,
206
+ "loss": 0.8302,
207
+ "step": 33
208
+ },
209
+ {
210
+ "epoch": 0.1,
211
+ "learning_rate": 0.0006392580821846041,
212
+ "loss": 0.8456,
213
+ "step": 34
214
+ },
215
+ {
216
+ "epoch": 0.1,
217
+ "learning_rate": 0.0006384524818926723,
218
+ "loss": 0.8067,
219
+ "step": 35
220
+ },
221
+ {
222
+ "epoch": 0.11,
223
+ "learning_rate": 0.0006376183006703367,
224
+ "loss": 0.8307,
225
+ "step": 36
226
+ },
227
+ {
228
+ "epoch": 0.11,
229
+ "learning_rate": 0.0006367556145791275,
230
+ "loss": 0.8347,
231
+ "step": 37
232
+ },
233
+ {
234
+ "epoch": 0.11,
235
+ "learning_rate": 0.0006358645022796795,
236
+ "loss": 0.8086,
237
+ "step": 38
238
+ },
239
+ {
240
+ "epoch": 0.12,
241
+ "learning_rate": 0.0006349450450245589,
242
+ "loss": 0.8726,
243
+ "step": 39
244
+ },
245
+ {
246
+ "epoch": 0.12,
247
+ "learning_rate": 0.0006339973266508556,
248
+ "loss": 0.78,
249
+ "step": 40
250
+ },
251
+ {
252
+ "epoch": 0.12,
253
+ "learning_rate": 0.0006330214335725379,
254
+ "loss": 0.7902,
255
+ "step": 41
256
+ },
257
+ {
258
+ "epoch": 0.12,
259
+ "learning_rate": 0.0006320174547725736,
260
+ "loss": 0.7823,
261
+ "step": 42
262
+ },
263
+ {
264
+ "epoch": 0.13,
265
+ "learning_rate": 0.0006309854817948169,
266
+ "loss": 0.8211,
267
+ "step": 43
268
+ },
269
+ {
270
+ "epoch": 0.13,
271
+ "learning_rate": 0.0006299256087356603,
272
+ "loss": 0.8656,
273
+ "step": 44
274
+ },
275
+ {
276
+ "epoch": 0.13,
277
+ "learning_rate": 0.000628837932235456,
278
+ "loss": 0.7767,
279
+ "step": 45
280
+ },
281
+ {
282
+ "epoch": 0.14,
283
+ "learning_rate": 0.0006277225514697028,
284
+ "loss": 0.7542,
285
+ "step": 46
286
+ },
287
+ {
288
+ "epoch": 0.14,
289
+ "learning_rate": 0.0006265795681400046,
290
+ "loss": 0.8254,
291
+ "step": 47
292
+ },
293
+ {
294
+ "epoch": 0.14,
295
+ "learning_rate": 0.0006254090864647957,
296
+ "loss": 0.8099,
297
+ "step": 48
298
+ },
299
+ {
300
+ "epoch": 0.15,
301
+ "learning_rate": 0.0006242112131698394,
302
+ "loss": 0.7786,
303
+ "step": 49
304
+ },
305
+ {
306
+ "epoch": 0.15,
307
+ "learning_rate": 0.0006229860574784954,
308
+ "loss": 0.7895,
309
+ "step": 50
310
+ },
311
+ {
312
+ "epoch": 0.15,
313
+ "learning_rate": 0.0006217337311017619,
314
+ "loss": 0.8092,
315
+ "step": 51
316
+ },
317
+ {
318
+ "epoch": 0.15,
319
+ "learning_rate": 0.0006204543482280886,
320
+ "loss": 0.7998,
321
+ "step": 52
322
+ },
323
+ {
324
+ "epoch": 0.16,
325
+ "learning_rate": 0.0006191480255129656,
326
+ "loss": 0.802,
327
+ "step": 53
328
+ },
329
+ {
330
+ "epoch": 0.16,
331
+ "learning_rate": 0.0006178148820682862,
332
+ "loss": 0.7691,
333
+ "step": 54
334
+ },
335
+ {
336
+ "epoch": 0.16,
337
+ "learning_rate": 0.0006164550394514865,
338
+ "loss": 0.8572,
339
+ "step": 55
340
+ },
341
+ {
342
+ "epoch": 0.17,
343
+ "learning_rate": 0.0006150686216544614,
344
+ "loss": 0.7915,
345
+ "step": 56
346
+ },
347
+ {
348
+ "epoch": 0.17,
349
+ "learning_rate": 0.0006136557550922589,
350
+ "loss": 0.759,
351
+ "step": 57
352
+ },
353
+ {
354
+ "epoch": 0.17,
355
+ "learning_rate": 0.0006122165685915537,
356
+ "loss": 0.7282,
357
+ "step": 58
358
+ },
359
+ {
360
+ "epoch": 0.18,
361
+ "learning_rate": 0.0006107511933789002,
362
+ "loss": 0.768,
363
+ "step": 59
364
+ },
365
+ {
366
+ "epoch": 0.18,
367
+ "learning_rate": 0.0006092597630687677,
368
+ "loss": 0.7868,
369
+ "step": 60
370
+ },
371
+ {
372
+ "epoch": 0.18,
373
+ "learning_rate": 0.0006077424136513567,
374
+ "loss": 0.7827,
375
+ "step": 61
376
+ },
377
+ {
378
+ "epoch": 0.18,
379
+ "learning_rate": 0.0006061992834801996,
380
+ "loss": 0.7755,
381
+ "step": 62
382
+ },
383
+ {
384
+ "epoch": 0.19,
385
+ "learning_rate": 0.0006046305132595453,
386
+ "loss": 0.8332,
387
+ "step": 63
388
+ },
389
+ {
390
+ "epoch": 0.19,
391
+ "learning_rate": 0.0006030362460315296,
392
+ "loss": 0.7241,
393
+ "step": 64
394
+ },
395
+ {
396
+ "epoch": 0.19,
397
+ "learning_rate": 0.0006014166271631326,
398
+ "loss": 0.8074,
399
+ "step": 65
400
+ },
401
+ {
402
+ "epoch": 0.2,
403
+ "learning_rate": 0.000599771804332924,
404
+ "loss": 0.7491,
405
+ "step": 66
406
+ },
407
+ {
408
+ "epoch": 0.2,
409
+ "learning_rate": 0.0005981019275175972,
410
+ "loss": 0.8004,
411
+ "step": 67
412
+ },
413
+ {
414
+ "epoch": 0.2,
415
+ "learning_rate": 0.000596407148978295,
416
+ "loss": 0.7442,
417
+ "step": 68
418
+ },
419
+ {
420
+ "epoch": 0.21,
421
+ "learning_rate": 0.0005946876232467254,
422
+ "loss": 0.6956,
423
+ "step": 69
424
+ },
425
+ {
426
+ "epoch": 0.21,
427
+ "learning_rate": 0.0005929435071110721,
428
+ "loss": 0.7472,
429
+ "step": 70
430
+ },
431
+ {
432
+ "epoch": 0.21,
433
+ "learning_rate": 0.0005911749596016978,
434
+ "loss": 0.8097,
435
+ "step": 71
436
+ },
437
+ {
438
+ "epoch": 0.21,
439
+ "learning_rate": 0.0005893821419766438,
440
+ "loss": 0.8002,
441
+ "step": 72
442
+ },
443
+ {
444
+ "epoch": 0.22,
445
+ "learning_rate": 0.0005875652177069265,
446
+ "loss": 0.8203,
447
+ "step": 73
448
+ },
449
+ {
450
+ "epoch": 0.22,
451
+ "learning_rate": 0.0005857243524616315,
452
+ "loss": 0.7339,
453
+ "step": 74
454
+ },
455
+ {
456
+ "epoch": 0.22,
457
+ "learning_rate": 0.0005838597140928082,
458
+ "loss": 0.6844,
459
+ "step": 75
460
+ },
461
+ {
462
+ "epoch": 0.23,
463
+ "learning_rate": 0.0005819714726201646,
464
+ "loss": 0.7211,
465
+ "step": 76
466
+ },
467
+ {
468
+ "epoch": 0.23,
469
+ "learning_rate": 0.0005800598002155648,
470
+ "loss": 0.8289,
471
+ "step": 77
472
+ },
473
+ {
474
+ "epoch": 0.23,
475
+ "learning_rate": 0.0005781248711873302,
476
+ "loss": 0.7686,
477
+ "step": 78
478
+ },
479
+ {
480
+ "epoch": 0.23,
481
+ "learning_rate": 0.0005761668619643458,
482
+ "loss": 0.7618,
483
+ "step": 79
484
+ },
485
+ {
486
+ "epoch": 0.24,
487
+ "learning_rate": 0.0005741859510799734,
488
+ "loss": 0.779,
489
+ "step": 80
490
+ },
491
+ {
492
+ "epoch": 0.24,
493
+ "learning_rate": 0.0005721823191557725,
494
+ "loss": 0.7599,
495
+ "step": 81
496
+ },
497
+ {
498
+ "epoch": 0.24,
499
+ "learning_rate": 0.0005701561488850312,
500
+ "loss": 0.7495,
501
+ "step": 82
502
+ },
503
+ {
504
+ "epoch": 0.25,
505
+ "learning_rate": 0.000568107625016108,
506
+ "loss": 0.6977,
507
+ "step": 83
508
+ },
509
+ {
510
+ "epoch": 0.25,
511
+ "learning_rate": 0.0005660369343355862,
512
+ "loss": 0.757,
513
+ "step": 84
514
+ },
515
+ {
516
+ "epoch": 0.25,
517
+ "learning_rate": 0.0005639442656512426,
518
+ "loss": 0.8295,
519
+ "step": 85
520
+ },
521
+ {
522
+ "epoch": 0.26,
523
+ "learning_rate": 0.0005618298097748316,
524
+ "loss": 0.7394,
525
+ "step": 86
526
+ },
527
+ {
528
+ "epoch": 0.26,
529
+ "learning_rate": 0.0005596937595046872,
530
+ "loss": 0.7728,
531
+ "step": 87
532
+ },
533
+ {
534
+ "epoch": 0.26,
535
+ "learning_rate": 0.0005575363096081429,
536
+ "loss": 0.7721,
537
+ "step": 88
538
+ },
539
+ {
540
+ "epoch": 0.26,
541
+ "learning_rate": 0.0005553576568037731,
542
+ "loss": 0.7191,
543
+ "step": 89
544
+ },
545
+ {
546
+ "epoch": 0.27,
547
+ "learning_rate": 0.0005531579997434555,
548
+ "loss": 0.7594,
549
+ "step": 90
550
+ },
551
+ {
552
+ "epoch": 0.27,
553
+ "learning_rate": 0.0005509375389942588,
554
+ "loss": 0.7511,
555
+ "step": 91
556
+ },
557
+ {
558
+ "epoch": 0.27,
559
+ "learning_rate": 0.0005486964770201533,
560
+ "loss": 0.7644,
561
+ "step": 92
562
+ },
563
+ {
564
+ "epoch": 0.28,
565
+ "learning_rate": 0.0005464350181635519,
566
+ "loss": 0.7403,
567
+ "step": 93
568
+ },
569
+ {
570
+ "epoch": 0.28,
571
+ "learning_rate": 0.000544153368626676,
572
+ "loss": 0.7204,
573
+ "step": 94
574
+ },
575
+ {
576
+ "epoch": 0.28,
577
+ "learning_rate": 0.0005418517364527552,
578
+ "loss": 0.7358,
579
+ "step": 95
580
+ },
581
+ {
582
+ "epoch": 0.29,
583
+ "learning_rate": 0.0005395303315070571,
584
+ "loss": 0.7607,
585
+ "step": 96
586
+ },
587
+ {
588
+ "epoch": 0.29,
589
+ "learning_rate": 0.0005371893654577517,
590
+ "loss": 0.7505,
591
+ "step": 97
592
+ },
593
+ {
594
+ "epoch": 0.29,
595
+ "learning_rate": 0.0005348290517566107,
596
+ "loss": 0.7274,
597
+ "step": 98
598
+ },
599
+ {
600
+ "epoch": 0.29,
601
+ "learning_rate": 0.0005324496056195461,
602
+ "loss": 0.8161,
603
+ "step": 99
604
+ },
605
+ {
606
+ "epoch": 0.3,
607
+ "learning_rate": 0.0005300512440069852,
608
+ "loss": 0.7043,
609
+ "step": 100
610
+ }
611
+ ],
612
+ "logging_steps": 1,
613
+ "max_steps": 336,
614
+ "num_input_tokens_seen": 0,
615
+ "num_train_epochs": 1,
616
+ "save_steps": 50,
617
+ "total_flos": 1.2671181951152947e+17,
618
+ "train_batch_size": 6,
619
+ "trial_name": null,
620
+ "trial_params": null
621
+ }
checkpoint-100/training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:3a720d2daf4afe192195297ee83d1a8834602d7e8eda493eaba5ee762dd57a90
3
+ size 4664
checkpoint-150/README.md ADDED
@@ -0,0 +1,204 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ library_name: peft
3
+ base_model: unsloth/llama-2-7b
4
+ ---
5
+
6
+ # Model Card for Model ID
7
+
8
+ <!-- Provide a quick summary of what the model is/does. -->
9
+
10
+
11
+
12
+ ## Model Details
13
+
14
+ ### Model Description
15
+
16
+ <!-- Provide a longer summary of what this model is. -->
17
+
18
+
19
+
20
+ - **Developed by:** [More Information Needed]
21
+ - **Funded by [optional]:** [More Information Needed]
22
+ - **Shared by [optional]:** [More Information Needed]
23
+ - **Model type:** [More Information Needed]
24
+ - **Language(s) (NLP):** [More Information Needed]
25
+ - **License:** [More Information Needed]
26
+ - **Finetuned from model [optional]:** [More Information Needed]
27
+
28
+ ### Model Sources [optional]
29
+
30
+ <!-- Provide the basic links for the model. -->
31
+
32
+ - **Repository:** [More Information Needed]
33
+ - **Paper [optional]:** [More Information Needed]
34
+ - **Demo [optional]:** [More Information Needed]
35
+
36
+ ## Uses
37
+
38
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
39
+
40
+ ### Direct Use
41
+
42
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
43
+
44
+ [More Information Needed]
45
+
46
+ ### Downstream Use [optional]
47
+
48
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
49
+
50
+ [More Information Needed]
51
+
52
+ ### Out-of-Scope Use
53
+
54
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
55
+
56
+ [More Information Needed]
57
+
58
+ ## Bias, Risks, and Limitations
59
+
60
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
61
+
62
+ [More Information Needed]
63
+
64
+ ### Recommendations
65
+
66
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
67
+
68
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
69
+
70
+ ## How to Get Started with the Model
71
+
72
+ Use the code below to get started with the model.
73
+
74
+ [More Information Needed]
75
+
76
+ ## Training Details
77
+
78
+ ### Training Data
79
+
80
+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
81
+
82
+ [More Information Needed]
83
+
84
+ ### Training Procedure
85
+
86
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
87
+
88
+ #### Preprocessing [optional]
89
+
90
+ [More Information Needed]
91
+
92
+
93
+ #### Training Hyperparameters
94
+
95
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
96
+
97
+ #### Speeds, Sizes, Times [optional]
98
+
99
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
100
+
101
+ [More Information Needed]
102
+
103
+ ## Evaluation
104
+
105
+ <!-- This section describes the evaluation protocols and provides the results. -->
106
+
107
+ ### Testing Data, Factors & Metrics
108
+
109
+ #### Testing Data
110
+
111
+ <!-- This should link to a Dataset Card if possible. -->
112
+
113
+ [More Information Needed]
114
+
115
+ #### Factors
116
+
117
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
118
+
119
+ [More Information Needed]
120
+
121
+ #### Metrics
122
+
123
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
124
+
125
+ [More Information Needed]
126
+
127
+ ### Results
128
+
129
+ [More Information Needed]
130
+
131
+ #### Summary
132
+
133
+
134
+
135
+ ## Model Examination [optional]
136
+
137
+ <!-- Relevant interpretability work for the model goes here -->
138
+
139
+ [More Information Needed]
140
+
141
+ ## Environmental Impact
142
+
143
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
144
+
145
+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
146
+
147
+ - **Hardware Type:** [More Information Needed]
148
+ - **Hours used:** [More Information Needed]
149
+ - **Cloud Provider:** [More Information Needed]
150
+ - **Compute Region:** [More Information Needed]
151
+ - **Carbon Emitted:** [More Information Needed]
152
+
153
+ ## Technical Specifications [optional]
154
+
155
+ ### Model Architecture and Objective
156
+
157
+ [More Information Needed]
158
+
159
+ ### Compute Infrastructure
160
+
161
+ [More Information Needed]
162
+
163
+ #### Hardware
164
+
165
+ [More Information Needed]
166
+
167
+ #### Software
168
+
169
+ [More Information Needed]
170
+
171
+ ## Citation [optional]
172
+
173
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
174
+
175
+ **BibTeX:**
176
+
177
+ [More Information Needed]
178
+
179
+ **APA:**
180
+
181
+ [More Information Needed]
182
+
183
+ ## Glossary [optional]
184
+
185
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
186
+
187
+ [More Information Needed]
188
+
189
+ ## More Information [optional]
190
+
191
+ [More Information Needed]
192
+
193
+ ## Model Card Authors [optional]
194
+
195
+ [More Information Needed]
196
+
197
+ ## Model Card Contact
198
+
199
+ [More Information Needed]
200
+
201
+
202
+ ### Framework versions
203
+
204
+ - PEFT 0.7.1
checkpoint-150/adapter_config.json ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alpha_pattern": {},
3
+ "auto_mapping": null,
4
+ "base_model_name_or_path": "unsloth/llama-2-7b",
5
+ "bias": "none",
6
+ "fan_in_fan_out": false,
7
+ "inference_mode": true,
8
+ "init_lora_weights": true,
9
+ "layers_pattern": null,
10
+ "layers_to_transform": null,
11
+ "loftq_config": {},
12
+ "lora_alpha": 16,
13
+ "lora_dropout": 0,
14
+ "megatron_config": null,
15
+ "megatron_core": "megatron.core",
16
+ "modules_to_save": null,
17
+ "peft_type": "LORA",
18
+ "r": 128,
19
+ "rank_pattern": {},
20
+ "revision": "unsloth",
21
+ "target_modules": [
22
+ "up_proj",
23
+ "k_proj",
24
+ "v_proj",
25
+ "o_proj",
26
+ "q_proj",
27
+ "down_proj",
28
+ "gate_proj"
29
+ ],
30
+ "task_type": "CAUSAL_LM"
31
+ }
checkpoint-150/adapter_model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:1d45bae02d193daabeb44e22b77d1346b6f457fe789898c014d8ad00f3c677d6
3
+ size 1279323952
checkpoint-150/optimizer.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f113362ee9123eab45f48c8db9430dabf38a3a68f64c5d67075959f49674ee3d
3
+ size 641407572
checkpoint-150/rng_state.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:fe2e145b09b2faab2c45440a9233e2700f8dca5428319c1eb306332f174e4af7
3
+ size 14244
checkpoint-150/scheduler.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:8b554875db3ba717349f139de3f598f899689f5944f5f578487a979cb70ca3e3
3
+ size 1064
checkpoint-150/trainer_state.json ADDED
@@ -0,0 +1,921 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "best_metric": null,
3
+ "best_model_checkpoint": null,
4
+ "epoch": 0.446162998215348,
5
+ "eval_steps": 500,
6
+ "global_step": 150,
7
+ "is_hyper_param_search": false,
8
+ "is_local_process_zero": true,
9
+ "is_world_process_zero": true,
10
+ "log_history": [
11
+ {
12
+ "epoch": 0.0,
13
+ "learning_rate": 9.285714285714285e-05,
14
+ "loss": 2.4427,
15
+ "step": 1
16
+ },
17
+ {
18
+ "epoch": 0.01,
19
+ "learning_rate": 0.0001857142857142857,
20
+ "loss": 2.3973,
21
+ "step": 2
22
+ },
23
+ {
24
+ "epoch": 0.01,
25
+ "learning_rate": 0.00027857142857142854,
26
+ "loss": 2.341,
27
+ "step": 3
28
+ },
29
+ {
30
+ "epoch": 0.01,
31
+ "learning_rate": 0.0003714285714285714,
32
+ "loss": 2.1281,
33
+ "step": 4
34
+ },
35
+ {
36
+ "epoch": 0.01,
37
+ "learning_rate": 0.0004642857142857143,
38
+ "loss": 1.4346,
39
+ "step": 5
40
+ },
41
+ {
42
+ "epoch": 0.02,
43
+ "learning_rate": 0.0005571428571428571,
44
+ "loss": 1.1715,
45
+ "step": 6
46
+ },
47
+ {
48
+ "epoch": 0.02,
49
+ "learning_rate": 0.00065,
50
+ "loss": 1.086,
51
+ "step": 7
52
+ },
53
+ {
54
+ "epoch": 0.02,
55
+ "learning_rate": 0.0006499851830773117,
56
+ "loss": 0.9921,
57
+ "step": 8
58
+ },
59
+ {
60
+ "epoch": 0.03,
61
+ "learning_rate": 0.00064994073366027,
62
+ "loss": 0.9231,
63
+ "step": 9
64
+ },
65
+ {
66
+ "epoch": 0.03,
67
+ "learning_rate": 0.0006498666558018197,
68
+ "loss": 0.9343,
69
+ "step": 10
70
+ },
71
+ {
72
+ "epoch": 0.03,
73
+ "learning_rate": 0.0006497629562564588,
74
+ "loss": 0.9191,
75
+ "step": 11
76
+ },
77
+ {
78
+ "epoch": 0.04,
79
+ "learning_rate": 0.0006496296444796219,
80
+ "loss": 0.8791,
81
+ "step": 12
82
+ },
83
+ {
84
+ "epoch": 0.04,
85
+ "learning_rate": 0.0006494667326268186,
86
+ "loss": 0.8632,
87
+ "step": 13
88
+ },
89
+ {
90
+ "epoch": 0.04,
91
+ "learning_rate": 0.0006492742355525248,
92
+ "loss": 0.9267,
93
+ "step": 14
94
+ },
95
+ {
96
+ "epoch": 0.04,
97
+ "learning_rate": 0.0006490521708088281,
98
+ "loss": 0.8644,
99
+ "step": 15
100
+ },
101
+ {
102
+ "epoch": 0.05,
103
+ "learning_rate": 0.000648800558643828,
104
+ "loss": 0.8353,
105
+ "step": 16
106
+ },
107
+ {
108
+ "epoch": 0.05,
109
+ "learning_rate": 0.0006485194219997891,
110
+ "loss": 0.8482,
111
+ "step": 17
112
+ },
113
+ {
114
+ "epoch": 0.05,
115
+ "learning_rate": 0.0006482087865110493,
116
+ "loss": 0.8587,
117
+ "step": 18
118
+ },
119
+ {
120
+ "epoch": 0.06,
121
+ "learning_rate": 0.0006478686805016826,
122
+ "loss": 0.9134,
123
+ "step": 19
124
+ },
125
+ {
126
+ "epoch": 0.06,
127
+ "learning_rate": 0.0006474991349829163,
128
+ "loss": 0.8238,
129
+ "step": 20
130
+ },
131
+ {
132
+ "epoch": 0.06,
133
+ "learning_rate": 0.0006471001836503035,
134
+ "loss": 0.8329,
135
+ "step": 21
136
+ },
137
+ {
138
+ "epoch": 0.07,
139
+ "learning_rate": 0.0006466718628806508,
140
+ "loss": 0.7995,
141
+ "step": 22
142
+ },
143
+ {
144
+ "epoch": 0.07,
145
+ "learning_rate": 0.0006462142117287011,
146
+ "loss": 0.8363,
147
+ "step": 23
148
+ },
149
+ {
150
+ "epoch": 0.07,
151
+ "learning_rate": 0.0006457272719235728,
152
+ "loss": 0.7942,
153
+ "step": 24
154
+ },
155
+ {
156
+ "epoch": 0.07,
157
+ "learning_rate": 0.0006452110878649547,
158
+ "loss": 0.858,
159
+ "step": 25
160
+ },
161
+ {
162
+ "epoch": 0.08,
163
+ "learning_rate": 0.0006446657066190579,
164
+ "loss": 0.8474,
165
+ "step": 26
166
+ },
167
+ {
168
+ "epoch": 0.08,
169
+ "learning_rate": 0.000644091177914324,
170
+ "loss": 0.8175,
171
+ "step": 27
172
+ },
173
+ {
174
+ "epoch": 0.08,
175
+ "learning_rate": 0.0006434875541368907,
176
+ "loss": 0.7821,
177
+ "step": 28
178
+ },
179
+ {
180
+ "epoch": 0.09,
181
+ "learning_rate": 0.0006428548903258156,
182
+ "loss": 0.8583,
183
+ "step": 29
184
+ },
185
+ {
186
+ "epoch": 0.09,
187
+ "learning_rate": 0.0006421932441680574,
188
+ "loss": 0.8071,
189
+ "step": 30
190
+ },
191
+ {
192
+ "epoch": 0.09,
193
+ "learning_rate": 0.0006415026759932158,
194
+ "loss": 0.805,
195
+ "step": 31
196
+ },
197
+ {
198
+ "epoch": 0.1,
199
+ "learning_rate": 0.0006407832487680309,
200
+ "loss": 0.881,
201
+ "step": 32
202
+ },
203
+ {
204
+ "epoch": 0.1,
205
+ "learning_rate": 0.0006400350280906415,
206
+ "loss": 0.8302,
207
+ "step": 33
208
+ },
209
+ {
210
+ "epoch": 0.1,
211
+ "learning_rate": 0.0006392580821846041,
212
+ "loss": 0.8456,
213
+ "step": 34
214
+ },
215
+ {
216
+ "epoch": 0.1,
217
+ "learning_rate": 0.0006384524818926723,
218
+ "loss": 0.8067,
219
+ "step": 35
220
+ },
221
+ {
222
+ "epoch": 0.11,
223
+ "learning_rate": 0.0006376183006703367,
224
+ "loss": 0.8307,
225
+ "step": 36
226
+ },
227
+ {
228
+ "epoch": 0.11,
229
+ "learning_rate": 0.0006367556145791275,
230
+ "loss": 0.8347,
231
+ "step": 37
232
+ },
233
+ {
234
+ "epoch": 0.11,
235
+ "learning_rate": 0.0006358645022796795,
236
+ "loss": 0.8086,
237
+ "step": 38
238
+ },
239
+ {
240
+ "epoch": 0.12,
241
+ "learning_rate": 0.0006349450450245589,
242
+ "loss": 0.8726,
243
+ "step": 39
244
+ },
245
+ {
246
+ "epoch": 0.12,
247
+ "learning_rate": 0.0006339973266508556,
248
+ "loss": 0.78,
249
+ "step": 40
250
+ },
251
+ {
252
+ "epoch": 0.12,
253
+ "learning_rate": 0.0006330214335725379,
254
+ "loss": 0.7902,
255
+ "step": 41
256
+ },
257
+ {
258
+ "epoch": 0.12,
259
+ "learning_rate": 0.0006320174547725736,
260
+ "loss": 0.7823,
261
+ "step": 42
262
+ },
263
+ {
264
+ "epoch": 0.13,
265
+ "learning_rate": 0.0006309854817948169,
266
+ "loss": 0.8211,
267
+ "step": 43
268
+ },
269
+ {
270
+ "epoch": 0.13,
271
+ "learning_rate": 0.0006299256087356603,
272
+ "loss": 0.8656,
273
+ "step": 44
274
+ },
275
+ {
276
+ "epoch": 0.13,
277
+ "learning_rate": 0.000628837932235456,
278
+ "loss": 0.7767,
279
+ "step": 45
280
+ },
281
+ {
282
+ "epoch": 0.14,
283
+ "learning_rate": 0.0006277225514697028,
284
+ "loss": 0.7542,
285
+ "step": 46
286
+ },
287
+ {
288
+ "epoch": 0.14,
289
+ "learning_rate": 0.0006265795681400046,
290
+ "loss": 0.8254,
291
+ "step": 47
292
+ },
293
+ {
294
+ "epoch": 0.14,
295
+ "learning_rate": 0.0006254090864647957,
296
+ "loss": 0.8099,
297
+ "step": 48
298
+ },
299
+ {
300
+ "epoch": 0.15,
301
+ "learning_rate": 0.0006242112131698394,
302
+ "loss": 0.7786,
303
+ "step": 49
304
+ },
305
+ {
306
+ "epoch": 0.15,
307
+ "learning_rate": 0.0006229860574784954,
308
+ "loss": 0.7895,
309
+ "step": 50
310
+ },
311
+ {
312
+ "epoch": 0.15,
313
+ "learning_rate": 0.0006217337311017619,
314
+ "loss": 0.8092,
315
+ "step": 51
316
+ },
317
+ {
318
+ "epoch": 0.15,
319
+ "learning_rate": 0.0006204543482280886,
320
+ "loss": 0.7998,
321
+ "step": 52
322
+ },
323
+ {
324
+ "epoch": 0.16,
325
+ "learning_rate": 0.0006191480255129656,
326
+ "loss": 0.802,
327
+ "step": 53
328
+ },
329
+ {
330
+ "epoch": 0.16,
331
+ "learning_rate": 0.0006178148820682862,
332
+ "loss": 0.7691,
333
+ "step": 54
334
+ },
335
+ {
336
+ "epoch": 0.16,
337
+ "learning_rate": 0.0006164550394514865,
338
+ "loss": 0.8572,
339
+ "step": 55
340
+ },
341
+ {
342
+ "epoch": 0.17,
343
+ "learning_rate": 0.0006150686216544614,
344
+ "loss": 0.7915,
345
+ "step": 56
346
+ },
347
+ {
348
+ "epoch": 0.17,
349
+ "learning_rate": 0.0006136557550922589,
350
+ "loss": 0.759,
351
+ "step": 57
352
+ },
353
+ {
354
+ "epoch": 0.17,
355
+ "learning_rate": 0.0006122165685915537,
356
+ "loss": 0.7282,
357
+ "step": 58
358
+ },
359
+ {
360
+ "epoch": 0.18,
361
+ "learning_rate": 0.0006107511933789002,
362
+ "loss": 0.768,
363
+ "step": 59
364
+ },
365
+ {
366
+ "epoch": 0.18,
367
+ "learning_rate": 0.0006092597630687677,
368
+ "loss": 0.7868,
369
+ "step": 60
370
+ },
371
+ {
372
+ "epoch": 0.18,
373
+ "learning_rate": 0.0006077424136513567,
374
+ "loss": 0.7827,
375
+ "step": 61
376
+ },
377
+ {
378
+ "epoch": 0.18,
379
+ "learning_rate": 0.0006061992834801996,
380
+ "loss": 0.7755,
381
+ "step": 62
382
+ },
383
+ {
384
+ "epoch": 0.19,
385
+ "learning_rate": 0.0006046305132595453,
386
+ "loss": 0.8332,
387
+ "step": 63
388
+ },
389
+ {
390
+ "epoch": 0.19,
391
+ "learning_rate": 0.0006030362460315296,
392
+ "loss": 0.7241,
393
+ "step": 64
394
+ },
395
+ {
396
+ "epoch": 0.19,
397
+ "learning_rate": 0.0006014166271631326,
398
+ "loss": 0.8074,
399
+ "step": 65
400
+ },
401
+ {
402
+ "epoch": 0.2,
403
+ "learning_rate": 0.000599771804332924,
404
+ "loss": 0.7491,
405
+ "step": 66
406
+ },
407
+ {
408
+ "epoch": 0.2,
409
+ "learning_rate": 0.0005981019275175972,
410
+ "loss": 0.8004,
411
+ "step": 67
412
+ },
413
+ {
414
+ "epoch": 0.2,
415
+ "learning_rate": 0.000596407148978295,
416
+ "loss": 0.7442,
417
+ "step": 68
418
+ },
419
+ {
420
+ "epoch": 0.21,
421
+ "learning_rate": 0.0005946876232467254,
422
+ "loss": 0.6956,
423
+ "step": 69
424
+ },
425
+ {
426
+ "epoch": 0.21,
427
+ "learning_rate": 0.0005929435071110721,
428
+ "loss": 0.7472,
429
+ "step": 70
430
+ },
431
+ {
432
+ "epoch": 0.21,
433
+ "learning_rate": 0.0005911749596016978,
434
+ "loss": 0.8097,
435
+ "step": 71
436
+ },
437
+ {
438
+ "epoch": 0.21,
439
+ "learning_rate": 0.0005893821419766438,
440
+ "loss": 0.8002,
441
+ "step": 72
442
+ },
443
+ {
444
+ "epoch": 0.22,
445
+ "learning_rate": 0.0005875652177069265,
446
+ "loss": 0.8203,
447
+ "step": 73
448
+ },
449
+ {
450
+ "epoch": 0.22,
451
+ "learning_rate": 0.0005857243524616315,
452
+ "loss": 0.7339,
453
+ "step": 74
454
+ },
455
+ {
456
+ "epoch": 0.22,
457
+ "learning_rate": 0.0005838597140928082,
458
+ "loss": 0.6844,
459
+ "step": 75
460
+ },
461
+ {
462
+ "epoch": 0.23,
463
+ "learning_rate": 0.0005819714726201646,
464
+ "loss": 0.7211,
465
+ "step": 76
466
+ },
467
+ {
468
+ "epoch": 0.23,
469
+ "learning_rate": 0.0005800598002155648,
470
+ "loss": 0.8289,
471
+ "step": 77
472
+ },
473
+ {
474
+ "epoch": 0.23,
475
+ "learning_rate": 0.0005781248711873302,
476
+ "loss": 0.7686,
477
+ "step": 78
478
+ },
479
+ {
480
+ "epoch": 0.23,
481
+ "learning_rate": 0.0005761668619643458,
482
+ "loss": 0.7618,
483
+ "step": 79
484
+ },
485
+ {
486
+ "epoch": 0.24,
487
+ "learning_rate": 0.0005741859510799734,
488
+ "loss": 0.779,
489
+ "step": 80
490
+ },
491
+ {
492
+ "epoch": 0.24,
493
+ "learning_rate": 0.0005721823191557725,
494
+ "loss": 0.7599,
495
+ "step": 81
496
+ },
497
+ {
498
+ "epoch": 0.24,
499
+ "learning_rate": 0.0005701561488850312,
500
+ "loss": 0.7495,
501
+ "step": 82
502
+ },
503
+ {
504
+ "epoch": 0.25,
505
+ "learning_rate": 0.000568107625016108,
506
+ "loss": 0.6977,
507
+ "step": 83
508
+ },
509
+ {
510
+ "epoch": 0.25,
511
+ "learning_rate": 0.0005660369343355862,
512
+ "loss": 0.757,
513
+ "step": 84
514
+ },
515
+ {
516
+ "epoch": 0.25,
517
+ "learning_rate": 0.0005639442656512426,
518
+ "loss": 0.8295,
519
+ "step": 85
520
+ },
521
+ {
522
+ "epoch": 0.26,
523
+ "learning_rate": 0.0005618298097748316,
524
+ "loss": 0.7394,
525
+ "step": 86
526
+ },
527
+ {
528
+ "epoch": 0.26,
529
+ "learning_rate": 0.0005596937595046872,
530
+ "loss": 0.7728,
531
+ "step": 87
532
+ },
533
+ {
534
+ "epoch": 0.26,
535
+ "learning_rate": 0.0005575363096081429,
536
+ "loss": 0.7721,
537
+ "step": 88
538
+ },
539
+ {
540
+ "epoch": 0.26,
541
+ "learning_rate": 0.0005553576568037731,
542
+ "loss": 0.7191,
543
+ "step": 89
544
+ },
545
+ {
546
+ "epoch": 0.27,
547
+ "learning_rate": 0.0005531579997434555,
548
+ "loss": 0.7594,
549
+ "step": 90
550
+ },
551
+ {
552
+ "epoch": 0.27,
553
+ "learning_rate": 0.0005509375389942588,
554
+ "loss": 0.7511,
555
+ "step": 91
556
+ },
557
+ {
558
+ "epoch": 0.27,
559
+ "learning_rate": 0.0005486964770201533,
560
+ "loss": 0.7644,
561
+ "step": 92
562
+ },
563
+ {
564
+ "epoch": 0.28,
565
+ "learning_rate": 0.0005464350181635519,
566
+ "loss": 0.7403,
567
+ "step": 93
568
+ },
569
+ {
570
+ "epoch": 0.28,
571
+ "learning_rate": 0.000544153368626676,
572
+ "loss": 0.7204,
573
+ "step": 94
574
+ },
575
+ {
576
+ "epoch": 0.28,
577
+ "learning_rate": 0.0005418517364527552,
578
+ "loss": 0.7358,
579
+ "step": 95
580
+ },
581
+ {
582
+ "epoch": 0.29,
583
+ "learning_rate": 0.0005395303315070571,
584
+ "loss": 0.7607,
585
+ "step": 96
586
+ },
587
+ {
588
+ "epoch": 0.29,
589
+ "learning_rate": 0.0005371893654577517,
590
+ "loss": 0.7505,
591
+ "step": 97
592
+ },
593
+ {
594
+ "epoch": 0.29,
595
+ "learning_rate": 0.0005348290517566107,
596
+ "loss": 0.7274,
597
+ "step": 98
598
+ },
599
+ {
600
+ "epoch": 0.29,
601
+ "learning_rate": 0.0005324496056195461,
602
+ "loss": 0.8161,
603
+ "step": 99
604
+ },
605
+ {
606
+ "epoch": 0.3,
607
+ "learning_rate": 0.0005300512440069852,
608
+ "loss": 0.7043,
609
+ "step": 100
610
+ },
611
+ {
612
+ "epoch": 0.3,
613
+ "learning_rate": 0.0005276341856040884,
614
+ "loss": 0.7921,
615
+ "step": 101
616
+ },
617
+ {
618
+ "epoch": 0.3,
619
+ "learning_rate": 0.0005251986508008097,
620
+ "loss": 0.6852,
621
+ "step": 102
622
+ },
623
+ {
624
+ "epoch": 0.31,
625
+ "learning_rate": 0.0005227448616718004,
626
+ "loss": 0.6996,
627
+ "step": 103
628
+ },
629
+ {
630
+ "epoch": 0.31,
631
+ "learning_rate": 0.0005202730419561611,
632
+ "loss": 0.6323,
633
+ "step": 104
634
+ },
635
+ {
636
+ "epoch": 0.31,
637
+ "learning_rate": 0.0005177834170370404,
638
+ "loss": 0.7601,
639
+ "step": 105
640
+ },
641
+ {
642
+ "epoch": 0.32,
643
+ "learning_rate": 0.0005152762139210839,
644
+ "loss": 0.7499,
645
+ "step": 106
646
+ },
647
+ {
648
+ "epoch": 0.32,
649
+ "learning_rate": 0.0005127516612177365,
650
+ "loss": 0.73,
651
+ "step": 107
652
+ },
653
+ {
654
+ "epoch": 0.32,
655
+ "learning_rate": 0.0005102099891183958,
656
+ "loss": 0.7438,
657
+ "step": 108
658
+ },
659
+ {
660
+ "epoch": 0.32,
661
+ "learning_rate": 0.0005076514293754255,
662
+ "loss": 0.6614,
663
+ "step": 109
664
+ },
665
+ {
666
+ "epoch": 0.33,
667
+ "learning_rate": 0.0005050762152810218,
668
+ "loss": 0.7599,
669
+ "step": 110
670
+ },
671
+ {
672
+ "epoch": 0.33,
673
+ "learning_rate": 0.0005024845816459423,
674
+ "loss": 0.7471,
675
+ "step": 111
676
+ },
677
+ {
678
+ "epoch": 0.33,
679
+ "learning_rate": 0.0004998767647780961,
680
+ "loss": 0.7569,
681
+ "step": 112
682
+ },
683
+ {
684
+ "epoch": 0.34,
685
+ "learning_rate": 0.0004972530024609966,
686
+ "loss": 0.6561,
687
+ "step": 113
688
+ },
689
+ {
690
+ "epoch": 0.34,
691
+ "learning_rate": 0.0004946135339320798,
692
+ "loss": 0.7369,
693
+ "step": 114
694
+ },
695
+ {
696
+ "epoch": 0.34,
697
+ "learning_rate": 0.0004919585998608917,
698
+ "loss": 0.755,
699
+ "step": 115
700
+ },
701
+ {
702
+ "epoch": 0.35,
703
+ "learning_rate": 0.0004892884423271417,
704
+ "loss": 0.7307,
705
+ "step": 116
706
+ },
707
+ {
708
+ "epoch": 0.35,
709
+ "learning_rate": 0.0004866033047986317,
710
+ "loss": 0.7321,
711
+ "step": 117
712
+ },
713
+ {
714
+ "epoch": 0.35,
715
+ "learning_rate": 0.00048390343210905486,
716
+ "loss": 0.772,
717
+ "step": 118
718
+ },
719
+ {
720
+ "epoch": 0.35,
721
+ "learning_rate": 0.0004811890704356722,
722
+ "loss": 0.6707,
723
+ "step": 119
724
+ },
725
+ {
726
+ "epoch": 0.36,
727
+ "learning_rate": 0.0004784604672768657,
728
+ "loss": 0.695,
729
+ "step": 120
730
+ },
731
+ {
732
+ "epoch": 0.36,
733
+ "learning_rate": 0.0004757178714295709,
734
+ "loss": 0.6372,
735
+ "step": 121
736
+ },
737
+ {
738
+ "epoch": 0.36,
739
+ "learning_rate": 0.0004729615329665918,
740
+ "loss": 0.7303,
741
+ "step": 122
742
+ },
743
+ {
744
+ "epoch": 0.37,
745
+ "learning_rate": 0.0004701917032137987,
746
+ "loss": 0.7313,
747
+ "step": 123
748
+ },
749
+ {
750
+ "epoch": 0.37,
751
+ "learning_rate": 0.00046740863472721176,
752
+ "loss": 0.6939,
753
+ "step": 124
754
+ },
755
+ {
756
+ "epoch": 0.37,
757
+ "learning_rate": 0.0004646125812699734,
758
+ "loss": 0.711,
759
+ "step": 125
760
+ },
761
+ {
762
+ "epoch": 0.37,
763
+ "learning_rate": 0.0004618037977892089,
764
+ "loss": 0.7238,
765
+ "step": 126
766
+ },
767
+ {
768
+ "epoch": 0.38,
769
+ "learning_rate": 0.00045898254039278106,
770
+ "loss": 0.7508,
771
+ "step": 127
772
+ },
773
+ {
774
+ "epoch": 0.38,
775
+ "learning_rate": 0.0004561490663259375,
776
+ "loss": 0.7817,
777
+ "step": 128
778
+ },
779
+ {
780
+ "epoch": 0.38,
781
+ "learning_rate": 0.00045330363394785467,
782
+ "loss": 0.7149,
783
+ "step": 129
784
+ },
785
+ {
786
+ "epoch": 0.39,
787
+ "learning_rate": 0.0004504465027080806,
788
+ "loss": 0.7987,
789
+ "step": 130
790
+ },
791
+ {
792
+ "epoch": 0.39,
793
+ "learning_rate": 0.00044757793312287807,
794
+ "loss": 0.7047,
795
+ "step": 131
796
+ },
797
+ {
798
+ "epoch": 0.39,
799
+ "learning_rate": 0.00044469818675147024,
800
+ "loss": 0.7322,
801
+ "step": 132
802
+ },
803
+ {
804
+ "epoch": 0.4,
805
+ "learning_rate": 0.0004418075261721916,
806
+ "loss": 0.71,
807
+ "step": 133
808
+ },
809
+ {
810
+ "epoch": 0.4,
811
+ "learning_rate": 0.0004389062149585456,
812
+ "loss": 0.7306,
813
+ "step": 134
814
+ },
815
+ {
816
+ "epoch": 0.4,
817
+ "learning_rate": 0.0004359945176551721,
818
+ "loss": 0.6989,
819
+ "step": 135
820
+ },
821
+ {
822
+ "epoch": 0.4,
823
+ "learning_rate": 0.00043307269975372513,
824
+ "loss": 0.6898,
825
+ "step": 136
826
+ },
827
+ {
828
+ "epoch": 0.41,
829
+ "learning_rate": 0.0004301410276686663,
830
+ "loss": 0.7431,
831
+ "step": 137
832
+ },
833
+ {
834
+ "epoch": 0.41,
835
+ "learning_rate": 0.00042719976871297155,
836
+ "loss": 0.7236,
837
+ "step": 138
838
+ },
839
+ {
840
+ "epoch": 0.41,
841
+ "learning_rate": 0.0004242491910737582,
842
+ "loss": 0.7704,
843
+ "step": 139
844
+ },
845
+ {
846
+ "epoch": 0.42,
847
+ "learning_rate": 0.0004212895637878311,
848
+ "loss": 0.7125,
849
+ "step": 140
850
+ },
851
+ {
852
+ "epoch": 0.42,
853
+ "learning_rate": 0.00041832115671715107,
854
+ "loss": 0.7869,
855
+ "step": 141
856
+ },
857
+ {
858
+ "epoch": 0.42,
859
+ "learning_rate": 0.00041534424052422966,
860
+ "loss": 0.714,
861
+ "step": 142
862
+ },
863
+ {
864
+ "epoch": 0.43,
865
+ "learning_rate": 0.00041235908664744866,
866
+ "loss": 0.6927,
867
+ "step": 143
868
+ },
869
+ {
870
+ "epoch": 0.43,
871
+ "learning_rate": 0.00040936596727631104,
872
+ "loss": 0.7168,
873
+ "step": 144
874
+ },
875
+ {
876
+ "epoch": 0.43,
877
+ "learning_rate": 0.0004063651553266216,
878
+ "loss": 0.7199,
879
+ "step": 145
880
+ },
881
+ {
882
+ "epoch": 0.43,
883
+ "learning_rate": 0.00040335692441560304,
884
+ "loss": 0.7084,
885
+ "step": 146
886
+ },
887
+ {
888
+ "epoch": 0.44,
889
+ "learning_rate": 0.00040034154883694667,
890
+ "loss": 0.728,
891
+ "step": 147
892
+ },
893
+ {
894
+ "epoch": 0.44,
895
+ "learning_rate": 0.00039731930353580216,
896
+ "loss": 0.7368,
897
+ "step": 148
898
+ },
899
+ {
900
+ "epoch": 0.44,
901
+ "learning_rate": 0.0003942904640837078,
902
+ "loss": 0.7298,
903
+ "step": 149
904
+ },
905
+ {
906
+ "epoch": 0.45,
907
+ "learning_rate": 0.00039125530665346355,
908
+ "loss": 0.73,
909
+ "step": 150
910
+ }
911
+ ],
912
+ "logging_steps": 1,
913
+ "max_steps": 336,
914
+ "num_input_tokens_seen": 0,
915
+ "num_train_epochs": 1,
916
+ "save_steps": 50,
917
+ "total_flos": 1.9009765459545293e+17,
918
+ "train_batch_size": 6,
919
+ "trial_name": null,
920
+ "trial_params": null
921
+ }
checkpoint-150/training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:3a720d2daf4afe192195297ee83d1a8834602d7e8eda493eaba5ee762dd57a90
3
+ size 4664
checkpoint-200/README.md ADDED
@@ -0,0 +1,204 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ library_name: peft
3
+ base_model: unsloth/llama-2-7b
4
+ ---
5
+
6
+ # Model Card for Model ID
7
+
8
+ <!-- Provide a quick summary of what the model is/does. -->
9
+
10
+
11
+
12
+ ## Model Details
13
+
14
+ ### Model Description
15
+
16
+ <!-- Provide a longer summary of what this model is. -->
17
+
18
+
19
+
20
+ - **Developed by:** [More Information Needed]
21
+ - **Funded by [optional]:** [More Information Needed]
22
+ - **Shared by [optional]:** [More Information Needed]
23
+ - **Model type:** [More Information Needed]
24
+ - **Language(s) (NLP):** [More Information Needed]
25
+ - **License:** [More Information Needed]
26
+ - **Finetuned from model [optional]:** [More Information Needed]
27
+
28
+ ### Model Sources [optional]
29
+
30
+ <!-- Provide the basic links for the model. -->
31
+
32
+ - **Repository:** [More Information Needed]
33
+ - **Paper [optional]:** [More Information Needed]
34
+ - **Demo [optional]:** [More Information Needed]
35
+
36
+ ## Uses
37
+
38
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
39
+
40
+ ### Direct Use
41
+
42
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
43
+
44
+ [More Information Needed]
45
+
46
+ ### Downstream Use [optional]
47
+
48
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
49
+
50
+ [More Information Needed]
51
+
52
+ ### Out-of-Scope Use
53
+
54
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
55
+
56
+ [More Information Needed]
57
+
58
+ ## Bias, Risks, and Limitations
59
+
60
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
61
+
62
+ [More Information Needed]
63
+
64
+ ### Recommendations
65
+
66
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
67
+
68
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
69
+
70
+ ## How to Get Started with the Model
71
+
72
+ Use the code below to get started with the model.
73
+
74
+ [More Information Needed]
75
+
76
+ ## Training Details
77
+
78
+ ### Training Data
79
+
80
+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
81
+
82
+ [More Information Needed]
83
+
84
+ ### Training Procedure
85
+
86
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
87
+
88
+ #### Preprocessing [optional]
89
+
90
+ [More Information Needed]
91
+
92
+
93
+ #### Training Hyperparameters
94
+
95
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
96
+
97
+ #### Speeds, Sizes, Times [optional]
98
+
99
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
100
+
101
+ [More Information Needed]
102
+
103
+ ## Evaluation
104
+
105
+ <!-- This section describes the evaluation protocols and provides the results. -->
106
+
107
+ ### Testing Data, Factors & Metrics
108
+
109
+ #### Testing Data
110
+
111
+ <!-- This should link to a Dataset Card if possible. -->
112
+
113
+ [More Information Needed]
114
+
115
+ #### Factors
116
+
117
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
118
+
119
+ [More Information Needed]
120
+
121
+ #### Metrics
122
+
123
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
124
+
125
+ [More Information Needed]
126
+
127
+ ### Results
128
+
129
+ [More Information Needed]
130
+
131
+ #### Summary
132
+
133
+
134
+
135
+ ## Model Examination [optional]
136
+
137
+ <!-- Relevant interpretability work for the model goes here -->
138
+
139
+ [More Information Needed]
140
+
141
+ ## Environmental Impact
142
+
143
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
144
+
145
+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
146
+
147
+ - **Hardware Type:** [More Information Needed]
148
+ - **Hours used:** [More Information Needed]
149
+ - **Cloud Provider:** [More Information Needed]
150
+ - **Compute Region:** [More Information Needed]
151
+ - **Carbon Emitted:** [More Information Needed]
152
+
153
+ ## Technical Specifications [optional]
154
+
155
+ ### Model Architecture and Objective
156
+
157
+ [More Information Needed]
158
+
159
+ ### Compute Infrastructure
160
+
161
+ [More Information Needed]
162
+
163
+ #### Hardware
164
+
165
+ [More Information Needed]
166
+
167
+ #### Software
168
+
169
+ [More Information Needed]
170
+
171
+ ## Citation [optional]
172
+
173
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
174
+
175
+ **BibTeX:**
176
+
177
+ [More Information Needed]
178
+
179
+ **APA:**
180
+
181
+ [More Information Needed]
182
+
183
+ ## Glossary [optional]
184
+
185
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
186
+
187
+ [More Information Needed]
188
+
189
+ ## More Information [optional]
190
+
191
+ [More Information Needed]
192
+
193
+ ## Model Card Authors [optional]
194
+
195
+ [More Information Needed]
196
+
197
+ ## Model Card Contact
198
+
199
+ [More Information Needed]
200
+
201
+
202
+ ### Framework versions
203
+
204
+ - PEFT 0.7.1
checkpoint-200/adapter_config.json ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alpha_pattern": {},
3
+ "auto_mapping": null,
4
+ "base_model_name_or_path": "unsloth/llama-2-7b",
5
+ "bias": "none",
6
+ "fan_in_fan_out": false,
7
+ "inference_mode": true,
8
+ "init_lora_weights": true,
9
+ "layers_pattern": null,
10
+ "layers_to_transform": null,
11
+ "loftq_config": {},
12
+ "lora_alpha": 16,
13
+ "lora_dropout": 0,
14
+ "megatron_config": null,
15
+ "megatron_core": "megatron.core",
16
+ "modules_to_save": null,
17
+ "peft_type": "LORA",
18
+ "r": 128,
19
+ "rank_pattern": {},
20
+ "revision": "unsloth",
21
+ "target_modules": [
22
+ "up_proj",
23
+ "k_proj",
24
+ "v_proj",
25
+ "o_proj",
26
+ "q_proj",
27
+ "down_proj",
28
+ "gate_proj"
29
+ ],
30
+ "task_type": "CAUSAL_LM"
31
+ }
checkpoint-200/adapter_model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:0c40072580cdb8847f7a68a14591a12c2d91034b32c5d97da71747393a75f991
3
+ size 1279323952
checkpoint-200/optimizer.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:cc5288e60674d03b908711f5a166d44c8c8f9f28367078f7f5ff64dbd2c8d70a
3
+ size 641407572
checkpoint-200/rng_state.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:fe2e145b09b2faab2c45440a9233e2700f8dca5428319c1eb306332f174e4af7
3
+ size 14244
checkpoint-200/scheduler.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:118b4261b2696d1526156bb53cdfd49d3c03c38b24d5da5974eb026da0f48d98
3
+ size 1064
checkpoint-200/trainer_state.json ADDED
@@ -0,0 +1,1221 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "best_metric": null,
3
+ "best_model_checkpoint": null,
4
+ "epoch": 0.594883997620464,
5
+ "eval_steps": 500,
6
+ "global_step": 200,
7
+ "is_hyper_param_search": false,
8
+ "is_local_process_zero": true,
9
+ "is_world_process_zero": true,
10
+ "log_history": [
11
+ {
12
+ "epoch": 0.0,
13
+ "learning_rate": 9.285714285714285e-05,
14
+ "loss": 2.4427,
15
+ "step": 1
16
+ },
17
+ {
18
+ "epoch": 0.01,
19
+ "learning_rate": 0.0001857142857142857,
20
+ "loss": 2.3973,
21
+ "step": 2
22
+ },
23
+ {
24
+ "epoch": 0.01,
25
+ "learning_rate": 0.00027857142857142854,
26
+ "loss": 2.341,
27
+ "step": 3
28
+ },
29
+ {
30
+ "epoch": 0.01,
31
+ "learning_rate": 0.0003714285714285714,
32
+ "loss": 2.1281,
33
+ "step": 4
34
+ },
35
+ {
36
+ "epoch": 0.01,
37
+ "learning_rate": 0.0004642857142857143,
38
+ "loss": 1.4346,
39
+ "step": 5
40
+ },
41
+ {
42
+ "epoch": 0.02,
43
+ "learning_rate": 0.0005571428571428571,
44
+ "loss": 1.1715,
45
+ "step": 6
46
+ },
47
+ {
48
+ "epoch": 0.02,
49
+ "learning_rate": 0.00065,
50
+ "loss": 1.086,
51
+ "step": 7
52
+ },
53
+ {
54
+ "epoch": 0.02,
55
+ "learning_rate": 0.0006499851830773117,
56
+ "loss": 0.9921,
57
+ "step": 8
58
+ },
59
+ {
60
+ "epoch": 0.03,
61
+ "learning_rate": 0.00064994073366027,
62
+ "loss": 0.9231,
63
+ "step": 9
64
+ },
65
+ {
66
+ "epoch": 0.03,
67
+ "learning_rate": 0.0006498666558018197,
68
+ "loss": 0.9343,
69
+ "step": 10
70
+ },
71
+ {
72
+ "epoch": 0.03,
73
+ "learning_rate": 0.0006497629562564588,
74
+ "loss": 0.9191,
75
+ "step": 11
76
+ },
77
+ {
78
+ "epoch": 0.04,
79
+ "learning_rate": 0.0006496296444796219,
80
+ "loss": 0.8791,
81
+ "step": 12
82
+ },
83
+ {
84
+ "epoch": 0.04,
85
+ "learning_rate": 0.0006494667326268186,
86
+ "loss": 0.8632,
87
+ "step": 13
88
+ },
89
+ {
90
+ "epoch": 0.04,
91
+ "learning_rate": 0.0006492742355525248,
92
+ "loss": 0.9267,
93
+ "step": 14
94
+ },
95
+ {
96
+ "epoch": 0.04,
97
+ "learning_rate": 0.0006490521708088281,
98
+ "loss": 0.8644,
99
+ "step": 15
100
+ },
101
+ {
102
+ "epoch": 0.05,
103
+ "learning_rate": 0.000648800558643828,
104
+ "loss": 0.8353,
105
+ "step": 16
106
+ },
107
+ {
108
+ "epoch": 0.05,
109
+ "learning_rate": 0.0006485194219997891,
110
+ "loss": 0.8482,
111
+ "step": 17
112
+ },
113
+ {
114
+ "epoch": 0.05,
115
+ "learning_rate": 0.0006482087865110493,
116
+ "loss": 0.8587,
117
+ "step": 18
118
+ },
119
+ {
120
+ "epoch": 0.06,
121
+ "learning_rate": 0.0006478686805016826,
122
+ "loss": 0.9134,
123
+ "step": 19
124
+ },
125
+ {
126
+ "epoch": 0.06,
127
+ "learning_rate": 0.0006474991349829163,
128
+ "loss": 0.8238,
129
+ "step": 20
130
+ },
131
+ {
132
+ "epoch": 0.06,
133
+ "learning_rate": 0.0006471001836503035,
134
+ "loss": 0.8329,
135
+ "step": 21
136
+ },
137
+ {
138
+ "epoch": 0.07,
139
+ "learning_rate": 0.0006466718628806508,
140
+ "loss": 0.7995,
141
+ "step": 22
142
+ },
143
+ {
144
+ "epoch": 0.07,
145
+ "learning_rate": 0.0006462142117287011,
146
+ "loss": 0.8363,
147
+ "step": 23
148
+ },
149
+ {
150
+ "epoch": 0.07,
151
+ "learning_rate": 0.0006457272719235728,
152
+ "loss": 0.7942,
153
+ "step": 24
154
+ },
155
+ {
156
+ "epoch": 0.07,
157
+ "learning_rate": 0.0006452110878649547,
158
+ "loss": 0.858,
159
+ "step": 25
160
+ },
161
+ {
162
+ "epoch": 0.08,
163
+ "learning_rate": 0.0006446657066190579,
164
+ "loss": 0.8474,
165
+ "step": 26
166
+ },
167
+ {
168
+ "epoch": 0.08,
169
+ "learning_rate": 0.000644091177914324,
170
+ "loss": 0.8175,
171
+ "step": 27
172
+ },
173
+ {
174
+ "epoch": 0.08,
175
+ "learning_rate": 0.0006434875541368907,
176
+ "loss": 0.7821,
177
+ "step": 28
178
+ },
179
+ {
180
+ "epoch": 0.09,
181
+ "learning_rate": 0.0006428548903258156,
182
+ "loss": 0.8583,
183
+ "step": 29
184
+ },
185
+ {
186
+ "epoch": 0.09,
187
+ "learning_rate": 0.0006421932441680574,
188
+ "loss": 0.8071,
189
+ "step": 30
190
+ },
191
+ {
192
+ "epoch": 0.09,
193
+ "learning_rate": 0.0006415026759932158,
194
+ "loss": 0.805,
195
+ "step": 31
196
+ },
197
+ {
198
+ "epoch": 0.1,
199
+ "learning_rate": 0.0006407832487680309,
200
+ "loss": 0.881,
201
+ "step": 32
202
+ },
203
+ {
204
+ "epoch": 0.1,
205
+ "learning_rate": 0.0006400350280906415,
206
+ "loss": 0.8302,
207
+ "step": 33
208
+ },
209
+ {
210
+ "epoch": 0.1,
211
+ "learning_rate": 0.0006392580821846041,
212
+ "loss": 0.8456,
213
+ "step": 34
214
+ },
215
+ {
216
+ "epoch": 0.1,
217
+ "learning_rate": 0.0006384524818926723,
218
+ "loss": 0.8067,
219
+ "step": 35
220
+ },
221
+ {
222
+ "epoch": 0.11,
223
+ "learning_rate": 0.0006376183006703367,
224
+ "loss": 0.8307,
225
+ "step": 36
226
+ },
227
+ {
228
+ "epoch": 0.11,
229
+ "learning_rate": 0.0006367556145791275,
230
+ "loss": 0.8347,
231
+ "step": 37
232
+ },
233
+ {
234
+ "epoch": 0.11,
235
+ "learning_rate": 0.0006358645022796795,
236
+ "loss": 0.8086,
237
+ "step": 38
238
+ },
239
+ {
240
+ "epoch": 0.12,
241
+ "learning_rate": 0.0006349450450245589,
242
+ "loss": 0.8726,
243
+ "step": 39
244
+ },
245
+ {
246
+ "epoch": 0.12,
247
+ "learning_rate": 0.0006339973266508556,
248
+ "loss": 0.78,
249
+ "step": 40
250
+ },
251
+ {
252
+ "epoch": 0.12,
253
+ "learning_rate": 0.0006330214335725379,
254
+ "loss": 0.7902,
255
+ "step": 41
256
+ },
257
+ {
258
+ "epoch": 0.12,
259
+ "learning_rate": 0.0006320174547725736,
260
+ "loss": 0.7823,
261
+ "step": 42
262
+ },
263
+ {
264
+ "epoch": 0.13,
265
+ "learning_rate": 0.0006309854817948169,
266
+ "loss": 0.8211,
267
+ "step": 43
268
+ },
269
+ {
270
+ "epoch": 0.13,
271
+ "learning_rate": 0.0006299256087356603,
272
+ "loss": 0.8656,
273
+ "step": 44
274
+ },
275
+ {
276
+ "epoch": 0.13,
277
+ "learning_rate": 0.000628837932235456,
278
+ "loss": 0.7767,
279
+ "step": 45
280
+ },
281
+ {
282
+ "epoch": 0.14,
283
+ "learning_rate": 0.0006277225514697028,
284
+ "loss": 0.7542,
285
+ "step": 46
286
+ },
287
+ {
288
+ "epoch": 0.14,
289
+ "learning_rate": 0.0006265795681400046,
290
+ "loss": 0.8254,
291
+ "step": 47
292
+ },
293
+ {
294
+ "epoch": 0.14,
295
+ "learning_rate": 0.0006254090864647957,
296
+ "loss": 0.8099,
297
+ "step": 48
298
+ },
299
+ {
300
+ "epoch": 0.15,
301
+ "learning_rate": 0.0006242112131698394,
302
+ "loss": 0.7786,
303
+ "step": 49
304
+ },
305
+ {
306
+ "epoch": 0.15,
307
+ "learning_rate": 0.0006229860574784954,
308
+ "loss": 0.7895,
309
+ "step": 50
310
+ },
311
+ {
312
+ "epoch": 0.15,
313
+ "learning_rate": 0.0006217337311017619,
314
+ "loss": 0.8092,
315
+ "step": 51
316
+ },
317
+ {
318
+ "epoch": 0.15,
319
+ "learning_rate": 0.0006204543482280886,
320
+ "loss": 0.7998,
321
+ "step": 52
322
+ },
323
+ {
324
+ "epoch": 0.16,
325
+ "learning_rate": 0.0006191480255129656,
326
+ "loss": 0.802,
327
+ "step": 53
328
+ },
329
+ {
330
+ "epoch": 0.16,
331
+ "learning_rate": 0.0006178148820682862,
332
+ "loss": 0.7691,
333
+ "step": 54
334
+ },
335
+ {
336
+ "epoch": 0.16,
337
+ "learning_rate": 0.0006164550394514865,
338
+ "loss": 0.8572,
339
+ "step": 55
340
+ },
341
+ {
342
+ "epoch": 0.17,
343
+ "learning_rate": 0.0006150686216544614,
344
+ "loss": 0.7915,
345
+ "step": 56
346
+ },
347
+ {
348
+ "epoch": 0.17,
349
+ "learning_rate": 0.0006136557550922589,
350
+ "loss": 0.759,
351
+ "step": 57
352
+ },
353
+ {
354
+ "epoch": 0.17,
355
+ "learning_rate": 0.0006122165685915537,
356
+ "loss": 0.7282,
357
+ "step": 58
358
+ },
359
+ {
360
+ "epoch": 0.18,
361
+ "learning_rate": 0.0006107511933789002,
362
+ "loss": 0.768,
363
+ "step": 59
364
+ },
365
+ {
366
+ "epoch": 0.18,
367
+ "learning_rate": 0.0006092597630687677,
368
+ "loss": 0.7868,
369
+ "step": 60
370
+ },
371
+ {
372
+ "epoch": 0.18,
373
+ "learning_rate": 0.0006077424136513567,
374
+ "loss": 0.7827,
375
+ "step": 61
376
+ },
377
+ {
378
+ "epoch": 0.18,
379
+ "learning_rate": 0.0006061992834801996,
380
+ "loss": 0.7755,
381
+ "step": 62
382
+ },
383
+ {
384
+ "epoch": 0.19,
385
+ "learning_rate": 0.0006046305132595453,
386
+ "loss": 0.8332,
387
+ "step": 63
388
+ },
389
+ {
390
+ "epoch": 0.19,
391
+ "learning_rate": 0.0006030362460315296,
392
+ "loss": 0.7241,
393
+ "step": 64
394
+ },
395
+ {
396
+ "epoch": 0.19,
397
+ "learning_rate": 0.0006014166271631326,
398
+ "loss": 0.8074,
399
+ "step": 65
400
+ },
401
+ {
402
+ "epoch": 0.2,
403
+ "learning_rate": 0.000599771804332924,
404
+ "loss": 0.7491,
405
+ "step": 66
406
+ },
407
+ {
408
+ "epoch": 0.2,
409
+ "learning_rate": 0.0005981019275175972,
410
+ "loss": 0.8004,
411
+ "step": 67
412
+ },
413
+ {
414
+ "epoch": 0.2,
415
+ "learning_rate": 0.000596407148978295,
416
+ "loss": 0.7442,
417
+ "step": 68
418
+ },
419
+ {
420
+ "epoch": 0.21,
421
+ "learning_rate": 0.0005946876232467254,
422
+ "loss": 0.6956,
423
+ "step": 69
424
+ },
425
+ {
426
+ "epoch": 0.21,
427
+ "learning_rate": 0.0005929435071110721,
428
+ "loss": 0.7472,
429
+ "step": 70
430
+ },
431
+ {
432
+ "epoch": 0.21,
433
+ "learning_rate": 0.0005911749596016978,
434
+ "loss": 0.8097,
435
+ "step": 71
436
+ },
437
+ {
438
+ "epoch": 0.21,
439
+ "learning_rate": 0.0005893821419766438,
440
+ "loss": 0.8002,
441
+ "step": 72
442
+ },
443
+ {
444
+ "epoch": 0.22,
445
+ "learning_rate": 0.0005875652177069265,
446
+ "loss": 0.8203,
447
+ "step": 73
448
+ },
449
+ {
450
+ "epoch": 0.22,
451
+ "learning_rate": 0.0005857243524616315,
452
+ "loss": 0.7339,
453
+ "step": 74
454
+ },
455
+ {
456
+ "epoch": 0.22,
457
+ "learning_rate": 0.0005838597140928082,
458
+ "loss": 0.6844,
459
+ "step": 75
460
+ },
461
+ {
462
+ "epoch": 0.23,
463
+ "learning_rate": 0.0005819714726201646,
464
+ "loss": 0.7211,
465
+ "step": 76
466
+ },
467
+ {
468
+ "epoch": 0.23,
469
+ "learning_rate": 0.0005800598002155648,
470
+ "loss": 0.8289,
471
+ "step": 77
472
+ },
473
+ {
474
+ "epoch": 0.23,
475
+ "learning_rate": 0.0005781248711873302,
476
+ "loss": 0.7686,
477
+ "step": 78
478
+ },
479
+ {
480
+ "epoch": 0.23,
481
+ "learning_rate": 0.0005761668619643458,
482
+ "loss": 0.7618,
483
+ "step": 79
484
+ },
485
+ {
486
+ "epoch": 0.24,
487
+ "learning_rate": 0.0005741859510799734,
488
+ "loss": 0.779,
489
+ "step": 80
490
+ },
491
+ {
492
+ "epoch": 0.24,
493
+ "learning_rate": 0.0005721823191557725,
494
+ "loss": 0.7599,
495
+ "step": 81
496
+ },
497
+ {
498
+ "epoch": 0.24,
499
+ "learning_rate": 0.0005701561488850312,
500
+ "loss": 0.7495,
501
+ "step": 82
502
+ },
503
+ {
504
+ "epoch": 0.25,
505
+ "learning_rate": 0.000568107625016108,
506
+ "loss": 0.6977,
507
+ "step": 83
508
+ },
509
+ {
510
+ "epoch": 0.25,
511
+ "learning_rate": 0.0005660369343355862,
512
+ "loss": 0.757,
513
+ "step": 84
514
+ },
515
+ {
516
+ "epoch": 0.25,
517
+ "learning_rate": 0.0005639442656512426,
518
+ "loss": 0.8295,
519
+ "step": 85
520
+ },
521
+ {
522
+ "epoch": 0.26,
523
+ "learning_rate": 0.0005618298097748316,
524
+ "loss": 0.7394,
525
+ "step": 86
526
+ },
527
+ {
528
+ "epoch": 0.26,
529
+ "learning_rate": 0.0005596937595046872,
530
+ "loss": 0.7728,
531
+ "step": 87
532
+ },
533
+ {
534
+ "epoch": 0.26,
535
+ "learning_rate": 0.0005575363096081429,
536
+ "loss": 0.7721,
537
+ "step": 88
538
+ },
539
+ {
540
+ "epoch": 0.26,
541
+ "learning_rate": 0.0005553576568037731,
542
+ "loss": 0.7191,
543
+ "step": 89
544
+ },
545
+ {
546
+ "epoch": 0.27,
547
+ "learning_rate": 0.0005531579997434555,
548
+ "loss": 0.7594,
549
+ "step": 90
550
+ },
551
+ {
552
+ "epoch": 0.27,
553
+ "learning_rate": 0.0005509375389942588,
554
+ "loss": 0.7511,
555
+ "step": 91
556
+ },
557
+ {
558
+ "epoch": 0.27,
559
+ "learning_rate": 0.0005486964770201533,
560
+ "loss": 0.7644,
561
+ "step": 92
562
+ },
563
+ {
564
+ "epoch": 0.28,
565
+ "learning_rate": 0.0005464350181635519,
566
+ "loss": 0.7403,
567
+ "step": 93
568
+ },
569
+ {
570
+ "epoch": 0.28,
571
+ "learning_rate": 0.000544153368626676,
572
+ "loss": 0.7204,
573
+ "step": 94
574
+ },
575
+ {
576
+ "epoch": 0.28,
577
+ "learning_rate": 0.0005418517364527552,
578
+ "loss": 0.7358,
579
+ "step": 95
580
+ },
581
+ {
582
+ "epoch": 0.29,
583
+ "learning_rate": 0.0005395303315070571,
584
+ "loss": 0.7607,
585
+ "step": 96
586
+ },
587
+ {
588
+ "epoch": 0.29,
589
+ "learning_rate": 0.0005371893654577517,
590
+ "loss": 0.7505,
591
+ "step": 97
592
+ },
593
+ {
594
+ "epoch": 0.29,
595
+ "learning_rate": 0.0005348290517566107,
596
+ "loss": 0.7274,
597
+ "step": 98
598
+ },
599
+ {
600
+ "epoch": 0.29,
601
+ "learning_rate": 0.0005324496056195461,
602
+ "loss": 0.8161,
603
+ "step": 99
604
+ },
605
+ {
606
+ "epoch": 0.3,
607
+ "learning_rate": 0.0005300512440069852,
608
+ "loss": 0.7043,
609
+ "step": 100
610
+ },
611
+ {
612
+ "epoch": 0.3,
613
+ "learning_rate": 0.0005276341856040884,
614
+ "loss": 0.7921,
615
+ "step": 101
616
+ },
617
+ {
618
+ "epoch": 0.3,
619
+ "learning_rate": 0.0005251986508008097,
620
+ "loss": 0.6852,
621
+ "step": 102
622
+ },
623
+ {
624
+ "epoch": 0.31,
625
+ "learning_rate": 0.0005227448616718004,
626
+ "loss": 0.6996,
627
+ "step": 103
628
+ },
629
+ {
630
+ "epoch": 0.31,
631
+ "learning_rate": 0.0005202730419561611,
632
+ "loss": 0.6323,
633
+ "step": 104
634
+ },
635
+ {
636
+ "epoch": 0.31,
637
+ "learning_rate": 0.0005177834170370404,
638
+ "loss": 0.7601,
639
+ "step": 105
640
+ },
641
+ {
642
+ "epoch": 0.32,
643
+ "learning_rate": 0.0005152762139210839,
644
+ "loss": 0.7499,
645
+ "step": 106
646
+ },
647
+ {
648
+ "epoch": 0.32,
649
+ "learning_rate": 0.0005127516612177365,
650
+ "loss": 0.73,
651
+ "step": 107
652
+ },
653
+ {
654
+ "epoch": 0.32,
655
+ "learning_rate": 0.0005102099891183958,
656
+ "loss": 0.7438,
657
+ "step": 108
658
+ },
659
+ {
660
+ "epoch": 0.32,
661
+ "learning_rate": 0.0005076514293754255,
662
+ "loss": 0.6614,
663
+ "step": 109
664
+ },
665
+ {
666
+ "epoch": 0.33,
667
+ "learning_rate": 0.0005050762152810218,
668
+ "loss": 0.7599,
669
+ "step": 110
670
+ },
671
+ {
672
+ "epoch": 0.33,
673
+ "learning_rate": 0.0005024845816459423,
674
+ "loss": 0.7471,
675
+ "step": 111
676
+ },
677
+ {
678
+ "epoch": 0.33,
679
+ "learning_rate": 0.0004998767647780961,
680
+ "loss": 0.7569,
681
+ "step": 112
682
+ },
683
+ {
684
+ "epoch": 0.34,
685
+ "learning_rate": 0.0004972530024609966,
686
+ "loss": 0.6561,
687
+ "step": 113
688
+ },
689
+ {
690
+ "epoch": 0.34,
691
+ "learning_rate": 0.0004946135339320798,
692
+ "loss": 0.7369,
693
+ "step": 114
694
+ },
695
+ {
696
+ "epoch": 0.34,
697
+ "learning_rate": 0.0004919585998608917,
698
+ "loss": 0.755,
699
+ "step": 115
700
+ },
701
+ {
702
+ "epoch": 0.35,
703
+ "learning_rate": 0.0004892884423271417,
704
+ "loss": 0.7307,
705
+ "step": 116
706
+ },
707
+ {
708
+ "epoch": 0.35,
709
+ "learning_rate": 0.0004866033047986317,
710
+ "loss": 0.7321,
711
+ "step": 117
712
+ },
713
+ {
714
+ "epoch": 0.35,
715
+ "learning_rate": 0.00048390343210905486,
716
+ "loss": 0.772,
717
+ "step": 118
718
+ },
719
+ {
720
+ "epoch": 0.35,
721
+ "learning_rate": 0.0004811890704356722,
722
+ "loss": 0.6707,
723
+ "step": 119
724
+ },
725
+ {
726
+ "epoch": 0.36,
727
+ "learning_rate": 0.0004784604672768657,
728
+ "loss": 0.695,
729
+ "step": 120
730
+ },
731
+ {
732
+ "epoch": 0.36,
733
+ "learning_rate": 0.0004757178714295709,
734
+ "loss": 0.6372,
735
+ "step": 121
736
+ },
737
+ {
738
+ "epoch": 0.36,
739
+ "learning_rate": 0.0004729615329665918,
740
+ "loss": 0.7303,
741
+ "step": 122
742
+ },
743
+ {
744
+ "epoch": 0.37,
745
+ "learning_rate": 0.0004701917032137987,
746
+ "loss": 0.7313,
747
+ "step": 123
748
+ },
749
+ {
750
+ "epoch": 0.37,
751
+ "learning_rate": 0.00046740863472721176,
752
+ "loss": 0.6939,
753
+ "step": 124
754
+ },
755
+ {
756
+ "epoch": 0.37,
757
+ "learning_rate": 0.0004646125812699734,
758
+ "loss": 0.711,
759
+ "step": 125
760
+ },
761
+ {
762
+ "epoch": 0.37,
763
+ "learning_rate": 0.0004618037977892089,
764
+ "loss": 0.7238,
765
+ "step": 126
766
+ },
767
+ {
768
+ "epoch": 0.38,
769
+ "learning_rate": 0.00045898254039278106,
770
+ "loss": 0.7508,
771
+ "step": 127
772
+ },
773
+ {
774
+ "epoch": 0.38,
775
+ "learning_rate": 0.0004561490663259375,
776
+ "loss": 0.7817,
777
+ "step": 128
778
+ },
779
+ {
780
+ "epoch": 0.38,
781
+ "learning_rate": 0.00045330363394785467,
782
+ "loss": 0.7149,
783
+ "step": 129
784
+ },
785
+ {
786
+ "epoch": 0.39,
787
+ "learning_rate": 0.0004504465027080806,
788
+ "loss": 0.7987,
789
+ "step": 130
790
+ },
791
+ {
792
+ "epoch": 0.39,
793
+ "learning_rate": 0.00044757793312287807,
794
+ "loss": 0.7047,
795
+ "step": 131
796
+ },
797
+ {
798
+ "epoch": 0.39,
799
+ "learning_rate": 0.00044469818675147024,
800
+ "loss": 0.7322,
801
+ "step": 132
802
+ },
803
+ {
804
+ "epoch": 0.4,
805
+ "learning_rate": 0.0004418075261721916,
806
+ "loss": 0.71,
807
+ "step": 133
808
+ },
809
+ {
810
+ "epoch": 0.4,
811
+ "learning_rate": 0.0004389062149585456,
812
+ "loss": 0.7306,
813
+ "step": 134
814
+ },
815
+ {
816
+ "epoch": 0.4,
817
+ "learning_rate": 0.0004359945176551721,
818
+ "loss": 0.6989,
819
+ "step": 135
820
+ },
821
+ {
822
+ "epoch": 0.4,
823
+ "learning_rate": 0.00043307269975372513,
824
+ "loss": 0.6898,
825
+ "step": 136
826
+ },
827
+ {
828
+ "epoch": 0.41,
829
+ "learning_rate": 0.0004301410276686663,
830
+ "loss": 0.7431,
831
+ "step": 137
832
+ },
833
+ {
834
+ "epoch": 0.41,
835
+ "learning_rate": 0.00042719976871297155,
836
+ "loss": 0.7236,
837
+ "step": 138
838
+ },
839
+ {
840
+ "epoch": 0.41,
841
+ "learning_rate": 0.0004242491910737582,
842
+ "loss": 0.7704,
843
+ "step": 139
844
+ },
845
+ {
846
+ "epoch": 0.42,
847
+ "learning_rate": 0.0004212895637878311,
848
+ "loss": 0.7125,
849
+ "step": 140
850
+ },
851
+ {
852
+ "epoch": 0.42,
853
+ "learning_rate": 0.00041832115671715107,
854
+ "loss": 0.7869,
855
+ "step": 141
856
+ },
857
+ {
858
+ "epoch": 0.42,
859
+ "learning_rate": 0.00041534424052422966,
860
+ "loss": 0.714,
861
+ "step": 142
862
+ },
863
+ {
864
+ "epoch": 0.43,
865
+ "learning_rate": 0.00041235908664744866,
866
+ "loss": 0.6927,
867
+ "step": 143
868
+ },
869
+ {
870
+ "epoch": 0.43,
871
+ "learning_rate": 0.00040936596727631104,
872
+ "loss": 0.7168,
873
+ "step": 144
874
+ },
875
+ {
876
+ "epoch": 0.43,
877
+ "learning_rate": 0.0004063651553266216,
878
+ "loss": 0.7199,
879
+ "step": 145
880
+ },
881
+ {
882
+ "epoch": 0.43,
883
+ "learning_rate": 0.00040335692441560304,
884
+ "loss": 0.7084,
885
+ "step": 146
886
+ },
887
+ {
888
+ "epoch": 0.44,
889
+ "learning_rate": 0.00040034154883694667,
890
+ "loss": 0.728,
891
+ "step": 147
892
+ },
893
+ {
894
+ "epoch": 0.44,
895
+ "learning_rate": 0.00039731930353580216,
896
+ "loss": 0.7368,
897
+ "step": 148
898
+ },
899
+ {
900
+ "epoch": 0.44,
901
+ "learning_rate": 0.0003942904640837078,
902
+ "loss": 0.7298,
903
+ "step": 149
904
+ },
905
+ {
906
+ "epoch": 0.45,
907
+ "learning_rate": 0.00039125530665346355,
908
+ "loss": 0.73,
909
+ "step": 150
910
+ },
911
+ {
912
+ "epoch": 0.45,
913
+ "learning_rate": 0.00038821410799394935,
914
+ "loss": 0.6635,
915
+ "step": 151
916
+ },
917
+ {
918
+ "epoch": 0.45,
919
+ "learning_rate": 0.0003851671454048909,
920
+ "loss": 0.7228,
921
+ "step": 152
922
+ },
923
+ {
924
+ "epoch": 0.46,
925
+ "learning_rate": 0.00038211469671157496,
926
+ "loss": 0.7276,
927
+ "step": 153
928
+ },
929
+ {
930
+ "epoch": 0.46,
931
+ "learning_rate": 0.00037905704023951726,
932
+ "loss": 0.7386,
933
+ "step": 154
934
+ },
935
+ {
936
+ "epoch": 0.46,
937
+ "learning_rate": 0.0003759944547890843,
938
+ "loss": 0.7577,
939
+ "step": 155
940
+ },
941
+ {
942
+ "epoch": 0.46,
943
+ "learning_rate": 0.0003729272196100721,
944
+ "loss": 0.6462,
945
+ "step": 156
946
+ },
947
+ {
948
+ "epoch": 0.47,
949
+ "learning_rate": 0.0003698556143762437,
950
+ "loss": 0.7328,
951
+ "step": 157
952
+ },
953
+ {
954
+ "epoch": 0.47,
955
+ "learning_rate": 0.0003667799191598287,
956
+ "loss": 0.7096,
957
+ "step": 158
958
+ },
959
+ {
960
+ "epoch": 0.47,
961
+ "learning_rate": 0.00036370041440598517,
962
+ "loss": 0.6856,
963
+ "step": 159
964
+ },
965
+ {
966
+ "epoch": 0.48,
967
+ "learning_rate": 0.0003606173809072294,
968
+ "loss": 0.7114,
969
+ "step": 160
970
+ },
971
+ {
972
+ "epoch": 0.48,
973
+ "learning_rate": 0.000357531099777832,
974
+ "loss": 0.694,
975
+ "step": 161
976
+ },
977
+ {
978
+ "epoch": 0.48,
979
+ "learning_rate": 0.00035444185242818624,
980
+ "loss": 0.7436,
981
+ "step": 162
982
+ },
983
+ {
984
+ "epoch": 0.48,
985
+ "learning_rate": 0.0003513499205391482,
986
+ "loss": 0.727,
987
+ "step": 163
988
+ },
989
+ {
990
+ "epoch": 0.49,
991
+ "learning_rate": 0.00034825558603635346,
992
+ "loss": 0.6991,
993
+ "step": 164
994
+ },
995
+ {
996
+ "epoch": 0.49,
997
+ "learning_rate": 0.0003451591310645103,
998
+ "loss": 0.6891,
999
+ "step": 165
1000
+ },
1001
+ {
1002
+ "epoch": 0.49,
1003
+ "learning_rate": 0.0003420608379616738,
1004
+ "loss": 0.6636,
1005
+ "step": 166
1006
+ },
1007
+ {
1008
+ "epoch": 0.5,
1009
+ "learning_rate": 0.0003389609892335013,
1010
+ "loss": 0.6759,
1011
+ "step": 167
1012
+ },
1013
+ {
1014
+ "epoch": 0.5,
1015
+ "learning_rate": 0.0003358598675274942,
1016
+ "loss": 0.7353,
1017
+ "step": 168
1018
+ },
1019
+ {
1020
+ "epoch": 0.5,
1021
+ "learning_rate": 0.00033275775560722527,
1022
+ "loss": 0.6926,
1023
+ "step": 169
1024
+ },
1025
+ {
1026
+ "epoch": 0.51,
1027
+ "learning_rate": 0.0003296549363265559,
1028
+ "loss": 0.7334,
1029
+ "step": 170
1030
+ },
1031
+ {
1032
+ "epoch": 0.51,
1033
+ "learning_rate": 0.0003265516926038455,
1034
+ "loss": 0.6609,
1035
+ "step": 171
1036
+ },
1037
+ {
1038
+ "epoch": 0.51,
1039
+ "learning_rate": 0.0003234483073961544,
1040
+ "loss": 0.6996,
1041
+ "step": 172
1042
+ },
1043
+ {
1044
+ "epoch": 0.51,
1045
+ "learning_rate": 0.0003203450636734441,
1046
+ "loss": 0.6515,
1047
+ "step": 173
1048
+ },
1049
+ {
1050
+ "epoch": 0.52,
1051
+ "learning_rate": 0.00031724224439277476,
1052
+ "loss": 0.7299,
1053
+ "step": 174
1054
+ },
1055
+ {
1056
+ "epoch": 0.52,
1057
+ "learning_rate": 0.00031414013247250586,
1058
+ "loss": 0.7283,
1059
+ "step": 175
1060
+ },
1061
+ {
1062
+ "epoch": 0.52,
1063
+ "learning_rate": 0.0003110390107664987,
1064
+ "loss": 0.6697,
1065
+ "step": 176
1066
+ },
1067
+ {
1068
+ "epoch": 0.53,
1069
+ "learning_rate": 0.00030793916203832625,
1070
+ "loss": 0.6834,
1071
+ "step": 177
1072
+ },
1073
+ {
1074
+ "epoch": 0.53,
1075
+ "learning_rate": 0.00030484086893548966,
1076
+ "loss": 0.7315,
1077
+ "step": 178
1078
+ },
1079
+ {
1080
+ "epoch": 0.53,
1081
+ "learning_rate": 0.0003017444139636465,
1082
+ "loss": 0.721,
1083
+ "step": 179
1084
+ },
1085
+ {
1086
+ "epoch": 0.54,
1087
+ "learning_rate": 0.0002986500794608518,
1088
+ "loss": 0.6572,
1089
+ "step": 180
1090
+ },
1091
+ {
1092
+ "epoch": 0.54,
1093
+ "learning_rate": 0.0002955581475718138,
1094
+ "loss": 0.7086,
1095
+ "step": 181
1096
+ },
1097
+ {
1098
+ "epoch": 0.54,
1099
+ "learning_rate": 0.000292468900222168,
1100
+ "loss": 0.7413,
1101
+ "step": 182
1102
+ },
1103
+ {
1104
+ "epoch": 0.54,
1105
+ "learning_rate": 0.0002893826190927707,
1106
+ "loss": 0.7398,
1107
+ "step": 183
1108
+ },
1109
+ {
1110
+ "epoch": 0.55,
1111
+ "learning_rate": 0.0002862995855940148,
1112
+ "loss": 0.6878,
1113
+ "step": 184
1114
+ },
1115
+ {
1116
+ "epoch": 0.55,
1117
+ "learning_rate": 0.00028322008084017135,
1118
+ "loss": 0.7316,
1119
+ "step": 185
1120
+ },
1121
+ {
1122
+ "epoch": 0.55,
1123
+ "learning_rate": 0.0002801443856237563,
1124
+ "loss": 0.7612,
1125
+ "step": 186
1126
+ },
1127
+ {
1128
+ "epoch": 0.56,
1129
+ "learning_rate": 0.000277072780389928,
1130
+ "loss": 0.7012,
1131
+ "step": 187
1132
+ },
1133
+ {
1134
+ "epoch": 0.56,
1135
+ "learning_rate": 0.0002740055452109156,
1136
+ "loss": 0.7154,
1137
+ "step": 188
1138
+ },
1139
+ {
1140
+ "epoch": 0.56,
1141
+ "learning_rate": 0.0002709429597604827,
1142
+ "loss": 0.707,
1143
+ "step": 189
1144
+ },
1145
+ {
1146
+ "epoch": 0.57,
1147
+ "learning_rate": 0.0002678853032884251,
1148
+ "loss": 0.6693,
1149
+ "step": 190
1150
+ },
1151
+ {
1152
+ "epoch": 0.57,
1153
+ "learning_rate": 0.0002648328545951092,
1154
+ "loss": 0.7198,
1155
+ "step": 191
1156
+ },
1157
+ {
1158
+ "epoch": 0.57,
1159
+ "learning_rate": 0.0002617858920060506,
1160
+ "loss": 0.6859,
1161
+ "step": 192
1162
+ },
1163
+ {
1164
+ "epoch": 0.57,
1165
+ "learning_rate": 0.0002587446933465364,
1166
+ "loss": 0.7213,
1167
+ "step": 193
1168
+ },
1169
+ {
1170
+ "epoch": 0.58,
1171
+ "learning_rate": 0.00025570953591629226,
1172
+ "loss": 0.7649,
1173
+ "step": 194
1174
+ },
1175
+ {
1176
+ "epoch": 0.58,
1177
+ "learning_rate": 0.0002526806964641978,
1178
+ "loss": 0.6622,
1179
+ "step": 195
1180
+ },
1181
+ {
1182
+ "epoch": 0.58,
1183
+ "learning_rate": 0.0002496584511630533,
1184
+ "loss": 0.6788,
1185
+ "step": 196
1186
+ },
1187
+ {
1188
+ "epoch": 0.59,
1189
+ "learning_rate": 0.000246643075584397,
1190
+ "loss": 0.7441,
1191
+ "step": 197
1192
+ },
1193
+ {
1194
+ "epoch": 0.59,
1195
+ "learning_rate": 0.00024363484467337842,
1196
+ "loss": 0.6831,
1197
+ "step": 198
1198
+ },
1199
+ {
1200
+ "epoch": 0.59,
1201
+ "learning_rate": 0.0002406340327236891,
1202
+ "loss": 0.721,
1203
+ "step": 199
1204
+ },
1205
+ {
1206
+ "epoch": 0.59,
1207
+ "learning_rate": 0.00023764091335255131,
1208
+ "loss": 0.6427,
1209
+ "step": 200
1210
+ }
1211
+ ],
1212
+ "logging_steps": 1,
1213
+ "max_steps": 336,
1214
+ "num_input_tokens_seen": 0,
1215
+ "num_train_epochs": 1,
1216
+ "save_steps": 50,
1217
+ "total_flos": 2.5342363902305894e+17,
1218
+ "train_batch_size": 6,
1219
+ "trial_name": null,
1220
+ "trial_params": null
1221
+ }
checkpoint-200/training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:3a720d2daf4afe192195297ee83d1a8834602d7e8eda493eaba5ee762dd57a90
3
+ size 4664
checkpoint-250/README.md ADDED
@@ -0,0 +1,204 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ library_name: peft
3
+ base_model: unsloth/llama-2-7b
4
+ ---
5
+
6
+ # Model Card for Model ID
7
+
8
+ <!-- Provide a quick summary of what the model is/does. -->
9
+
10
+
11
+
12
+ ## Model Details
13
+
14
+ ### Model Description
15
+
16
+ <!-- Provide a longer summary of what this model is. -->
17
+
18
+
19
+
20
+ - **Developed by:** [More Information Needed]
21
+ - **Funded by [optional]:** [More Information Needed]
22
+ - **Shared by [optional]:** [More Information Needed]
23
+ - **Model type:** [More Information Needed]
24
+ - **Language(s) (NLP):** [More Information Needed]
25
+ - **License:** [More Information Needed]
26
+ - **Finetuned from model [optional]:** [More Information Needed]
27
+
28
+ ### Model Sources [optional]
29
+
30
+ <!-- Provide the basic links for the model. -->
31
+
32
+ - **Repository:** [More Information Needed]
33
+ - **Paper [optional]:** [More Information Needed]
34
+ - **Demo [optional]:** [More Information Needed]
35
+
36
+ ## Uses
37
+
38
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
39
+
40
+ ### Direct Use
41
+
42
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
43
+
44
+ [More Information Needed]
45
+
46
+ ### Downstream Use [optional]
47
+
48
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
49
+
50
+ [More Information Needed]
51
+
52
+ ### Out-of-Scope Use
53
+
54
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
55
+
56
+ [More Information Needed]
57
+
58
+ ## Bias, Risks, and Limitations
59
+
60
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
61
+
62
+ [More Information Needed]
63
+
64
+ ### Recommendations
65
+
66
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
67
+
68
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
69
+
70
+ ## How to Get Started with the Model
71
+
72
+ Use the code below to get started with the model.
73
+
74
+ [More Information Needed]
75
+
76
+ ## Training Details
77
+
78
+ ### Training Data
79
+
80
+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
81
+
82
+ [More Information Needed]
83
+
84
+ ### Training Procedure
85
+
86
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
87
+
88
+ #### Preprocessing [optional]
89
+
90
+ [More Information Needed]
91
+
92
+
93
+ #### Training Hyperparameters
94
+
95
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
96
+
97
+ #### Speeds, Sizes, Times [optional]
98
+
99
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
100
+
101
+ [More Information Needed]
102
+
103
+ ## Evaluation
104
+
105
+ <!-- This section describes the evaluation protocols and provides the results. -->
106
+
107
+ ### Testing Data, Factors & Metrics
108
+
109
+ #### Testing Data
110
+
111
+ <!-- This should link to a Dataset Card if possible. -->
112
+
113
+ [More Information Needed]
114
+
115
+ #### Factors
116
+
117
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
118
+
119
+ [More Information Needed]
120
+
121
+ #### Metrics
122
+
123
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
124
+
125
+ [More Information Needed]
126
+
127
+ ### Results
128
+
129
+ [More Information Needed]
130
+
131
+ #### Summary
132
+
133
+
134
+
135
+ ## Model Examination [optional]
136
+
137
+ <!-- Relevant interpretability work for the model goes here -->
138
+
139
+ [More Information Needed]
140
+
141
+ ## Environmental Impact
142
+
143
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
144
+
145
+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
146
+
147
+ - **Hardware Type:** [More Information Needed]
148
+ - **Hours used:** [More Information Needed]
149
+ - **Cloud Provider:** [More Information Needed]
150
+ - **Compute Region:** [More Information Needed]
151
+ - **Carbon Emitted:** [More Information Needed]
152
+
153
+ ## Technical Specifications [optional]
154
+
155
+ ### Model Architecture and Objective
156
+
157
+ [More Information Needed]
158
+
159
+ ### Compute Infrastructure
160
+
161
+ [More Information Needed]
162
+
163
+ #### Hardware
164
+
165
+ [More Information Needed]
166
+
167
+ #### Software
168
+
169
+ [More Information Needed]
170
+
171
+ ## Citation [optional]
172
+
173
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
174
+
175
+ **BibTeX:**
176
+
177
+ [More Information Needed]
178
+
179
+ **APA:**
180
+
181
+ [More Information Needed]
182
+
183
+ ## Glossary [optional]
184
+
185
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
186
+
187
+ [More Information Needed]
188
+
189
+ ## More Information [optional]
190
+
191
+ [More Information Needed]
192
+
193
+ ## Model Card Authors [optional]
194
+
195
+ [More Information Needed]
196
+
197
+ ## Model Card Contact
198
+
199
+ [More Information Needed]
200
+
201
+
202
+ ### Framework versions
203
+
204
+ - PEFT 0.7.1
checkpoint-250/adapter_config.json ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alpha_pattern": {},
3
+ "auto_mapping": null,
4
+ "base_model_name_or_path": "unsloth/llama-2-7b",
5
+ "bias": "none",
6
+ "fan_in_fan_out": false,
7
+ "inference_mode": true,
8
+ "init_lora_weights": true,
9
+ "layers_pattern": null,
10
+ "layers_to_transform": null,
11
+ "loftq_config": {},
12
+ "lora_alpha": 16,
13
+ "lora_dropout": 0,
14
+ "megatron_config": null,
15
+ "megatron_core": "megatron.core",
16
+ "modules_to_save": null,
17
+ "peft_type": "LORA",
18
+ "r": 128,
19
+ "rank_pattern": {},
20
+ "revision": "unsloth",
21
+ "target_modules": [
22
+ "up_proj",
23
+ "k_proj",
24
+ "v_proj",
25
+ "o_proj",
26
+ "q_proj",
27
+ "down_proj",
28
+ "gate_proj"
29
+ ],
30
+ "task_type": "CAUSAL_LM"
31
+ }
checkpoint-250/adapter_model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:dff8d1846aad40946115b3b7bc1e50fe2871d849b26744fc38ed90be276bdaca
3
+ size 1279323952
checkpoint-250/optimizer.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:1298ae808b5260196e5657e0bfca74f2d200068b27ca6b22314761c30e4e6b36
3
+ size 641407572
checkpoint-250/rng_state.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:fe2e145b09b2faab2c45440a9233e2700f8dca5428319c1eb306332f174e4af7
3
+ size 14244
checkpoint-250/scheduler.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:b598d421de3bc1e44e2c9a9831d1f24e3f8a3c2ab34584d610f9b65f7d130369
3
+ size 1064
checkpoint-250/trainer_state.json ADDED
@@ -0,0 +1,1521 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "best_metric": null,
3
+ "best_model_checkpoint": null,
4
+ "epoch": 0.74360499702558,
5
+ "eval_steps": 500,
6
+ "global_step": 250,
7
+ "is_hyper_param_search": false,
8
+ "is_local_process_zero": true,
9
+ "is_world_process_zero": true,
10
+ "log_history": [
11
+ {
12
+ "epoch": 0.0,
13
+ "learning_rate": 9.285714285714285e-05,
14
+ "loss": 2.4427,
15
+ "step": 1
16
+ },
17
+ {
18
+ "epoch": 0.01,
19
+ "learning_rate": 0.0001857142857142857,
20
+ "loss": 2.3973,
21
+ "step": 2
22
+ },
23
+ {
24
+ "epoch": 0.01,
25
+ "learning_rate": 0.00027857142857142854,
26
+ "loss": 2.341,
27
+ "step": 3
28
+ },
29
+ {
30
+ "epoch": 0.01,
31
+ "learning_rate": 0.0003714285714285714,
32
+ "loss": 2.1281,
33
+ "step": 4
34
+ },
35
+ {
36
+ "epoch": 0.01,
37
+ "learning_rate": 0.0004642857142857143,
38
+ "loss": 1.4346,
39
+ "step": 5
40
+ },
41
+ {
42
+ "epoch": 0.02,
43
+ "learning_rate": 0.0005571428571428571,
44
+ "loss": 1.1715,
45
+ "step": 6
46
+ },
47
+ {
48
+ "epoch": 0.02,
49
+ "learning_rate": 0.00065,
50
+ "loss": 1.086,
51
+ "step": 7
52
+ },
53
+ {
54
+ "epoch": 0.02,
55
+ "learning_rate": 0.0006499851830773117,
56
+ "loss": 0.9921,
57
+ "step": 8
58
+ },
59
+ {
60
+ "epoch": 0.03,
61
+ "learning_rate": 0.00064994073366027,
62
+ "loss": 0.9231,
63
+ "step": 9
64
+ },
65
+ {
66
+ "epoch": 0.03,
67
+ "learning_rate": 0.0006498666558018197,
68
+ "loss": 0.9343,
69
+ "step": 10
70
+ },
71
+ {
72
+ "epoch": 0.03,
73
+ "learning_rate": 0.0006497629562564588,
74
+ "loss": 0.9191,
75
+ "step": 11
76
+ },
77
+ {
78
+ "epoch": 0.04,
79
+ "learning_rate": 0.0006496296444796219,
80
+ "loss": 0.8791,
81
+ "step": 12
82
+ },
83
+ {
84
+ "epoch": 0.04,
85
+ "learning_rate": 0.0006494667326268186,
86
+ "loss": 0.8632,
87
+ "step": 13
88
+ },
89
+ {
90
+ "epoch": 0.04,
91
+ "learning_rate": 0.0006492742355525248,
92
+ "loss": 0.9267,
93
+ "step": 14
94
+ },
95
+ {
96
+ "epoch": 0.04,
97
+ "learning_rate": 0.0006490521708088281,
98
+ "loss": 0.8644,
99
+ "step": 15
100
+ },
101
+ {
102
+ "epoch": 0.05,
103
+ "learning_rate": 0.000648800558643828,
104
+ "loss": 0.8353,
105
+ "step": 16
106
+ },
107
+ {
108
+ "epoch": 0.05,
109
+ "learning_rate": 0.0006485194219997891,
110
+ "loss": 0.8482,
111
+ "step": 17
112
+ },
113
+ {
114
+ "epoch": 0.05,
115
+ "learning_rate": 0.0006482087865110493,
116
+ "loss": 0.8587,
117
+ "step": 18
118
+ },
119
+ {
120
+ "epoch": 0.06,
121
+ "learning_rate": 0.0006478686805016826,
122
+ "loss": 0.9134,
123
+ "step": 19
124
+ },
125
+ {
126
+ "epoch": 0.06,
127
+ "learning_rate": 0.0006474991349829163,
128
+ "loss": 0.8238,
129
+ "step": 20
130
+ },
131
+ {
132
+ "epoch": 0.06,
133
+ "learning_rate": 0.0006471001836503035,
134
+ "loss": 0.8329,
135
+ "step": 21
136
+ },
137
+ {
138
+ "epoch": 0.07,
139
+ "learning_rate": 0.0006466718628806508,
140
+ "loss": 0.7995,
141
+ "step": 22
142
+ },
143
+ {
144
+ "epoch": 0.07,
145
+ "learning_rate": 0.0006462142117287011,
146
+ "loss": 0.8363,
147
+ "step": 23
148
+ },
149
+ {
150
+ "epoch": 0.07,
151
+ "learning_rate": 0.0006457272719235728,
152
+ "loss": 0.7942,
153
+ "step": 24
154
+ },
155
+ {
156
+ "epoch": 0.07,
157
+ "learning_rate": 0.0006452110878649547,
158
+ "loss": 0.858,
159
+ "step": 25
160
+ },
161
+ {
162
+ "epoch": 0.08,
163
+ "learning_rate": 0.0006446657066190579,
164
+ "loss": 0.8474,
165
+ "step": 26
166
+ },
167
+ {
168
+ "epoch": 0.08,
169
+ "learning_rate": 0.000644091177914324,
170
+ "loss": 0.8175,
171
+ "step": 27
172
+ },
173
+ {
174
+ "epoch": 0.08,
175
+ "learning_rate": 0.0006434875541368907,
176
+ "loss": 0.7821,
177
+ "step": 28
178
+ },
179
+ {
180
+ "epoch": 0.09,
181
+ "learning_rate": 0.0006428548903258156,
182
+ "loss": 0.8583,
183
+ "step": 29
184
+ },
185
+ {
186
+ "epoch": 0.09,
187
+ "learning_rate": 0.0006421932441680574,
188
+ "loss": 0.8071,
189
+ "step": 30
190
+ },
191
+ {
192
+ "epoch": 0.09,
193
+ "learning_rate": 0.0006415026759932158,
194
+ "loss": 0.805,
195
+ "step": 31
196
+ },
197
+ {
198
+ "epoch": 0.1,
199
+ "learning_rate": 0.0006407832487680309,
200
+ "loss": 0.881,
201
+ "step": 32
202
+ },
203
+ {
204
+ "epoch": 0.1,
205
+ "learning_rate": 0.0006400350280906415,
206
+ "loss": 0.8302,
207
+ "step": 33
208
+ },
209
+ {
210
+ "epoch": 0.1,
211
+ "learning_rate": 0.0006392580821846041,
212
+ "loss": 0.8456,
213
+ "step": 34
214
+ },
215
+ {
216
+ "epoch": 0.1,
217
+ "learning_rate": 0.0006384524818926723,
218
+ "loss": 0.8067,
219
+ "step": 35
220
+ },
221
+ {
222
+ "epoch": 0.11,
223
+ "learning_rate": 0.0006376183006703367,
224
+ "loss": 0.8307,
225
+ "step": 36
226
+ },
227
+ {
228
+ "epoch": 0.11,
229
+ "learning_rate": 0.0006367556145791275,
230
+ "loss": 0.8347,
231
+ "step": 37
232
+ },
233
+ {
234
+ "epoch": 0.11,
235
+ "learning_rate": 0.0006358645022796795,
236
+ "loss": 0.8086,
237
+ "step": 38
238
+ },
239
+ {
240
+ "epoch": 0.12,
241
+ "learning_rate": 0.0006349450450245589,
242
+ "loss": 0.8726,
243
+ "step": 39
244
+ },
245
+ {
246
+ "epoch": 0.12,
247
+ "learning_rate": 0.0006339973266508556,
248
+ "loss": 0.78,
249
+ "step": 40
250
+ },
251
+ {
252
+ "epoch": 0.12,
253
+ "learning_rate": 0.0006330214335725379,
254
+ "loss": 0.7902,
255
+ "step": 41
256
+ },
257
+ {
258
+ "epoch": 0.12,
259
+ "learning_rate": 0.0006320174547725736,
260
+ "loss": 0.7823,
261
+ "step": 42
262
+ },
263
+ {
264
+ "epoch": 0.13,
265
+ "learning_rate": 0.0006309854817948169,
266
+ "loss": 0.8211,
267
+ "step": 43
268
+ },
269
+ {
270
+ "epoch": 0.13,
271
+ "learning_rate": 0.0006299256087356603,
272
+ "loss": 0.8656,
273
+ "step": 44
274
+ },
275
+ {
276
+ "epoch": 0.13,
277
+ "learning_rate": 0.000628837932235456,
278
+ "loss": 0.7767,
279
+ "step": 45
280
+ },
281
+ {
282
+ "epoch": 0.14,
283
+ "learning_rate": 0.0006277225514697028,
284
+ "loss": 0.7542,
285
+ "step": 46
286
+ },
287
+ {
288
+ "epoch": 0.14,
289
+ "learning_rate": 0.0006265795681400046,
290
+ "loss": 0.8254,
291
+ "step": 47
292
+ },
293
+ {
294
+ "epoch": 0.14,
295
+ "learning_rate": 0.0006254090864647957,
296
+ "loss": 0.8099,
297
+ "step": 48
298
+ },
299
+ {
300
+ "epoch": 0.15,
301
+ "learning_rate": 0.0006242112131698394,
302
+ "loss": 0.7786,
303
+ "step": 49
304
+ },
305
+ {
306
+ "epoch": 0.15,
307
+ "learning_rate": 0.0006229860574784954,
308
+ "loss": 0.7895,
309
+ "step": 50
310
+ },
311
+ {
312
+ "epoch": 0.15,
313
+ "learning_rate": 0.0006217337311017619,
314
+ "loss": 0.8092,
315
+ "step": 51
316
+ },
317
+ {
318
+ "epoch": 0.15,
319
+ "learning_rate": 0.0006204543482280886,
320
+ "loss": 0.7998,
321
+ "step": 52
322
+ },
323
+ {
324
+ "epoch": 0.16,
325
+ "learning_rate": 0.0006191480255129656,
326
+ "loss": 0.802,
327
+ "step": 53
328
+ },
329
+ {
330
+ "epoch": 0.16,
331
+ "learning_rate": 0.0006178148820682862,
332
+ "loss": 0.7691,
333
+ "step": 54
334
+ },
335
+ {
336
+ "epoch": 0.16,
337
+ "learning_rate": 0.0006164550394514865,
338
+ "loss": 0.8572,
339
+ "step": 55
340
+ },
341
+ {
342
+ "epoch": 0.17,
343
+ "learning_rate": 0.0006150686216544614,
344
+ "loss": 0.7915,
345
+ "step": 56
346
+ },
347
+ {
348
+ "epoch": 0.17,
349
+ "learning_rate": 0.0006136557550922589,
350
+ "loss": 0.759,
351
+ "step": 57
352
+ },
353
+ {
354
+ "epoch": 0.17,
355
+ "learning_rate": 0.0006122165685915537,
356
+ "loss": 0.7282,
357
+ "step": 58
358
+ },
359
+ {
360
+ "epoch": 0.18,
361
+ "learning_rate": 0.0006107511933789002,
362
+ "loss": 0.768,
363
+ "step": 59
364
+ },
365
+ {
366
+ "epoch": 0.18,
367
+ "learning_rate": 0.0006092597630687677,
368
+ "loss": 0.7868,
369
+ "step": 60
370
+ },
371
+ {
372
+ "epoch": 0.18,
373
+ "learning_rate": 0.0006077424136513567,
374
+ "loss": 0.7827,
375
+ "step": 61
376
+ },
377
+ {
378
+ "epoch": 0.18,
379
+ "learning_rate": 0.0006061992834801996,
380
+ "loss": 0.7755,
381
+ "step": 62
382
+ },
383
+ {
384
+ "epoch": 0.19,
385
+ "learning_rate": 0.0006046305132595453,
386
+ "loss": 0.8332,
387
+ "step": 63
388
+ },
389
+ {
390
+ "epoch": 0.19,
391
+ "learning_rate": 0.0006030362460315296,
392
+ "loss": 0.7241,
393
+ "step": 64
394
+ },
395
+ {
396
+ "epoch": 0.19,
397
+ "learning_rate": 0.0006014166271631326,
398
+ "loss": 0.8074,
399
+ "step": 65
400
+ },
401
+ {
402
+ "epoch": 0.2,
403
+ "learning_rate": 0.000599771804332924,
404
+ "loss": 0.7491,
405
+ "step": 66
406
+ },
407
+ {
408
+ "epoch": 0.2,
409
+ "learning_rate": 0.0005981019275175972,
410
+ "loss": 0.8004,
411
+ "step": 67
412
+ },
413
+ {
414
+ "epoch": 0.2,
415
+ "learning_rate": 0.000596407148978295,
416
+ "loss": 0.7442,
417
+ "step": 68
418
+ },
419
+ {
420
+ "epoch": 0.21,
421
+ "learning_rate": 0.0005946876232467254,
422
+ "loss": 0.6956,
423
+ "step": 69
424
+ },
425
+ {
426
+ "epoch": 0.21,
427
+ "learning_rate": 0.0005929435071110721,
428
+ "loss": 0.7472,
429
+ "step": 70
430
+ },
431
+ {
432
+ "epoch": 0.21,
433
+ "learning_rate": 0.0005911749596016978,
434
+ "loss": 0.8097,
435
+ "step": 71
436
+ },
437
+ {
438
+ "epoch": 0.21,
439
+ "learning_rate": 0.0005893821419766438,
440
+ "loss": 0.8002,
441
+ "step": 72
442
+ },
443
+ {
444
+ "epoch": 0.22,
445
+ "learning_rate": 0.0005875652177069265,
446
+ "loss": 0.8203,
447
+ "step": 73
448
+ },
449
+ {
450
+ "epoch": 0.22,
451
+ "learning_rate": 0.0005857243524616315,
452
+ "loss": 0.7339,
453
+ "step": 74
454
+ },
455
+ {
456
+ "epoch": 0.22,
457
+ "learning_rate": 0.0005838597140928082,
458
+ "loss": 0.6844,
459
+ "step": 75
460
+ },
461
+ {
462
+ "epoch": 0.23,
463
+ "learning_rate": 0.0005819714726201646,
464
+ "loss": 0.7211,
465
+ "step": 76
466
+ },
467
+ {
468
+ "epoch": 0.23,
469
+ "learning_rate": 0.0005800598002155648,
470
+ "loss": 0.8289,
471
+ "step": 77
472
+ },
473
+ {
474
+ "epoch": 0.23,
475
+ "learning_rate": 0.0005781248711873302,
476
+ "loss": 0.7686,
477
+ "step": 78
478
+ },
479
+ {
480
+ "epoch": 0.23,
481
+ "learning_rate": 0.0005761668619643458,
482
+ "loss": 0.7618,
483
+ "step": 79
484
+ },
485
+ {
486
+ "epoch": 0.24,
487
+ "learning_rate": 0.0005741859510799734,
488
+ "loss": 0.779,
489
+ "step": 80
490
+ },
491
+ {
492
+ "epoch": 0.24,
493
+ "learning_rate": 0.0005721823191557725,
494
+ "loss": 0.7599,
495
+ "step": 81
496
+ },
497
+ {
498
+ "epoch": 0.24,
499
+ "learning_rate": 0.0005701561488850312,
500
+ "loss": 0.7495,
501
+ "step": 82
502
+ },
503
+ {
504
+ "epoch": 0.25,
505
+ "learning_rate": 0.000568107625016108,
506
+ "loss": 0.6977,
507
+ "step": 83
508
+ },
509
+ {
510
+ "epoch": 0.25,
511
+ "learning_rate": 0.0005660369343355862,
512
+ "loss": 0.757,
513
+ "step": 84
514
+ },
515
+ {
516
+ "epoch": 0.25,
517
+ "learning_rate": 0.0005639442656512426,
518
+ "loss": 0.8295,
519
+ "step": 85
520
+ },
521
+ {
522
+ "epoch": 0.26,
523
+ "learning_rate": 0.0005618298097748316,
524
+ "loss": 0.7394,
525
+ "step": 86
526
+ },
527
+ {
528
+ "epoch": 0.26,
529
+ "learning_rate": 0.0005596937595046872,
530
+ "loss": 0.7728,
531
+ "step": 87
532
+ },
533
+ {
534
+ "epoch": 0.26,
535
+ "learning_rate": 0.0005575363096081429,
536
+ "loss": 0.7721,
537
+ "step": 88
538
+ },
539
+ {
540
+ "epoch": 0.26,
541
+ "learning_rate": 0.0005553576568037731,
542
+ "loss": 0.7191,
543
+ "step": 89
544
+ },
545
+ {
546
+ "epoch": 0.27,
547
+ "learning_rate": 0.0005531579997434555,
548
+ "loss": 0.7594,
549
+ "step": 90
550
+ },
551
+ {
552
+ "epoch": 0.27,
553
+ "learning_rate": 0.0005509375389942588,
554
+ "loss": 0.7511,
555
+ "step": 91
556
+ },
557
+ {
558
+ "epoch": 0.27,
559
+ "learning_rate": 0.0005486964770201533,
560
+ "loss": 0.7644,
561
+ "step": 92
562
+ },
563
+ {
564
+ "epoch": 0.28,
565
+ "learning_rate": 0.0005464350181635519,
566
+ "loss": 0.7403,
567
+ "step": 93
568
+ },
569
+ {
570
+ "epoch": 0.28,
571
+ "learning_rate": 0.000544153368626676,
572
+ "loss": 0.7204,
573
+ "step": 94
574
+ },
575
+ {
576
+ "epoch": 0.28,
577
+ "learning_rate": 0.0005418517364527552,
578
+ "loss": 0.7358,
579
+ "step": 95
580
+ },
581
+ {
582
+ "epoch": 0.29,
583
+ "learning_rate": 0.0005395303315070571,
584
+ "loss": 0.7607,
585
+ "step": 96
586
+ },
587
+ {
588
+ "epoch": 0.29,
589
+ "learning_rate": 0.0005371893654577517,
590
+ "loss": 0.7505,
591
+ "step": 97
592
+ },
593
+ {
594
+ "epoch": 0.29,
595
+ "learning_rate": 0.0005348290517566107,
596
+ "loss": 0.7274,
597
+ "step": 98
598
+ },
599
+ {
600
+ "epoch": 0.29,
601
+ "learning_rate": 0.0005324496056195461,
602
+ "loss": 0.8161,
603
+ "step": 99
604
+ },
605
+ {
606
+ "epoch": 0.3,
607
+ "learning_rate": 0.0005300512440069852,
608
+ "loss": 0.7043,
609
+ "step": 100
610
+ },
611
+ {
612
+ "epoch": 0.3,
613
+ "learning_rate": 0.0005276341856040884,
614
+ "loss": 0.7921,
615
+ "step": 101
616
+ },
617
+ {
618
+ "epoch": 0.3,
619
+ "learning_rate": 0.0005251986508008097,
620
+ "loss": 0.6852,
621
+ "step": 102
622
+ },
623
+ {
624
+ "epoch": 0.31,
625
+ "learning_rate": 0.0005227448616718004,
626
+ "loss": 0.6996,
627
+ "step": 103
628
+ },
629
+ {
630
+ "epoch": 0.31,
631
+ "learning_rate": 0.0005202730419561611,
632
+ "loss": 0.6323,
633
+ "step": 104
634
+ },
635
+ {
636
+ "epoch": 0.31,
637
+ "learning_rate": 0.0005177834170370404,
638
+ "loss": 0.7601,
639
+ "step": 105
640
+ },
641
+ {
642
+ "epoch": 0.32,
643
+ "learning_rate": 0.0005152762139210839,
644
+ "loss": 0.7499,
645
+ "step": 106
646
+ },
647
+ {
648
+ "epoch": 0.32,
649
+ "learning_rate": 0.0005127516612177365,
650
+ "loss": 0.73,
651
+ "step": 107
652
+ },
653
+ {
654
+ "epoch": 0.32,
655
+ "learning_rate": 0.0005102099891183958,
656
+ "loss": 0.7438,
657
+ "step": 108
658
+ },
659
+ {
660
+ "epoch": 0.32,
661
+ "learning_rate": 0.0005076514293754255,
662
+ "loss": 0.6614,
663
+ "step": 109
664
+ },
665
+ {
666
+ "epoch": 0.33,
667
+ "learning_rate": 0.0005050762152810218,
668
+ "loss": 0.7599,
669
+ "step": 110
670
+ },
671
+ {
672
+ "epoch": 0.33,
673
+ "learning_rate": 0.0005024845816459423,
674
+ "loss": 0.7471,
675
+ "step": 111
676
+ },
677
+ {
678
+ "epoch": 0.33,
679
+ "learning_rate": 0.0004998767647780961,
680
+ "loss": 0.7569,
681
+ "step": 112
682
+ },
683
+ {
684
+ "epoch": 0.34,
685
+ "learning_rate": 0.0004972530024609966,
686
+ "loss": 0.6561,
687
+ "step": 113
688
+ },
689
+ {
690
+ "epoch": 0.34,
691
+ "learning_rate": 0.0004946135339320798,
692
+ "loss": 0.7369,
693
+ "step": 114
694
+ },
695
+ {
696
+ "epoch": 0.34,
697
+ "learning_rate": 0.0004919585998608917,
698
+ "loss": 0.755,
699
+ "step": 115
700
+ },
701
+ {
702
+ "epoch": 0.35,
703
+ "learning_rate": 0.0004892884423271417,
704
+ "loss": 0.7307,
705
+ "step": 116
706
+ },
707
+ {
708
+ "epoch": 0.35,
709
+ "learning_rate": 0.0004866033047986317,
710
+ "loss": 0.7321,
711
+ "step": 117
712
+ },
713
+ {
714
+ "epoch": 0.35,
715
+ "learning_rate": 0.00048390343210905486,
716
+ "loss": 0.772,
717
+ "step": 118
718
+ },
719
+ {
720
+ "epoch": 0.35,
721
+ "learning_rate": 0.0004811890704356722,
722
+ "loss": 0.6707,
723
+ "step": 119
724
+ },
725
+ {
726
+ "epoch": 0.36,
727
+ "learning_rate": 0.0004784604672768657,
728
+ "loss": 0.695,
729
+ "step": 120
730
+ },
731
+ {
732
+ "epoch": 0.36,
733
+ "learning_rate": 0.0004757178714295709,
734
+ "loss": 0.6372,
735
+ "step": 121
736
+ },
737
+ {
738
+ "epoch": 0.36,
739
+ "learning_rate": 0.0004729615329665918,
740
+ "loss": 0.7303,
741
+ "step": 122
742
+ },
743
+ {
744
+ "epoch": 0.37,
745
+ "learning_rate": 0.0004701917032137987,
746
+ "loss": 0.7313,
747
+ "step": 123
748
+ },
749
+ {
750
+ "epoch": 0.37,
751
+ "learning_rate": 0.00046740863472721176,
752
+ "loss": 0.6939,
753
+ "step": 124
754
+ },
755
+ {
756
+ "epoch": 0.37,
757
+ "learning_rate": 0.0004646125812699734,
758
+ "loss": 0.711,
759
+ "step": 125
760
+ },
761
+ {
762
+ "epoch": 0.37,
763
+ "learning_rate": 0.0004618037977892089,
764
+ "loss": 0.7238,
765
+ "step": 126
766
+ },
767
+ {
768
+ "epoch": 0.38,
769
+ "learning_rate": 0.00045898254039278106,
770
+ "loss": 0.7508,
771
+ "step": 127
772
+ },
773
+ {
774
+ "epoch": 0.38,
775
+ "learning_rate": 0.0004561490663259375,
776
+ "loss": 0.7817,
777
+ "step": 128
778
+ },
779
+ {
780
+ "epoch": 0.38,
781
+ "learning_rate": 0.00045330363394785467,
782
+ "loss": 0.7149,
783
+ "step": 129
784
+ },
785
+ {
786
+ "epoch": 0.39,
787
+ "learning_rate": 0.0004504465027080806,
788
+ "loss": 0.7987,
789
+ "step": 130
790
+ },
791
+ {
792
+ "epoch": 0.39,
793
+ "learning_rate": 0.00044757793312287807,
794
+ "loss": 0.7047,
795
+ "step": 131
796
+ },
797
+ {
798
+ "epoch": 0.39,
799
+ "learning_rate": 0.00044469818675147024,
800
+ "loss": 0.7322,
801
+ "step": 132
802
+ },
803
+ {
804
+ "epoch": 0.4,
805
+ "learning_rate": 0.0004418075261721916,
806
+ "loss": 0.71,
807
+ "step": 133
808
+ },
809
+ {
810
+ "epoch": 0.4,
811
+ "learning_rate": 0.0004389062149585456,
812
+ "loss": 0.7306,
813
+ "step": 134
814
+ },
815
+ {
816
+ "epoch": 0.4,
817
+ "learning_rate": 0.0004359945176551721,
818
+ "loss": 0.6989,
819
+ "step": 135
820
+ },
821
+ {
822
+ "epoch": 0.4,
823
+ "learning_rate": 0.00043307269975372513,
824
+ "loss": 0.6898,
825
+ "step": 136
826
+ },
827
+ {
828
+ "epoch": 0.41,
829
+ "learning_rate": 0.0004301410276686663,
830
+ "loss": 0.7431,
831
+ "step": 137
832
+ },
833
+ {
834
+ "epoch": 0.41,
835
+ "learning_rate": 0.00042719976871297155,
836
+ "loss": 0.7236,
837
+ "step": 138
838
+ },
839
+ {
840
+ "epoch": 0.41,
841
+ "learning_rate": 0.0004242491910737582,
842
+ "loss": 0.7704,
843
+ "step": 139
844
+ },
845
+ {
846
+ "epoch": 0.42,
847
+ "learning_rate": 0.0004212895637878311,
848
+ "loss": 0.7125,
849
+ "step": 140
850
+ },
851
+ {
852
+ "epoch": 0.42,
853
+ "learning_rate": 0.00041832115671715107,
854
+ "loss": 0.7869,
855
+ "step": 141
856
+ },
857
+ {
858
+ "epoch": 0.42,
859
+ "learning_rate": 0.00041534424052422966,
860
+ "loss": 0.714,
861
+ "step": 142
862
+ },
863
+ {
864
+ "epoch": 0.43,
865
+ "learning_rate": 0.00041235908664744866,
866
+ "loss": 0.6927,
867
+ "step": 143
868
+ },
869
+ {
870
+ "epoch": 0.43,
871
+ "learning_rate": 0.00040936596727631104,
872
+ "loss": 0.7168,
873
+ "step": 144
874
+ },
875
+ {
876
+ "epoch": 0.43,
877
+ "learning_rate": 0.0004063651553266216,
878
+ "loss": 0.7199,
879
+ "step": 145
880
+ },
881
+ {
882
+ "epoch": 0.43,
883
+ "learning_rate": 0.00040335692441560304,
884
+ "loss": 0.7084,
885
+ "step": 146
886
+ },
887
+ {
888
+ "epoch": 0.44,
889
+ "learning_rate": 0.00040034154883694667,
890
+ "loss": 0.728,
891
+ "step": 147
892
+ },
893
+ {
894
+ "epoch": 0.44,
895
+ "learning_rate": 0.00039731930353580216,
896
+ "loss": 0.7368,
897
+ "step": 148
898
+ },
899
+ {
900
+ "epoch": 0.44,
901
+ "learning_rate": 0.0003942904640837078,
902
+ "loss": 0.7298,
903
+ "step": 149
904
+ },
905
+ {
906
+ "epoch": 0.45,
907
+ "learning_rate": 0.00039125530665346355,
908
+ "loss": 0.73,
909
+ "step": 150
910
+ },
911
+ {
912
+ "epoch": 0.45,
913
+ "learning_rate": 0.00038821410799394935,
914
+ "loss": 0.6635,
915
+ "step": 151
916
+ },
917
+ {
918
+ "epoch": 0.45,
919
+ "learning_rate": 0.0003851671454048909,
920
+ "loss": 0.7228,
921
+ "step": 152
922
+ },
923
+ {
924
+ "epoch": 0.46,
925
+ "learning_rate": 0.00038211469671157496,
926
+ "loss": 0.7276,
927
+ "step": 153
928
+ },
929
+ {
930
+ "epoch": 0.46,
931
+ "learning_rate": 0.00037905704023951726,
932
+ "loss": 0.7386,
933
+ "step": 154
934
+ },
935
+ {
936
+ "epoch": 0.46,
937
+ "learning_rate": 0.0003759944547890843,
938
+ "loss": 0.7577,
939
+ "step": 155
940
+ },
941
+ {
942
+ "epoch": 0.46,
943
+ "learning_rate": 0.0003729272196100721,
944
+ "loss": 0.6462,
945
+ "step": 156
946
+ },
947
+ {
948
+ "epoch": 0.47,
949
+ "learning_rate": 0.0003698556143762437,
950
+ "loss": 0.7328,
951
+ "step": 157
952
+ },
953
+ {
954
+ "epoch": 0.47,
955
+ "learning_rate": 0.0003667799191598287,
956
+ "loss": 0.7096,
957
+ "step": 158
958
+ },
959
+ {
960
+ "epoch": 0.47,
961
+ "learning_rate": 0.00036370041440598517,
962
+ "loss": 0.6856,
963
+ "step": 159
964
+ },
965
+ {
966
+ "epoch": 0.48,
967
+ "learning_rate": 0.0003606173809072294,
968
+ "loss": 0.7114,
969
+ "step": 160
970
+ },
971
+ {
972
+ "epoch": 0.48,
973
+ "learning_rate": 0.000357531099777832,
974
+ "loss": 0.694,
975
+ "step": 161
976
+ },
977
+ {
978
+ "epoch": 0.48,
979
+ "learning_rate": 0.00035444185242818624,
980
+ "loss": 0.7436,
981
+ "step": 162
982
+ },
983
+ {
984
+ "epoch": 0.48,
985
+ "learning_rate": 0.0003513499205391482,
986
+ "loss": 0.727,
987
+ "step": 163
988
+ },
989
+ {
990
+ "epoch": 0.49,
991
+ "learning_rate": 0.00034825558603635346,
992
+ "loss": 0.6991,
993
+ "step": 164
994
+ },
995
+ {
996
+ "epoch": 0.49,
997
+ "learning_rate": 0.0003451591310645103,
998
+ "loss": 0.6891,
999
+ "step": 165
1000
+ },
1001
+ {
1002
+ "epoch": 0.49,
1003
+ "learning_rate": 0.0003420608379616738,
1004
+ "loss": 0.6636,
1005
+ "step": 166
1006
+ },
1007
+ {
1008
+ "epoch": 0.5,
1009
+ "learning_rate": 0.0003389609892335013,
1010
+ "loss": 0.6759,
1011
+ "step": 167
1012
+ },
1013
+ {
1014
+ "epoch": 0.5,
1015
+ "learning_rate": 0.0003358598675274942,
1016
+ "loss": 0.7353,
1017
+ "step": 168
1018
+ },
1019
+ {
1020
+ "epoch": 0.5,
1021
+ "learning_rate": 0.00033275775560722527,
1022
+ "loss": 0.6926,
1023
+ "step": 169
1024
+ },
1025
+ {
1026
+ "epoch": 0.51,
1027
+ "learning_rate": 0.0003296549363265559,
1028
+ "loss": 0.7334,
1029
+ "step": 170
1030
+ },
1031
+ {
1032
+ "epoch": 0.51,
1033
+ "learning_rate": 0.0003265516926038455,
1034
+ "loss": 0.6609,
1035
+ "step": 171
1036
+ },
1037
+ {
1038
+ "epoch": 0.51,
1039
+ "learning_rate": 0.0003234483073961544,
1040
+ "loss": 0.6996,
1041
+ "step": 172
1042
+ },
1043
+ {
1044
+ "epoch": 0.51,
1045
+ "learning_rate": 0.0003203450636734441,
1046
+ "loss": 0.6515,
1047
+ "step": 173
1048
+ },
1049
+ {
1050
+ "epoch": 0.52,
1051
+ "learning_rate": 0.00031724224439277476,
1052
+ "loss": 0.7299,
1053
+ "step": 174
1054
+ },
1055
+ {
1056
+ "epoch": 0.52,
1057
+ "learning_rate": 0.00031414013247250586,
1058
+ "loss": 0.7283,
1059
+ "step": 175
1060
+ },
1061
+ {
1062
+ "epoch": 0.52,
1063
+ "learning_rate": 0.0003110390107664987,
1064
+ "loss": 0.6697,
1065
+ "step": 176
1066
+ },
1067
+ {
1068
+ "epoch": 0.53,
1069
+ "learning_rate": 0.00030793916203832625,
1070
+ "loss": 0.6834,
1071
+ "step": 177
1072
+ },
1073
+ {
1074
+ "epoch": 0.53,
1075
+ "learning_rate": 0.00030484086893548966,
1076
+ "loss": 0.7315,
1077
+ "step": 178
1078
+ },
1079
+ {
1080
+ "epoch": 0.53,
1081
+ "learning_rate": 0.0003017444139636465,
1082
+ "loss": 0.721,
1083
+ "step": 179
1084
+ },
1085
+ {
1086
+ "epoch": 0.54,
1087
+ "learning_rate": 0.0002986500794608518,
1088
+ "loss": 0.6572,
1089
+ "step": 180
1090
+ },
1091
+ {
1092
+ "epoch": 0.54,
1093
+ "learning_rate": 0.0002955581475718138,
1094
+ "loss": 0.7086,
1095
+ "step": 181
1096
+ },
1097
+ {
1098
+ "epoch": 0.54,
1099
+ "learning_rate": 0.000292468900222168,
1100
+ "loss": 0.7413,
1101
+ "step": 182
1102
+ },
1103
+ {
1104
+ "epoch": 0.54,
1105
+ "learning_rate": 0.0002893826190927707,
1106
+ "loss": 0.7398,
1107
+ "step": 183
1108
+ },
1109
+ {
1110
+ "epoch": 0.55,
1111
+ "learning_rate": 0.0002862995855940148,
1112
+ "loss": 0.6878,
1113
+ "step": 184
1114
+ },
1115
+ {
1116
+ "epoch": 0.55,
1117
+ "learning_rate": 0.00028322008084017135,
1118
+ "loss": 0.7316,
1119
+ "step": 185
1120
+ },
1121
+ {
1122
+ "epoch": 0.55,
1123
+ "learning_rate": 0.0002801443856237563,
1124
+ "loss": 0.7612,
1125
+ "step": 186
1126
+ },
1127
+ {
1128
+ "epoch": 0.56,
1129
+ "learning_rate": 0.000277072780389928,
1130
+ "loss": 0.7012,
1131
+ "step": 187
1132
+ },
1133
+ {
1134
+ "epoch": 0.56,
1135
+ "learning_rate": 0.0002740055452109156,
1136
+ "loss": 0.7154,
1137
+ "step": 188
1138
+ },
1139
+ {
1140
+ "epoch": 0.56,
1141
+ "learning_rate": 0.0002709429597604827,
1142
+ "loss": 0.707,
1143
+ "step": 189
1144
+ },
1145
+ {
1146
+ "epoch": 0.57,
1147
+ "learning_rate": 0.0002678853032884251,
1148
+ "loss": 0.6693,
1149
+ "step": 190
1150
+ },
1151
+ {
1152
+ "epoch": 0.57,
1153
+ "learning_rate": 0.0002648328545951092,
1154
+ "loss": 0.7198,
1155
+ "step": 191
1156
+ },
1157
+ {
1158
+ "epoch": 0.57,
1159
+ "learning_rate": 0.0002617858920060506,
1160
+ "loss": 0.6859,
1161
+ "step": 192
1162
+ },
1163
+ {
1164
+ "epoch": 0.57,
1165
+ "learning_rate": 0.0002587446933465364,
1166
+ "loss": 0.7213,
1167
+ "step": 193
1168
+ },
1169
+ {
1170
+ "epoch": 0.58,
1171
+ "learning_rate": 0.00025570953591629226,
1172
+ "loss": 0.7649,
1173
+ "step": 194
1174
+ },
1175
+ {
1176
+ "epoch": 0.58,
1177
+ "learning_rate": 0.0002526806964641978,
1178
+ "loss": 0.6622,
1179
+ "step": 195
1180
+ },
1181
+ {
1182
+ "epoch": 0.58,
1183
+ "learning_rate": 0.0002496584511630533,
1184
+ "loss": 0.6788,
1185
+ "step": 196
1186
+ },
1187
+ {
1188
+ "epoch": 0.59,
1189
+ "learning_rate": 0.000246643075584397,
1190
+ "loss": 0.7441,
1191
+ "step": 197
1192
+ },
1193
+ {
1194
+ "epoch": 0.59,
1195
+ "learning_rate": 0.00024363484467337842,
1196
+ "loss": 0.6831,
1197
+ "step": 198
1198
+ },
1199
+ {
1200
+ "epoch": 0.59,
1201
+ "learning_rate": 0.0002406340327236891,
1202
+ "loss": 0.721,
1203
+ "step": 199
1204
+ },
1205
+ {
1206
+ "epoch": 0.59,
1207
+ "learning_rate": 0.00023764091335255131,
1208
+ "loss": 0.6427,
1209
+ "step": 200
1210
+ },
1211
+ {
1212
+ "epoch": 0.6,
1213
+ "learning_rate": 0.00023465575947577034,
1214
+ "loss": 0.6415,
1215
+ "step": 201
1216
+ },
1217
+ {
1218
+ "epoch": 0.6,
1219
+ "learning_rate": 0.0002316788432828489,
1220
+ "loss": 0.6217,
1221
+ "step": 202
1222
+ },
1223
+ {
1224
+ "epoch": 0.6,
1225
+ "learning_rate": 0.00022871043621216898,
1226
+ "loss": 0.7135,
1227
+ "step": 203
1228
+ },
1229
+ {
1230
+ "epoch": 0.61,
1231
+ "learning_rate": 0.0002257508089262417,
1232
+ "loss": 0.6396,
1233
+ "step": 204
1234
+ },
1235
+ {
1236
+ "epoch": 0.61,
1237
+ "learning_rate": 0.0002228002312870284,
1238
+ "loss": 0.6426,
1239
+ "step": 205
1240
+ },
1241
+ {
1242
+ "epoch": 0.61,
1243
+ "learning_rate": 0.0002198589723313337,
1244
+ "loss": 0.7122,
1245
+ "step": 206
1246
+ },
1247
+ {
1248
+ "epoch": 0.62,
1249
+ "learning_rate": 0.00021692730024627484,
1250
+ "loss": 0.6733,
1251
+ "step": 207
1252
+ },
1253
+ {
1254
+ "epoch": 0.62,
1255
+ "learning_rate": 0.000214005482344828,
1256
+ "loss": 0.6623,
1257
+ "step": 208
1258
+ },
1259
+ {
1260
+ "epoch": 0.62,
1261
+ "learning_rate": 0.00021109378504145427,
1262
+ "loss": 0.6955,
1263
+ "step": 209
1264
+ },
1265
+ {
1266
+ "epoch": 0.62,
1267
+ "learning_rate": 0.00020819247382780837,
1268
+ "loss": 0.7119,
1269
+ "step": 210
1270
+ },
1271
+ {
1272
+ "epoch": 0.63,
1273
+ "learning_rate": 0.0002053018132485298,
1274
+ "loss": 0.7348,
1275
+ "step": 211
1276
+ },
1277
+ {
1278
+ "epoch": 0.63,
1279
+ "learning_rate": 0.00020242206687712196,
1280
+ "loss": 0.6481,
1281
+ "step": 212
1282
+ },
1283
+ {
1284
+ "epoch": 0.63,
1285
+ "learning_rate": 0.00019955349729191941,
1286
+ "loss": 0.7042,
1287
+ "step": 213
1288
+ },
1289
+ {
1290
+ "epoch": 0.64,
1291
+ "learning_rate": 0.00019669636605214536,
1292
+ "loss": 0.6279,
1293
+ "step": 214
1294
+ },
1295
+ {
1296
+ "epoch": 0.64,
1297
+ "learning_rate": 0.00019385093367406254,
1298
+ "loss": 0.7328,
1299
+ "step": 215
1300
+ },
1301
+ {
1302
+ "epoch": 0.64,
1303
+ "learning_rate": 0.00019101745960721896,
1304
+ "loss": 0.6726,
1305
+ "step": 216
1306
+ },
1307
+ {
1308
+ "epoch": 0.65,
1309
+ "learning_rate": 0.00018819620221079117,
1310
+ "loss": 0.6294,
1311
+ "step": 217
1312
+ },
1313
+ {
1314
+ "epoch": 0.65,
1315
+ "learning_rate": 0.00018538741873002672,
1316
+ "loss": 0.7579,
1317
+ "step": 218
1318
+ },
1319
+ {
1320
+ "epoch": 0.65,
1321
+ "learning_rate": 0.0001825913652727883,
1322
+ "loss": 0.6927,
1323
+ "step": 219
1324
+ },
1325
+ {
1326
+ "epoch": 0.65,
1327
+ "learning_rate": 0.0001798082967862013,
1328
+ "loss": 0.7399,
1329
+ "step": 220
1330
+ },
1331
+ {
1332
+ "epoch": 0.66,
1333
+ "learning_rate": 0.00017703846703340817,
1334
+ "loss": 0.7435,
1335
+ "step": 221
1336
+ },
1337
+ {
1338
+ "epoch": 0.66,
1339
+ "learning_rate": 0.000174282128570429,
1340
+ "loss": 0.6239,
1341
+ "step": 222
1342
+ },
1343
+ {
1344
+ "epoch": 0.66,
1345
+ "learning_rate": 0.0001715395327231343,
1346
+ "loss": 0.6879,
1347
+ "step": 223
1348
+ },
1349
+ {
1350
+ "epoch": 0.67,
1351
+ "learning_rate": 0.00016881092956432775,
1352
+ "loss": 0.6886,
1353
+ "step": 224
1354
+ },
1355
+ {
1356
+ "epoch": 0.67,
1357
+ "learning_rate": 0.00016609656789094506,
1358
+ "loss": 0.7171,
1359
+ "step": 225
1360
+ },
1361
+ {
1362
+ "epoch": 0.67,
1363
+ "learning_rate": 0.00016339669520136827,
1364
+ "loss": 0.673,
1365
+ "step": 226
1366
+ },
1367
+ {
1368
+ "epoch": 0.68,
1369
+ "learning_rate": 0.00016071155767285826,
1370
+ "loss": 0.6988,
1371
+ "step": 227
1372
+ },
1373
+ {
1374
+ "epoch": 0.68,
1375
+ "learning_rate": 0.0001580414001391084,
1376
+ "loss": 0.7076,
1377
+ "step": 228
1378
+ },
1379
+ {
1380
+ "epoch": 0.68,
1381
+ "learning_rate": 0.00015538646606792005,
1382
+ "loss": 0.7,
1383
+ "step": 229
1384
+ },
1385
+ {
1386
+ "epoch": 0.68,
1387
+ "learning_rate": 0.00015274699753900343,
1388
+ "loss": 0.7011,
1389
+ "step": 230
1390
+ },
1391
+ {
1392
+ "epoch": 0.69,
1393
+ "learning_rate": 0.00015012323522190388,
1394
+ "loss": 0.68,
1395
+ "step": 231
1396
+ },
1397
+ {
1398
+ "epoch": 0.69,
1399
+ "learning_rate": 0.00014751541835405772,
1400
+ "loss": 0.6937,
1401
+ "step": 232
1402
+ },
1403
+ {
1404
+ "epoch": 0.69,
1405
+ "learning_rate": 0.00014492378471897817,
1406
+ "loss": 0.6418,
1407
+ "step": 233
1408
+ },
1409
+ {
1410
+ "epoch": 0.7,
1411
+ "learning_rate": 0.0001423485706245744,
1412
+ "loss": 0.7231,
1413
+ "step": 234
1414
+ },
1415
+ {
1416
+ "epoch": 0.7,
1417
+ "learning_rate": 0.00013979001088160417,
1418
+ "loss": 0.6351,
1419
+ "step": 235
1420
+ },
1421
+ {
1422
+ "epoch": 0.7,
1423
+ "learning_rate": 0.0001372483387822637,
1424
+ "loss": 0.7035,
1425
+ "step": 236
1426
+ },
1427
+ {
1428
+ "epoch": 0.7,
1429
+ "learning_rate": 0.00013472378607891597,
1430
+ "loss": 0.6478,
1431
+ "step": 237
1432
+ },
1433
+ {
1434
+ "epoch": 0.71,
1435
+ "learning_rate": 0.00013221658296295956,
1436
+ "loss": 0.7228,
1437
+ "step": 238
1438
+ },
1439
+ {
1440
+ "epoch": 0.71,
1441
+ "learning_rate": 0.00012972695804383885,
1442
+ "loss": 0.7148,
1443
+ "step": 239
1444
+ },
1445
+ {
1446
+ "epoch": 0.71,
1447
+ "learning_rate": 0.00012725513832819964,
1448
+ "loss": 0.7167,
1449
+ "step": 240
1450
+ },
1451
+ {
1452
+ "epoch": 0.72,
1453
+ "learning_rate": 0.00012480134919919038,
1454
+ "loss": 0.7208,
1455
+ "step": 241
1456
+ },
1457
+ {
1458
+ "epoch": 0.72,
1459
+ "learning_rate": 0.00012236581439591161,
1460
+ "loss": 0.6406,
1461
+ "step": 242
1462
+ },
1463
+ {
1464
+ "epoch": 0.72,
1465
+ "learning_rate": 0.00011994875599301488,
1466
+ "loss": 0.6974,
1467
+ "step": 243
1468
+ },
1469
+ {
1470
+ "epoch": 0.73,
1471
+ "learning_rate": 0.00011755039438045392,
1472
+ "loss": 0.6357,
1473
+ "step": 244
1474
+ },
1475
+ {
1476
+ "epoch": 0.73,
1477
+ "learning_rate": 0.0001151709482433892,
1478
+ "loss": 0.7179,
1479
+ "step": 245
1480
+ },
1481
+ {
1482
+ "epoch": 0.73,
1483
+ "learning_rate": 0.00011281063454224838,
1484
+ "loss": 0.7553,
1485
+ "step": 246
1486
+ },
1487
+ {
1488
+ "epoch": 0.73,
1489
+ "learning_rate": 0.00011046966849294289,
1490
+ "loss": 0.7019,
1491
+ "step": 247
1492
+ },
1493
+ {
1494
+ "epoch": 0.74,
1495
+ "learning_rate": 0.00010814826354724483,
1496
+ "loss": 0.6608,
1497
+ "step": 248
1498
+ },
1499
+ {
1500
+ "epoch": 0.74,
1501
+ "learning_rate": 0.00010584663137332396,
1502
+ "loss": 0.6674,
1503
+ "step": 249
1504
+ },
1505
+ {
1506
+ "epoch": 0.74,
1507
+ "learning_rate": 0.00010356498183644816,
1508
+ "loss": 0.7079,
1509
+ "step": 250
1510
+ }
1511
+ ],
1512
+ "logging_steps": 1,
1513
+ "max_steps": 336,
1514
+ "num_input_tokens_seen": 0,
1515
+ "num_train_epochs": 1,
1516
+ "save_steps": 50,
1517
+ "total_flos": 3.167057329693655e+17,
1518
+ "train_batch_size": 6,
1519
+ "trial_name": null,
1520
+ "trial_params": null
1521
+ }
checkpoint-250/training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:3a720d2daf4afe192195297ee83d1a8834602d7e8eda493eaba5ee762dd57a90
3
+ size 4664
checkpoint-300/README.md ADDED
@@ -0,0 +1,204 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ library_name: peft
3
+ base_model: unsloth/llama-2-7b
4
+ ---
5
+
6
+ # Model Card for Model ID
7
+
8
+ <!-- Provide a quick summary of what the model is/does. -->
9
+
10
+
11
+
12
+ ## Model Details
13
+
14
+ ### Model Description
15
+
16
+ <!-- Provide a longer summary of what this model is. -->
17
+
18
+
19
+
20
+ - **Developed by:** [More Information Needed]
21
+ - **Funded by [optional]:** [More Information Needed]
22
+ - **Shared by [optional]:** [More Information Needed]
23
+ - **Model type:** [More Information Needed]
24
+ - **Language(s) (NLP):** [More Information Needed]
25
+ - **License:** [More Information Needed]
26
+ - **Finetuned from model [optional]:** [More Information Needed]
27
+
28
+ ### Model Sources [optional]
29
+
30
+ <!-- Provide the basic links for the model. -->
31
+
32
+ - **Repository:** [More Information Needed]
33
+ - **Paper [optional]:** [More Information Needed]
34
+ - **Demo [optional]:** [More Information Needed]
35
+
36
+ ## Uses
37
+
38
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
39
+
40
+ ### Direct Use
41
+
42
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
43
+
44
+ [More Information Needed]
45
+
46
+ ### Downstream Use [optional]
47
+
48
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
49
+
50
+ [More Information Needed]
51
+
52
+ ### Out-of-Scope Use
53
+
54
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
55
+
56
+ [More Information Needed]
57
+
58
+ ## Bias, Risks, and Limitations
59
+
60
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
61
+
62
+ [More Information Needed]
63
+
64
+ ### Recommendations
65
+
66
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
67
+
68
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
69
+
70
+ ## How to Get Started with the Model
71
+
72
+ Use the code below to get started with the model.
73
+
74
+ [More Information Needed]
75
+
76
+ ## Training Details
77
+
78
+ ### Training Data
79
+
80
+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
81
+
82
+ [More Information Needed]
83
+
84
+ ### Training Procedure
85
+
86
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
87
+
88
+ #### Preprocessing [optional]
89
+
90
+ [More Information Needed]
91
+
92
+
93
+ #### Training Hyperparameters
94
+
95
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
96
+
97
+ #### Speeds, Sizes, Times [optional]
98
+
99
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
100
+
101
+ [More Information Needed]
102
+
103
+ ## Evaluation
104
+
105
+ <!-- This section describes the evaluation protocols and provides the results. -->
106
+
107
+ ### Testing Data, Factors & Metrics
108
+
109
+ #### Testing Data
110
+
111
+ <!-- This should link to a Dataset Card if possible. -->
112
+
113
+ [More Information Needed]
114
+
115
+ #### Factors
116
+
117
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
118
+
119
+ [More Information Needed]
120
+
121
+ #### Metrics
122
+
123
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
124
+
125
+ [More Information Needed]
126
+
127
+ ### Results
128
+
129
+ [More Information Needed]
130
+
131
+ #### Summary
132
+
133
+
134
+
135
+ ## Model Examination [optional]
136
+
137
+ <!-- Relevant interpretability work for the model goes here -->
138
+
139
+ [More Information Needed]
140
+
141
+ ## Environmental Impact
142
+
143
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
144
+
145
+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
146
+
147
+ - **Hardware Type:** [More Information Needed]
148
+ - **Hours used:** [More Information Needed]
149
+ - **Cloud Provider:** [More Information Needed]
150
+ - **Compute Region:** [More Information Needed]
151
+ - **Carbon Emitted:** [More Information Needed]
152
+
153
+ ## Technical Specifications [optional]
154
+
155
+ ### Model Architecture and Objective
156
+
157
+ [More Information Needed]
158
+
159
+ ### Compute Infrastructure
160
+
161
+ [More Information Needed]
162
+
163
+ #### Hardware
164
+
165
+ [More Information Needed]
166
+
167
+ #### Software
168
+
169
+ [More Information Needed]
170
+
171
+ ## Citation [optional]
172
+
173
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
174
+
175
+ **BibTeX:**
176
+
177
+ [More Information Needed]
178
+
179
+ **APA:**
180
+
181
+ [More Information Needed]
182
+
183
+ ## Glossary [optional]
184
+
185
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
186
+
187
+ [More Information Needed]
188
+
189
+ ## More Information [optional]
190
+
191
+ [More Information Needed]
192
+
193
+ ## Model Card Authors [optional]
194
+
195
+ [More Information Needed]
196
+
197
+ ## Model Card Contact
198
+
199
+ [More Information Needed]
200
+
201
+
202
+ ### Framework versions
203
+
204
+ - PEFT 0.7.1
checkpoint-300/adapter_config.json ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alpha_pattern": {},
3
+ "auto_mapping": null,
4
+ "base_model_name_or_path": "unsloth/llama-2-7b",
5
+ "bias": "none",
6
+ "fan_in_fan_out": false,
7
+ "inference_mode": true,
8
+ "init_lora_weights": true,
9
+ "layers_pattern": null,
10
+ "layers_to_transform": null,
11
+ "loftq_config": {},
12
+ "lora_alpha": 16,
13
+ "lora_dropout": 0,
14
+ "megatron_config": null,
15
+ "megatron_core": "megatron.core",
16
+ "modules_to_save": null,
17
+ "peft_type": "LORA",
18
+ "r": 128,
19
+ "rank_pattern": {},
20
+ "revision": "unsloth",
21
+ "target_modules": [
22
+ "up_proj",
23
+ "k_proj",
24
+ "v_proj",
25
+ "o_proj",
26
+ "q_proj",
27
+ "down_proj",
28
+ "gate_proj"
29
+ ],
30
+ "task_type": "CAUSAL_LM"
31
+ }
checkpoint-300/adapter_model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:4c809fe604c5ae97d974767dda335f3ea372fdca58c12366f2769e816310a113
3
+ size 1279323952
checkpoint-300/optimizer.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:27c86489d96393a70bef702e90ec9dee84e0e5d92112addd5a166db9288c77e5
3
+ size 641408020
checkpoint-300/rng_state.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:fe2e145b09b2faab2c45440a9233e2700f8dca5428319c1eb306332f174e4af7
3
+ size 14244
checkpoint-300/scheduler.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:bbca4e04e25e9d7a2781746d7dc3ce8ea70de6a415db28047f24c4620852475c
3
+ size 1064
checkpoint-300/trainer_state.json ADDED
@@ -0,0 +1,1821 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "best_metric": null,
3
+ "best_model_checkpoint": null,
4
+ "epoch": 0.892325996430696,
5
+ "eval_steps": 500,
6
+ "global_step": 300,
7
+ "is_hyper_param_search": false,
8
+ "is_local_process_zero": true,
9
+ "is_world_process_zero": true,
10
+ "log_history": [
11
+ {
12
+ "epoch": 0.0,
13
+ "learning_rate": 9.285714285714285e-05,
14
+ "loss": 2.4427,
15
+ "step": 1
16
+ },
17
+ {
18
+ "epoch": 0.01,
19
+ "learning_rate": 0.0001857142857142857,
20
+ "loss": 2.3973,
21
+ "step": 2
22
+ },
23
+ {
24
+ "epoch": 0.01,
25
+ "learning_rate": 0.00027857142857142854,
26
+ "loss": 2.341,
27
+ "step": 3
28
+ },
29
+ {
30
+ "epoch": 0.01,
31
+ "learning_rate": 0.0003714285714285714,
32
+ "loss": 2.1281,
33
+ "step": 4
34
+ },
35
+ {
36
+ "epoch": 0.01,
37
+ "learning_rate": 0.0004642857142857143,
38
+ "loss": 1.4346,
39
+ "step": 5
40
+ },
41
+ {
42
+ "epoch": 0.02,
43
+ "learning_rate": 0.0005571428571428571,
44
+ "loss": 1.1715,
45
+ "step": 6
46
+ },
47
+ {
48
+ "epoch": 0.02,
49
+ "learning_rate": 0.00065,
50
+ "loss": 1.086,
51
+ "step": 7
52
+ },
53
+ {
54
+ "epoch": 0.02,
55
+ "learning_rate": 0.0006499851830773117,
56
+ "loss": 0.9921,
57
+ "step": 8
58
+ },
59
+ {
60
+ "epoch": 0.03,
61
+ "learning_rate": 0.00064994073366027,
62
+ "loss": 0.9231,
63
+ "step": 9
64
+ },
65
+ {
66
+ "epoch": 0.03,
67
+ "learning_rate": 0.0006498666558018197,
68
+ "loss": 0.9343,
69
+ "step": 10
70
+ },
71
+ {
72
+ "epoch": 0.03,
73
+ "learning_rate": 0.0006497629562564588,
74
+ "loss": 0.9191,
75
+ "step": 11
76
+ },
77
+ {
78
+ "epoch": 0.04,
79
+ "learning_rate": 0.0006496296444796219,
80
+ "loss": 0.8791,
81
+ "step": 12
82
+ },
83
+ {
84
+ "epoch": 0.04,
85
+ "learning_rate": 0.0006494667326268186,
86
+ "loss": 0.8632,
87
+ "step": 13
88
+ },
89
+ {
90
+ "epoch": 0.04,
91
+ "learning_rate": 0.0006492742355525248,
92
+ "loss": 0.9267,
93
+ "step": 14
94
+ },
95
+ {
96
+ "epoch": 0.04,
97
+ "learning_rate": 0.0006490521708088281,
98
+ "loss": 0.8644,
99
+ "step": 15
100
+ },
101
+ {
102
+ "epoch": 0.05,
103
+ "learning_rate": 0.000648800558643828,
104
+ "loss": 0.8353,
105
+ "step": 16
106
+ },
107
+ {
108
+ "epoch": 0.05,
109
+ "learning_rate": 0.0006485194219997891,
110
+ "loss": 0.8482,
111
+ "step": 17
112
+ },
113
+ {
114
+ "epoch": 0.05,
115
+ "learning_rate": 0.0006482087865110493,
116
+ "loss": 0.8587,
117
+ "step": 18
118
+ },
119
+ {
120
+ "epoch": 0.06,
121
+ "learning_rate": 0.0006478686805016826,
122
+ "loss": 0.9134,
123
+ "step": 19
124
+ },
125
+ {
126
+ "epoch": 0.06,
127
+ "learning_rate": 0.0006474991349829163,
128
+ "loss": 0.8238,
129
+ "step": 20
130
+ },
131
+ {
132
+ "epoch": 0.06,
133
+ "learning_rate": 0.0006471001836503035,
134
+ "loss": 0.8329,
135
+ "step": 21
136
+ },
137
+ {
138
+ "epoch": 0.07,
139
+ "learning_rate": 0.0006466718628806508,
140
+ "loss": 0.7995,
141
+ "step": 22
142
+ },
143
+ {
144
+ "epoch": 0.07,
145
+ "learning_rate": 0.0006462142117287011,
146
+ "loss": 0.8363,
147
+ "step": 23
148
+ },
149
+ {
150
+ "epoch": 0.07,
151
+ "learning_rate": 0.0006457272719235728,
152
+ "loss": 0.7942,
153
+ "step": 24
154
+ },
155
+ {
156
+ "epoch": 0.07,
157
+ "learning_rate": 0.0006452110878649547,
158
+ "loss": 0.858,
159
+ "step": 25
160
+ },
161
+ {
162
+ "epoch": 0.08,
163
+ "learning_rate": 0.0006446657066190579,
164
+ "loss": 0.8474,
165
+ "step": 26
166
+ },
167
+ {
168
+ "epoch": 0.08,
169
+ "learning_rate": 0.000644091177914324,
170
+ "loss": 0.8175,
171
+ "step": 27
172
+ },
173
+ {
174
+ "epoch": 0.08,
175
+ "learning_rate": 0.0006434875541368907,
176
+ "loss": 0.7821,
177
+ "step": 28
178
+ },
179
+ {
180
+ "epoch": 0.09,
181
+ "learning_rate": 0.0006428548903258156,
182
+ "loss": 0.8583,
183
+ "step": 29
184
+ },
185
+ {
186
+ "epoch": 0.09,
187
+ "learning_rate": 0.0006421932441680574,
188
+ "loss": 0.8071,
189
+ "step": 30
190
+ },
191
+ {
192
+ "epoch": 0.09,
193
+ "learning_rate": 0.0006415026759932158,
194
+ "loss": 0.805,
195
+ "step": 31
196
+ },
197
+ {
198
+ "epoch": 0.1,
199
+ "learning_rate": 0.0006407832487680309,
200
+ "loss": 0.881,
201
+ "step": 32
202
+ },
203
+ {
204
+ "epoch": 0.1,
205
+ "learning_rate": 0.0006400350280906415,
206
+ "loss": 0.8302,
207
+ "step": 33
208
+ },
209
+ {
210
+ "epoch": 0.1,
211
+ "learning_rate": 0.0006392580821846041,
212
+ "loss": 0.8456,
213
+ "step": 34
214
+ },
215
+ {
216
+ "epoch": 0.1,
217
+ "learning_rate": 0.0006384524818926723,
218
+ "loss": 0.8067,
219
+ "step": 35
220
+ },
221
+ {
222
+ "epoch": 0.11,
223
+ "learning_rate": 0.0006376183006703367,
224
+ "loss": 0.8307,
225
+ "step": 36
226
+ },
227
+ {
228
+ "epoch": 0.11,
229
+ "learning_rate": 0.0006367556145791275,
230
+ "loss": 0.8347,
231
+ "step": 37
232
+ },
233
+ {
234
+ "epoch": 0.11,
235
+ "learning_rate": 0.0006358645022796795,
236
+ "loss": 0.8086,
237
+ "step": 38
238
+ },
239
+ {
240
+ "epoch": 0.12,
241
+ "learning_rate": 0.0006349450450245589,
242
+ "loss": 0.8726,
243
+ "step": 39
244
+ },
245
+ {
246
+ "epoch": 0.12,
247
+ "learning_rate": 0.0006339973266508556,
248
+ "loss": 0.78,
249
+ "step": 40
250
+ },
251
+ {
252
+ "epoch": 0.12,
253
+ "learning_rate": 0.0006330214335725379,
254
+ "loss": 0.7902,
255
+ "step": 41
256
+ },
257
+ {
258
+ "epoch": 0.12,
259
+ "learning_rate": 0.0006320174547725736,
260
+ "loss": 0.7823,
261
+ "step": 42
262
+ },
263
+ {
264
+ "epoch": 0.13,
265
+ "learning_rate": 0.0006309854817948169,
266
+ "loss": 0.8211,
267
+ "step": 43
268
+ },
269
+ {
270
+ "epoch": 0.13,
271
+ "learning_rate": 0.0006299256087356603,
272
+ "loss": 0.8656,
273
+ "step": 44
274
+ },
275
+ {
276
+ "epoch": 0.13,
277
+ "learning_rate": 0.000628837932235456,
278
+ "loss": 0.7767,
279
+ "step": 45
280
+ },
281
+ {
282
+ "epoch": 0.14,
283
+ "learning_rate": 0.0006277225514697028,
284
+ "loss": 0.7542,
285
+ "step": 46
286
+ },
287
+ {
288
+ "epoch": 0.14,
289
+ "learning_rate": 0.0006265795681400046,
290
+ "loss": 0.8254,
291
+ "step": 47
292
+ },
293
+ {
294
+ "epoch": 0.14,
295
+ "learning_rate": 0.0006254090864647957,
296
+ "loss": 0.8099,
297
+ "step": 48
298
+ },
299
+ {
300
+ "epoch": 0.15,
301
+ "learning_rate": 0.0006242112131698394,
302
+ "loss": 0.7786,
303
+ "step": 49
304
+ },
305
+ {
306
+ "epoch": 0.15,
307
+ "learning_rate": 0.0006229860574784954,
308
+ "loss": 0.7895,
309
+ "step": 50
310
+ },
311
+ {
312
+ "epoch": 0.15,
313
+ "learning_rate": 0.0006217337311017619,
314
+ "loss": 0.8092,
315
+ "step": 51
316
+ },
317
+ {
318
+ "epoch": 0.15,
319
+ "learning_rate": 0.0006204543482280886,
320
+ "loss": 0.7998,
321
+ "step": 52
322
+ },
323
+ {
324
+ "epoch": 0.16,
325
+ "learning_rate": 0.0006191480255129656,
326
+ "loss": 0.802,
327
+ "step": 53
328
+ },
329
+ {
330
+ "epoch": 0.16,
331
+ "learning_rate": 0.0006178148820682862,
332
+ "loss": 0.7691,
333
+ "step": 54
334
+ },
335
+ {
336
+ "epoch": 0.16,
337
+ "learning_rate": 0.0006164550394514865,
338
+ "loss": 0.8572,
339
+ "step": 55
340
+ },
341
+ {
342
+ "epoch": 0.17,
343
+ "learning_rate": 0.0006150686216544614,
344
+ "loss": 0.7915,
345
+ "step": 56
346
+ },
347
+ {
348
+ "epoch": 0.17,
349
+ "learning_rate": 0.0006136557550922589,
350
+ "loss": 0.759,
351
+ "step": 57
352
+ },
353
+ {
354
+ "epoch": 0.17,
355
+ "learning_rate": 0.0006122165685915537,
356
+ "loss": 0.7282,
357
+ "step": 58
358
+ },
359
+ {
360
+ "epoch": 0.18,
361
+ "learning_rate": 0.0006107511933789002,
362
+ "loss": 0.768,
363
+ "step": 59
364
+ },
365
+ {
366
+ "epoch": 0.18,
367
+ "learning_rate": 0.0006092597630687677,
368
+ "loss": 0.7868,
369
+ "step": 60
370
+ },
371
+ {
372
+ "epoch": 0.18,
373
+ "learning_rate": 0.0006077424136513567,
374
+ "loss": 0.7827,
375
+ "step": 61
376
+ },
377
+ {
378
+ "epoch": 0.18,
379
+ "learning_rate": 0.0006061992834801996,
380
+ "loss": 0.7755,
381
+ "step": 62
382
+ },
383
+ {
384
+ "epoch": 0.19,
385
+ "learning_rate": 0.0006046305132595453,
386
+ "loss": 0.8332,
387
+ "step": 63
388
+ },
389
+ {
390
+ "epoch": 0.19,
391
+ "learning_rate": 0.0006030362460315296,
392
+ "loss": 0.7241,
393
+ "step": 64
394
+ },
395
+ {
396
+ "epoch": 0.19,
397
+ "learning_rate": 0.0006014166271631326,
398
+ "loss": 0.8074,
399
+ "step": 65
400
+ },
401
+ {
402
+ "epoch": 0.2,
403
+ "learning_rate": 0.000599771804332924,
404
+ "loss": 0.7491,
405
+ "step": 66
406
+ },
407
+ {
408
+ "epoch": 0.2,
409
+ "learning_rate": 0.0005981019275175972,
410
+ "loss": 0.8004,
411
+ "step": 67
412
+ },
413
+ {
414
+ "epoch": 0.2,
415
+ "learning_rate": 0.000596407148978295,
416
+ "loss": 0.7442,
417
+ "step": 68
418
+ },
419
+ {
420
+ "epoch": 0.21,
421
+ "learning_rate": 0.0005946876232467254,
422
+ "loss": 0.6956,
423
+ "step": 69
424
+ },
425
+ {
426
+ "epoch": 0.21,
427
+ "learning_rate": 0.0005929435071110721,
428
+ "loss": 0.7472,
429
+ "step": 70
430
+ },
431
+ {
432
+ "epoch": 0.21,
433
+ "learning_rate": 0.0005911749596016978,
434
+ "loss": 0.8097,
435
+ "step": 71
436
+ },
437
+ {
438
+ "epoch": 0.21,
439
+ "learning_rate": 0.0005893821419766438,
440
+ "loss": 0.8002,
441
+ "step": 72
442
+ },
443
+ {
444
+ "epoch": 0.22,
445
+ "learning_rate": 0.0005875652177069265,
446
+ "loss": 0.8203,
447
+ "step": 73
448
+ },
449
+ {
450
+ "epoch": 0.22,
451
+ "learning_rate": 0.0005857243524616315,
452
+ "loss": 0.7339,
453
+ "step": 74
454
+ },
455
+ {
456
+ "epoch": 0.22,
457
+ "learning_rate": 0.0005838597140928082,
458
+ "loss": 0.6844,
459
+ "step": 75
460
+ },
461
+ {
462
+ "epoch": 0.23,
463
+ "learning_rate": 0.0005819714726201646,
464
+ "loss": 0.7211,
465
+ "step": 76
466
+ },
467
+ {
468
+ "epoch": 0.23,
469
+ "learning_rate": 0.0005800598002155648,
470
+ "loss": 0.8289,
471
+ "step": 77
472
+ },
473
+ {
474
+ "epoch": 0.23,
475
+ "learning_rate": 0.0005781248711873302,
476
+ "loss": 0.7686,
477
+ "step": 78
478
+ },
479
+ {
480
+ "epoch": 0.23,
481
+ "learning_rate": 0.0005761668619643458,
482
+ "loss": 0.7618,
483
+ "step": 79
484
+ },
485
+ {
486
+ "epoch": 0.24,
487
+ "learning_rate": 0.0005741859510799734,
488
+ "loss": 0.779,
489
+ "step": 80
490
+ },
491
+ {
492
+ "epoch": 0.24,
493
+ "learning_rate": 0.0005721823191557725,
494
+ "loss": 0.7599,
495
+ "step": 81
496
+ },
497
+ {
498
+ "epoch": 0.24,
499
+ "learning_rate": 0.0005701561488850312,
500
+ "loss": 0.7495,
501
+ "step": 82
502
+ },
503
+ {
504
+ "epoch": 0.25,
505
+ "learning_rate": 0.000568107625016108,
506
+ "loss": 0.6977,
507
+ "step": 83
508
+ },
509
+ {
510
+ "epoch": 0.25,
511
+ "learning_rate": 0.0005660369343355862,
512
+ "loss": 0.757,
513
+ "step": 84
514
+ },
515
+ {
516
+ "epoch": 0.25,
517
+ "learning_rate": 0.0005639442656512426,
518
+ "loss": 0.8295,
519
+ "step": 85
520
+ },
521
+ {
522
+ "epoch": 0.26,
523
+ "learning_rate": 0.0005618298097748316,
524
+ "loss": 0.7394,
525
+ "step": 86
526
+ },
527
+ {
528
+ "epoch": 0.26,
529
+ "learning_rate": 0.0005596937595046872,
530
+ "loss": 0.7728,
531
+ "step": 87
532
+ },
533
+ {
534
+ "epoch": 0.26,
535
+ "learning_rate": 0.0005575363096081429,
536
+ "loss": 0.7721,
537
+ "step": 88
538
+ },
539
+ {
540
+ "epoch": 0.26,
541
+ "learning_rate": 0.0005553576568037731,
542
+ "loss": 0.7191,
543
+ "step": 89
544
+ },
545
+ {
546
+ "epoch": 0.27,
547
+ "learning_rate": 0.0005531579997434555,
548
+ "loss": 0.7594,
549
+ "step": 90
550
+ },
551
+ {
552
+ "epoch": 0.27,
553
+ "learning_rate": 0.0005509375389942588,
554
+ "loss": 0.7511,
555
+ "step": 91
556
+ },
557
+ {
558
+ "epoch": 0.27,
559
+ "learning_rate": 0.0005486964770201533,
560
+ "loss": 0.7644,
561
+ "step": 92
562
+ },
563
+ {
564
+ "epoch": 0.28,
565
+ "learning_rate": 0.0005464350181635519,
566
+ "loss": 0.7403,
567
+ "step": 93
568
+ },
569
+ {
570
+ "epoch": 0.28,
571
+ "learning_rate": 0.000544153368626676,
572
+ "loss": 0.7204,
573
+ "step": 94
574
+ },
575
+ {
576
+ "epoch": 0.28,
577
+ "learning_rate": 0.0005418517364527552,
578
+ "loss": 0.7358,
579
+ "step": 95
580
+ },
581
+ {
582
+ "epoch": 0.29,
583
+ "learning_rate": 0.0005395303315070571,
584
+ "loss": 0.7607,
585
+ "step": 96
586
+ },
587
+ {
588
+ "epoch": 0.29,
589
+ "learning_rate": 0.0005371893654577517,
590
+ "loss": 0.7505,
591
+ "step": 97
592
+ },
593
+ {
594
+ "epoch": 0.29,
595
+ "learning_rate": 0.0005348290517566107,
596
+ "loss": 0.7274,
597
+ "step": 98
598
+ },
599
+ {
600
+ "epoch": 0.29,
601
+ "learning_rate": 0.0005324496056195461,
602
+ "loss": 0.8161,
603
+ "step": 99
604
+ },
605
+ {
606
+ "epoch": 0.3,
607
+ "learning_rate": 0.0005300512440069852,
608
+ "loss": 0.7043,
609
+ "step": 100
610
+ },
611
+ {
612
+ "epoch": 0.3,
613
+ "learning_rate": 0.0005276341856040884,
614
+ "loss": 0.7921,
615
+ "step": 101
616
+ },
617
+ {
618
+ "epoch": 0.3,
619
+ "learning_rate": 0.0005251986508008097,
620
+ "loss": 0.6852,
621
+ "step": 102
622
+ },
623
+ {
624
+ "epoch": 0.31,
625
+ "learning_rate": 0.0005227448616718004,
626
+ "loss": 0.6996,
627
+ "step": 103
628
+ },
629
+ {
630
+ "epoch": 0.31,
631
+ "learning_rate": 0.0005202730419561611,
632
+ "loss": 0.6323,
633
+ "step": 104
634
+ },
635
+ {
636
+ "epoch": 0.31,
637
+ "learning_rate": 0.0005177834170370404,
638
+ "loss": 0.7601,
639
+ "step": 105
640
+ },
641
+ {
642
+ "epoch": 0.32,
643
+ "learning_rate": 0.0005152762139210839,
644
+ "loss": 0.7499,
645
+ "step": 106
646
+ },
647
+ {
648
+ "epoch": 0.32,
649
+ "learning_rate": 0.0005127516612177365,
650
+ "loss": 0.73,
651
+ "step": 107
652
+ },
653
+ {
654
+ "epoch": 0.32,
655
+ "learning_rate": 0.0005102099891183958,
656
+ "loss": 0.7438,
657
+ "step": 108
658
+ },
659
+ {
660
+ "epoch": 0.32,
661
+ "learning_rate": 0.0005076514293754255,
662
+ "loss": 0.6614,
663
+ "step": 109
664
+ },
665
+ {
666
+ "epoch": 0.33,
667
+ "learning_rate": 0.0005050762152810218,
668
+ "loss": 0.7599,
669
+ "step": 110
670
+ },
671
+ {
672
+ "epoch": 0.33,
673
+ "learning_rate": 0.0005024845816459423,
674
+ "loss": 0.7471,
675
+ "step": 111
676
+ },
677
+ {
678
+ "epoch": 0.33,
679
+ "learning_rate": 0.0004998767647780961,
680
+ "loss": 0.7569,
681
+ "step": 112
682
+ },
683
+ {
684
+ "epoch": 0.34,
685
+ "learning_rate": 0.0004972530024609966,
686
+ "loss": 0.6561,
687
+ "step": 113
688
+ },
689
+ {
690
+ "epoch": 0.34,
691
+ "learning_rate": 0.0004946135339320798,
692
+ "loss": 0.7369,
693
+ "step": 114
694
+ },
695
+ {
696
+ "epoch": 0.34,
697
+ "learning_rate": 0.0004919585998608917,
698
+ "loss": 0.755,
699
+ "step": 115
700
+ },
701
+ {
702
+ "epoch": 0.35,
703
+ "learning_rate": 0.0004892884423271417,
704
+ "loss": 0.7307,
705
+ "step": 116
706
+ },
707
+ {
708
+ "epoch": 0.35,
709
+ "learning_rate": 0.0004866033047986317,
710
+ "loss": 0.7321,
711
+ "step": 117
712
+ },
713
+ {
714
+ "epoch": 0.35,
715
+ "learning_rate": 0.00048390343210905486,
716
+ "loss": 0.772,
717
+ "step": 118
718
+ },
719
+ {
720
+ "epoch": 0.35,
721
+ "learning_rate": 0.0004811890704356722,
722
+ "loss": 0.6707,
723
+ "step": 119
724
+ },
725
+ {
726
+ "epoch": 0.36,
727
+ "learning_rate": 0.0004784604672768657,
728
+ "loss": 0.695,
729
+ "step": 120
730
+ },
731
+ {
732
+ "epoch": 0.36,
733
+ "learning_rate": 0.0004757178714295709,
734
+ "loss": 0.6372,
735
+ "step": 121
736
+ },
737
+ {
738
+ "epoch": 0.36,
739
+ "learning_rate": 0.0004729615329665918,
740
+ "loss": 0.7303,
741
+ "step": 122
742
+ },
743
+ {
744
+ "epoch": 0.37,
745
+ "learning_rate": 0.0004701917032137987,
746
+ "loss": 0.7313,
747
+ "step": 123
748
+ },
749
+ {
750
+ "epoch": 0.37,
751
+ "learning_rate": 0.00046740863472721176,
752
+ "loss": 0.6939,
753
+ "step": 124
754
+ },
755
+ {
756
+ "epoch": 0.37,
757
+ "learning_rate": 0.0004646125812699734,
758
+ "loss": 0.711,
759
+ "step": 125
760
+ },
761
+ {
762
+ "epoch": 0.37,
763
+ "learning_rate": 0.0004618037977892089,
764
+ "loss": 0.7238,
765
+ "step": 126
766
+ },
767
+ {
768
+ "epoch": 0.38,
769
+ "learning_rate": 0.00045898254039278106,
770
+ "loss": 0.7508,
771
+ "step": 127
772
+ },
773
+ {
774
+ "epoch": 0.38,
775
+ "learning_rate": 0.0004561490663259375,
776
+ "loss": 0.7817,
777
+ "step": 128
778
+ },
779
+ {
780
+ "epoch": 0.38,
781
+ "learning_rate": 0.00045330363394785467,
782
+ "loss": 0.7149,
783
+ "step": 129
784
+ },
785
+ {
786
+ "epoch": 0.39,
787
+ "learning_rate": 0.0004504465027080806,
788
+ "loss": 0.7987,
789
+ "step": 130
790
+ },
791
+ {
792
+ "epoch": 0.39,
793
+ "learning_rate": 0.00044757793312287807,
794
+ "loss": 0.7047,
795
+ "step": 131
796
+ },
797
+ {
798
+ "epoch": 0.39,
799
+ "learning_rate": 0.00044469818675147024,
800
+ "loss": 0.7322,
801
+ "step": 132
802
+ },
803
+ {
804
+ "epoch": 0.4,
805
+ "learning_rate": 0.0004418075261721916,
806
+ "loss": 0.71,
807
+ "step": 133
808
+ },
809
+ {
810
+ "epoch": 0.4,
811
+ "learning_rate": 0.0004389062149585456,
812
+ "loss": 0.7306,
813
+ "step": 134
814
+ },
815
+ {
816
+ "epoch": 0.4,
817
+ "learning_rate": 0.0004359945176551721,
818
+ "loss": 0.6989,
819
+ "step": 135
820
+ },
821
+ {
822
+ "epoch": 0.4,
823
+ "learning_rate": 0.00043307269975372513,
824
+ "loss": 0.6898,
825
+ "step": 136
826
+ },
827
+ {
828
+ "epoch": 0.41,
829
+ "learning_rate": 0.0004301410276686663,
830
+ "loss": 0.7431,
831
+ "step": 137
832
+ },
833
+ {
834
+ "epoch": 0.41,
835
+ "learning_rate": 0.00042719976871297155,
836
+ "loss": 0.7236,
837
+ "step": 138
838
+ },
839
+ {
840
+ "epoch": 0.41,
841
+ "learning_rate": 0.0004242491910737582,
842
+ "loss": 0.7704,
843
+ "step": 139
844
+ },
845
+ {
846
+ "epoch": 0.42,
847
+ "learning_rate": 0.0004212895637878311,
848
+ "loss": 0.7125,
849
+ "step": 140
850
+ },
851
+ {
852
+ "epoch": 0.42,
853
+ "learning_rate": 0.00041832115671715107,
854
+ "loss": 0.7869,
855
+ "step": 141
856
+ },
857
+ {
858
+ "epoch": 0.42,
859
+ "learning_rate": 0.00041534424052422966,
860
+ "loss": 0.714,
861
+ "step": 142
862
+ },
863
+ {
864
+ "epoch": 0.43,
865
+ "learning_rate": 0.00041235908664744866,
866
+ "loss": 0.6927,
867
+ "step": 143
868
+ },
869
+ {
870
+ "epoch": 0.43,
871
+ "learning_rate": 0.00040936596727631104,
872
+ "loss": 0.7168,
873
+ "step": 144
874
+ },
875
+ {
876
+ "epoch": 0.43,
877
+ "learning_rate": 0.0004063651553266216,
878
+ "loss": 0.7199,
879
+ "step": 145
880
+ },
881
+ {
882
+ "epoch": 0.43,
883
+ "learning_rate": 0.00040335692441560304,
884
+ "loss": 0.7084,
885
+ "step": 146
886
+ },
887
+ {
888
+ "epoch": 0.44,
889
+ "learning_rate": 0.00040034154883694667,
890
+ "loss": 0.728,
891
+ "step": 147
892
+ },
893
+ {
894
+ "epoch": 0.44,
895
+ "learning_rate": 0.00039731930353580216,
896
+ "loss": 0.7368,
897
+ "step": 148
898
+ },
899
+ {
900
+ "epoch": 0.44,
901
+ "learning_rate": 0.0003942904640837078,
902
+ "loss": 0.7298,
903
+ "step": 149
904
+ },
905
+ {
906
+ "epoch": 0.45,
907
+ "learning_rate": 0.00039125530665346355,
908
+ "loss": 0.73,
909
+ "step": 150
910
+ },
911
+ {
912
+ "epoch": 0.45,
913
+ "learning_rate": 0.00038821410799394935,
914
+ "loss": 0.6635,
915
+ "step": 151
916
+ },
917
+ {
918
+ "epoch": 0.45,
919
+ "learning_rate": 0.0003851671454048909,
920
+ "loss": 0.7228,
921
+ "step": 152
922
+ },
923
+ {
924
+ "epoch": 0.46,
925
+ "learning_rate": 0.00038211469671157496,
926
+ "loss": 0.7276,
927
+ "step": 153
928
+ },
929
+ {
930
+ "epoch": 0.46,
931
+ "learning_rate": 0.00037905704023951726,
932
+ "loss": 0.7386,
933
+ "step": 154
934
+ },
935
+ {
936
+ "epoch": 0.46,
937
+ "learning_rate": 0.0003759944547890843,
938
+ "loss": 0.7577,
939
+ "step": 155
940
+ },
941
+ {
942
+ "epoch": 0.46,
943
+ "learning_rate": 0.0003729272196100721,
944
+ "loss": 0.6462,
945
+ "step": 156
946
+ },
947
+ {
948
+ "epoch": 0.47,
949
+ "learning_rate": 0.0003698556143762437,
950
+ "loss": 0.7328,
951
+ "step": 157
952
+ },
953
+ {
954
+ "epoch": 0.47,
955
+ "learning_rate": 0.0003667799191598287,
956
+ "loss": 0.7096,
957
+ "step": 158
958
+ },
959
+ {
960
+ "epoch": 0.47,
961
+ "learning_rate": 0.00036370041440598517,
962
+ "loss": 0.6856,
963
+ "step": 159
964
+ },
965
+ {
966
+ "epoch": 0.48,
967
+ "learning_rate": 0.0003606173809072294,
968
+ "loss": 0.7114,
969
+ "step": 160
970
+ },
971
+ {
972
+ "epoch": 0.48,
973
+ "learning_rate": 0.000357531099777832,
974
+ "loss": 0.694,
975
+ "step": 161
976
+ },
977
+ {
978
+ "epoch": 0.48,
979
+ "learning_rate": 0.00035444185242818624,
980
+ "loss": 0.7436,
981
+ "step": 162
982
+ },
983
+ {
984
+ "epoch": 0.48,
985
+ "learning_rate": 0.0003513499205391482,
986
+ "loss": 0.727,
987
+ "step": 163
988
+ },
989
+ {
990
+ "epoch": 0.49,
991
+ "learning_rate": 0.00034825558603635346,
992
+ "loss": 0.6991,
993
+ "step": 164
994
+ },
995
+ {
996
+ "epoch": 0.49,
997
+ "learning_rate": 0.0003451591310645103,
998
+ "loss": 0.6891,
999
+ "step": 165
1000
+ },
1001
+ {
1002
+ "epoch": 0.49,
1003
+ "learning_rate": 0.0003420608379616738,
1004
+ "loss": 0.6636,
1005
+ "step": 166
1006
+ },
1007
+ {
1008
+ "epoch": 0.5,
1009
+ "learning_rate": 0.0003389609892335013,
1010
+ "loss": 0.6759,
1011
+ "step": 167
1012
+ },
1013
+ {
1014
+ "epoch": 0.5,
1015
+ "learning_rate": 0.0003358598675274942,
1016
+ "loss": 0.7353,
1017
+ "step": 168
1018
+ },
1019
+ {
1020
+ "epoch": 0.5,
1021
+ "learning_rate": 0.00033275775560722527,
1022
+ "loss": 0.6926,
1023
+ "step": 169
1024
+ },
1025
+ {
1026
+ "epoch": 0.51,
1027
+ "learning_rate": 0.0003296549363265559,
1028
+ "loss": 0.7334,
1029
+ "step": 170
1030
+ },
1031
+ {
1032
+ "epoch": 0.51,
1033
+ "learning_rate": 0.0003265516926038455,
1034
+ "loss": 0.6609,
1035
+ "step": 171
1036
+ },
1037
+ {
1038
+ "epoch": 0.51,
1039
+ "learning_rate": 0.0003234483073961544,
1040
+ "loss": 0.6996,
1041
+ "step": 172
1042
+ },
1043
+ {
1044
+ "epoch": 0.51,
1045
+ "learning_rate": 0.0003203450636734441,
1046
+ "loss": 0.6515,
1047
+ "step": 173
1048
+ },
1049
+ {
1050
+ "epoch": 0.52,
1051
+ "learning_rate": 0.00031724224439277476,
1052
+ "loss": 0.7299,
1053
+ "step": 174
1054
+ },
1055
+ {
1056
+ "epoch": 0.52,
1057
+ "learning_rate": 0.00031414013247250586,
1058
+ "loss": 0.7283,
1059
+ "step": 175
1060
+ },
1061
+ {
1062
+ "epoch": 0.52,
1063
+ "learning_rate": 0.0003110390107664987,
1064
+ "loss": 0.6697,
1065
+ "step": 176
1066
+ },
1067
+ {
1068
+ "epoch": 0.53,
1069
+ "learning_rate": 0.00030793916203832625,
1070
+ "loss": 0.6834,
1071
+ "step": 177
1072
+ },
1073
+ {
1074
+ "epoch": 0.53,
1075
+ "learning_rate": 0.00030484086893548966,
1076
+ "loss": 0.7315,
1077
+ "step": 178
1078
+ },
1079
+ {
1080
+ "epoch": 0.53,
1081
+ "learning_rate": 0.0003017444139636465,
1082
+ "loss": 0.721,
1083
+ "step": 179
1084
+ },
1085
+ {
1086
+ "epoch": 0.54,
1087
+ "learning_rate": 0.0002986500794608518,
1088
+ "loss": 0.6572,
1089
+ "step": 180
1090
+ },
1091
+ {
1092
+ "epoch": 0.54,
1093
+ "learning_rate": 0.0002955581475718138,
1094
+ "loss": 0.7086,
1095
+ "step": 181
1096
+ },
1097
+ {
1098
+ "epoch": 0.54,
1099
+ "learning_rate": 0.000292468900222168,
1100
+ "loss": 0.7413,
1101
+ "step": 182
1102
+ },
1103
+ {
1104
+ "epoch": 0.54,
1105
+ "learning_rate": 0.0002893826190927707,
1106
+ "loss": 0.7398,
1107
+ "step": 183
1108
+ },
1109
+ {
1110
+ "epoch": 0.55,
1111
+ "learning_rate": 0.0002862995855940148,
1112
+ "loss": 0.6878,
1113
+ "step": 184
1114
+ },
1115
+ {
1116
+ "epoch": 0.55,
1117
+ "learning_rate": 0.00028322008084017135,
1118
+ "loss": 0.7316,
1119
+ "step": 185
1120
+ },
1121
+ {
1122
+ "epoch": 0.55,
1123
+ "learning_rate": 0.0002801443856237563,
1124
+ "loss": 0.7612,
1125
+ "step": 186
1126
+ },
1127
+ {
1128
+ "epoch": 0.56,
1129
+ "learning_rate": 0.000277072780389928,
1130
+ "loss": 0.7012,
1131
+ "step": 187
1132
+ },
1133
+ {
1134
+ "epoch": 0.56,
1135
+ "learning_rate": 0.0002740055452109156,
1136
+ "loss": 0.7154,
1137
+ "step": 188
1138
+ },
1139
+ {
1140
+ "epoch": 0.56,
1141
+ "learning_rate": 0.0002709429597604827,
1142
+ "loss": 0.707,
1143
+ "step": 189
1144
+ },
1145
+ {
1146
+ "epoch": 0.57,
1147
+ "learning_rate": 0.0002678853032884251,
1148
+ "loss": 0.6693,
1149
+ "step": 190
1150
+ },
1151
+ {
1152
+ "epoch": 0.57,
1153
+ "learning_rate": 0.0002648328545951092,
1154
+ "loss": 0.7198,
1155
+ "step": 191
1156
+ },
1157
+ {
1158
+ "epoch": 0.57,
1159
+ "learning_rate": 0.0002617858920060506,
1160
+ "loss": 0.6859,
1161
+ "step": 192
1162
+ },
1163
+ {
1164
+ "epoch": 0.57,
1165
+ "learning_rate": 0.0002587446933465364,
1166
+ "loss": 0.7213,
1167
+ "step": 193
1168
+ },
1169
+ {
1170
+ "epoch": 0.58,
1171
+ "learning_rate": 0.00025570953591629226,
1172
+ "loss": 0.7649,
1173
+ "step": 194
1174
+ },
1175
+ {
1176
+ "epoch": 0.58,
1177
+ "learning_rate": 0.0002526806964641978,
1178
+ "loss": 0.6622,
1179
+ "step": 195
1180
+ },
1181
+ {
1182
+ "epoch": 0.58,
1183
+ "learning_rate": 0.0002496584511630533,
1184
+ "loss": 0.6788,
1185
+ "step": 196
1186
+ },
1187
+ {
1188
+ "epoch": 0.59,
1189
+ "learning_rate": 0.000246643075584397,
1190
+ "loss": 0.7441,
1191
+ "step": 197
1192
+ },
1193
+ {
1194
+ "epoch": 0.59,
1195
+ "learning_rate": 0.00024363484467337842,
1196
+ "loss": 0.6831,
1197
+ "step": 198
1198
+ },
1199
+ {
1200
+ "epoch": 0.59,
1201
+ "learning_rate": 0.0002406340327236891,
1202
+ "loss": 0.721,
1203
+ "step": 199
1204
+ },
1205
+ {
1206
+ "epoch": 0.59,
1207
+ "learning_rate": 0.00023764091335255131,
1208
+ "loss": 0.6427,
1209
+ "step": 200
1210
+ },
1211
+ {
1212
+ "epoch": 0.6,
1213
+ "learning_rate": 0.00023465575947577034,
1214
+ "loss": 0.6415,
1215
+ "step": 201
1216
+ },
1217
+ {
1218
+ "epoch": 0.6,
1219
+ "learning_rate": 0.0002316788432828489,
1220
+ "loss": 0.6217,
1221
+ "step": 202
1222
+ },
1223
+ {
1224
+ "epoch": 0.6,
1225
+ "learning_rate": 0.00022871043621216898,
1226
+ "loss": 0.7135,
1227
+ "step": 203
1228
+ },
1229
+ {
1230
+ "epoch": 0.61,
1231
+ "learning_rate": 0.0002257508089262417,
1232
+ "loss": 0.6396,
1233
+ "step": 204
1234
+ },
1235
+ {
1236
+ "epoch": 0.61,
1237
+ "learning_rate": 0.0002228002312870284,
1238
+ "loss": 0.6426,
1239
+ "step": 205
1240
+ },
1241
+ {
1242
+ "epoch": 0.61,
1243
+ "learning_rate": 0.0002198589723313337,
1244
+ "loss": 0.7122,
1245
+ "step": 206
1246
+ },
1247
+ {
1248
+ "epoch": 0.62,
1249
+ "learning_rate": 0.00021692730024627484,
1250
+ "loss": 0.6733,
1251
+ "step": 207
1252
+ },
1253
+ {
1254
+ "epoch": 0.62,
1255
+ "learning_rate": 0.000214005482344828,
1256
+ "loss": 0.6623,
1257
+ "step": 208
1258
+ },
1259
+ {
1260
+ "epoch": 0.62,
1261
+ "learning_rate": 0.00021109378504145427,
1262
+ "loss": 0.6955,
1263
+ "step": 209
1264
+ },
1265
+ {
1266
+ "epoch": 0.62,
1267
+ "learning_rate": 0.00020819247382780837,
1268
+ "loss": 0.7119,
1269
+ "step": 210
1270
+ },
1271
+ {
1272
+ "epoch": 0.63,
1273
+ "learning_rate": 0.0002053018132485298,
1274
+ "loss": 0.7348,
1275
+ "step": 211
1276
+ },
1277
+ {
1278
+ "epoch": 0.63,
1279
+ "learning_rate": 0.00020242206687712196,
1280
+ "loss": 0.6481,
1281
+ "step": 212
1282
+ },
1283
+ {
1284
+ "epoch": 0.63,
1285
+ "learning_rate": 0.00019955349729191941,
1286
+ "loss": 0.7042,
1287
+ "step": 213
1288
+ },
1289
+ {
1290
+ "epoch": 0.64,
1291
+ "learning_rate": 0.00019669636605214536,
1292
+ "loss": 0.6279,
1293
+ "step": 214
1294
+ },
1295
+ {
1296
+ "epoch": 0.64,
1297
+ "learning_rate": 0.00019385093367406254,
1298
+ "loss": 0.7328,
1299
+ "step": 215
1300
+ },
1301
+ {
1302
+ "epoch": 0.64,
1303
+ "learning_rate": 0.00019101745960721896,
1304
+ "loss": 0.6726,
1305
+ "step": 216
1306
+ },
1307
+ {
1308
+ "epoch": 0.65,
1309
+ "learning_rate": 0.00018819620221079117,
1310
+ "loss": 0.6294,
1311
+ "step": 217
1312
+ },
1313
+ {
1314
+ "epoch": 0.65,
1315
+ "learning_rate": 0.00018538741873002672,
1316
+ "loss": 0.7579,
1317
+ "step": 218
1318
+ },
1319
+ {
1320
+ "epoch": 0.65,
1321
+ "learning_rate": 0.0001825913652727883,
1322
+ "loss": 0.6927,
1323
+ "step": 219
1324
+ },
1325
+ {
1326
+ "epoch": 0.65,
1327
+ "learning_rate": 0.0001798082967862013,
1328
+ "loss": 0.7399,
1329
+ "step": 220
1330
+ },
1331
+ {
1332
+ "epoch": 0.66,
1333
+ "learning_rate": 0.00017703846703340817,
1334
+ "loss": 0.7435,
1335
+ "step": 221
1336
+ },
1337
+ {
1338
+ "epoch": 0.66,
1339
+ "learning_rate": 0.000174282128570429,
1340
+ "loss": 0.6239,
1341
+ "step": 222
1342
+ },
1343
+ {
1344
+ "epoch": 0.66,
1345
+ "learning_rate": 0.0001715395327231343,
1346
+ "loss": 0.6879,
1347
+ "step": 223
1348
+ },
1349
+ {
1350
+ "epoch": 0.67,
1351
+ "learning_rate": 0.00016881092956432775,
1352
+ "loss": 0.6886,
1353
+ "step": 224
1354
+ },
1355
+ {
1356
+ "epoch": 0.67,
1357
+ "learning_rate": 0.00016609656789094506,
1358
+ "loss": 0.7171,
1359
+ "step": 225
1360
+ },
1361
+ {
1362
+ "epoch": 0.67,
1363
+ "learning_rate": 0.00016339669520136827,
1364
+ "loss": 0.673,
1365
+ "step": 226
1366
+ },
1367
+ {
1368
+ "epoch": 0.68,
1369
+ "learning_rate": 0.00016071155767285826,
1370
+ "loss": 0.6988,
1371
+ "step": 227
1372
+ },
1373
+ {
1374
+ "epoch": 0.68,
1375
+ "learning_rate": 0.0001580414001391084,
1376
+ "loss": 0.7076,
1377
+ "step": 228
1378
+ },
1379
+ {
1380
+ "epoch": 0.68,
1381
+ "learning_rate": 0.00015538646606792005,
1382
+ "loss": 0.7,
1383
+ "step": 229
1384
+ },
1385
+ {
1386
+ "epoch": 0.68,
1387
+ "learning_rate": 0.00015274699753900343,
1388
+ "loss": 0.7011,
1389
+ "step": 230
1390
+ },
1391
+ {
1392
+ "epoch": 0.69,
1393
+ "learning_rate": 0.00015012323522190388,
1394
+ "loss": 0.68,
1395
+ "step": 231
1396
+ },
1397
+ {
1398
+ "epoch": 0.69,
1399
+ "learning_rate": 0.00014751541835405772,
1400
+ "loss": 0.6937,
1401
+ "step": 232
1402
+ },
1403
+ {
1404
+ "epoch": 0.69,
1405
+ "learning_rate": 0.00014492378471897817,
1406
+ "loss": 0.6418,
1407
+ "step": 233
1408
+ },
1409
+ {
1410
+ "epoch": 0.7,
1411
+ "learning_rate": 0.0001423485706245744,
1412
+ "loss": 0.7231,
1413
+ "step": 234
1414
+ },
1415
+ {
1416
+ "epoch": 0.7,
1417
+ "learning_rate": 0.00013979001088160417,
1418
+ "loss": 0.6351,
1419
+ "step": 235
1420
+ },
1421
+ {
1422
+ "epoch": 0.7,
1423
+ "learning_rate": 0.0001372483387822637,
1424
+ "loss": 0.7035,
1425
+ "step": 236
1426
+ },
1427
+ {
1428
+ "epoch": 0.7,
1429
+ "learning_rate": 0.00013472378607891597,
1430
+ "loss": 0.6478,
1431
+ "step": 237
1432
+ },
1433
+ {
1434
+ "epoch": 0.71,
1435
+ "learning_rate": 0.00013221658296295956,
1436
+ "loss": 0.7228,
1437
+ "step": 238
1438
+ },
1439
+ {
1440
+ "epoch": 0.71,
1441
+ "learning_rate": 0.00012972695804383885,
1442
+ "loss": 0.7148,
1443
+ "step": 239
1444
+ },
1445
+ {
1446
+ "epoch": 0.71,
1447
+ "learning_rate": 0.00012725513832819964,
1448
+ "loss": 0.7167,
1449
+ "step": 240
1450
+ },
1451
+ {
1452
+ "epoch": 0.72,
1453
+ "learning_rate": 0.00012480134919919038,
1454
+ "loss": 0.7208,
1455
+ "step": 241
1456
+ },
1457
+ {
1458
+ "epoch": 0.72,
1459
+ "learning_rate": 0.00012236581439591161,
1460
+ "loss": 0.6406,
1461
+ "step": 242
1462
+ },
1463
+ {
1464
+ "epoch": 0.72,
1465
+ "learning_rate": 0.00011994875599301488,
1466
+ "loss": 0.6974,
1467
+ "step": 243
1468
+ },
1469
+ {
1470
+ "epoch": 0.73,
1471
+ "learning_rate": 0.00011755039438045392,
1472
+ "loss": 0.6357,
1473
+ "step": 244
1474
+ },
1475
+ {
1476
+ "epoch": 0.73,
1477
+ "learning_rate": 0.0001151709482433892,
1478
+ "loss": 0.7179,
1479
+ "step": 245
1480
+ },
1481
+ {
1482
+ "epoch": 0.73,
1483
+ "learning_rate": 0.00011281063454224838,
1484
+ "loss": 0.7553,
1485
+ "step": 246
1486
+ },
1487
+ {
1488
+ "epoch": 0.73,
1489
+ "learning_rate": 0.00011046966849294289,
1490
+ "loss": 0.7019,
1491
+ "step": 247
1492
+ },
1493
+ {
1494
+ "epoch": 0.74,
1495
+ "learning_rate": 0.00010814826354724483,
1496
+ "loss": 0.6608,
1497
+ "step": 248
1498
+ },
1499
+ {
1500
+ "epoch": 0.74,
1501
+ "learning_rate": 0.00010584663137332396,
1502
+ "loss": 0.6674,
1503
+ "step": 249
1504
+ },
1505
+ {
1506
+ "epoch": 0.74,
1507
+ "learning_rate": 0.00010356498183644816,
1508
+ "loss": 0.7079,
1509
+ "step": 250
1510
+ },
1511
+ {
1512
+ "epoch": 0.75,
1513
+ "learning_rate": 0.00010130352297984669,
1514
+ "loss": 0.6843,
1515
+ "step": 251
1516
+ },
1517
+ {
1518
+ "epoch": 0.75,
1519
+ "learning_rate": 9.906246100574125e-05,
1520
+ "loss": 0.6661,
1521
+ "step": 252
1522
+ },
1523
+ {
1524
+ "epoch": 0.75,
1525
+ "learning_rate": 9.684200025654447e-05,
1526
+ "loss": 0.7155,
1527
+ "step": 253
1528
+ },
1529
+ {
1530
+ "epoch": 0.76,
1531
+ "learning_rate": 9.4642343196227e-05,
1532
+ "loss": 0.6449,
1533
+ "step": 254
1534
+ },
1535
+ {
1536
+ "epoch": 0.76,
1537
+ "learning_rate": 9.246369039185703e-05,
1538
+ "loss": 0.6856,
1539
+ "step": 255
1540
+ },
1541
+ {
1542
+ "epoch": 0.76,
1543
+ "learning_rate": 9.030624049531278e-05,
1544
+ "loss": 0.7033,
1545
+ "step": 256
1546
+ },
1547
+ {
1548
+ "epoch": 0.76,
1549
+ "learning_rate": 8.817019022516829e-05,
1550
+ "loss": 0.629,
1551
+ "step": 257
1552
+ },
1553
+ {
1554
+ "epoch": 0.77,
1555
+ "learning_rate": 8.605573434875741e-05,
1556
+ "loss": 0.7189,
1557
+ "step": 258
1558
+ },
1559
+ {
1560
+ "epoch": 0.77,
1561
+ "learning_rate": 8.396306566441378e-05,
1562
+ "loss": 0.6984,
1563
+ "step": 259
1564
+ },
1565
+ {
1566
+ "epoch": 0.77,
1567
+ "learning_rate": 8.189237498389201e-05,
1568
+ "loss": 0.6973,
1569
+ "step": 260
1570
+ },
1571
+ {
1572
+ "epoch": 0.78,
1573
+ "learning_rate": 7.984385111496869e-05,
1574
+ "loss": 0.682,
1575
+ "step": 261
1576
+ },
1577
+ {
1578
+ "epoch": 0.78,
1579
+ "learning_rate": 7.781768084422741e-05,
1580
+ "loss": 0.6976,
1581
+ "step": 262
1582
+ },
1583
+ {
1584
+ "epoch": 0.78,
1585
+ "learning_rate": 7.581404892002655e-05,
1586
+ "loss": 0.656,
1587
+ "step": 263
1588
+ },
1589
+ {
1590
+ "epoch": 0.79,
1591
+ "learning_rate": 7.383313803565418e-05,
1592
+ "loss": 0.6696,
1593
+ "step": 264
1594
+ },
1595
+ {
1596
+ "epoch": 0.79,
1597
+ "learning_rate": 7.187512881266974e-05,
1598
+ "loss": 0.687,
1599
+ "step": 265
1600
+ },
1601
+ {
1602
+ "epoch": 0.79,
1603
+ "learning_rate": 6.994019978443517e-05,
1604
+ "loss": 0.6505,
1605
+ "step": 266
1606
+ },
1607
+ {
1608
+ "epoch": 0.79,
1609
+ "learning_rate": 6.802852737983543e-05,
1610
+ "loss": 0.6628,
1611
+ "step": 267
1612
+ },
1613
+ {
1614
+ "epoch": 0.8,
1615
+ "learning_rate": 6.614028590719186e-05,
1616
+ "loss": 0.6724,
1617
+ "step": 268
1618
+ },
1619
+ {
1620
+ "epoch": 0.8,
1621
+ "learning_rate": 6.427564753836846e-05,
1622
+ "loss": 0.6737,
1623
+ "step": 269
1624
+ },
1625
+ {
1626
+ "epoch": 0.8,
1627
+ "learning_rate": 6.243478229307349e-05,
1628
+ "loss": 0.6688,
1629
+ "step": 270
1630
+ },
1631
+ {
1632
+ "epoch": 0.81,
1633
+ "learning_rate": 6.061785802335616e-05,
1634
+ "loss": 0.69,
1635
+ "step": 271
1636
+ },
1637
+ {
1638
+ "epoch": 0.81,
1639
+ "learning_rate": 5.8825040398302217e-05,
1640
+ "loss": 0.713,
1641
+ "step": 272
1642
+ },
1643
+ {
1644
+ "epoch": 0.81,
1645
+ "learning_rate": 5.705649288892797e-05,
1646
+ "loss": 0.7063,
1647
+ "step": 273
1648
+ },
1649
+ {
1650
+ "epoch": 0.81,
1651
+ "learning_rate": 5.531237675327462e-05,
1652
+ "loss": 0.6675,
1653
+ "step": 274
1654
+ },
1655
+ {
1656
+ "epoch": 0.82,
1657
+ "learning_rate": 5.35928510217051e-05,
1658
+ "loss": 0.6759,
1659
+ "step": 275
1660
+ },
1661
+ {
1662
+ "epoch": 0.82,
1663
+ "learning_rate": 5.189807248240284e-05,
1664
+ "loss": 0.7002,
1665
+ "step": 276
1666
+ },
1667
+ {
1668
+ "epoch": 0.82,
1669
+ "learning_rate": 5.0228195667076076e-05,
1670
+ "loss": 0.6508,
1671
+ "step": 277
1672
+ },
1673
+ {
1674
+ "epoch": 0.83,
1675
+ "learning_rate": 4.858337283686736e-05,
1676
+ "loss": 0.6123,
1677
+ "step": 278
1678
+ },
1679
+ {
1680
+ "epoch": 0.83,
1681
+ "learning_rate": 4.696375396847039e-05,
1682
+ "loss": 0.6391,
1683
+ "step": 279
1684
+ },
1685
+ {
1686
+ "epoch": 0.83,
1687
+ "learning_rate": 4.536948674045477e-05,
1688
+ "loss": 0.7003,
1689
+ "step": 280
1690
+ },
1691
+ {
1692
+ "epoch": 0.84,
1693
+ "learning_rate": 4.380071651980048e-05,
1694
+ "loss": 0.6792,
1695
+ "step": 281
1696
+ },
1697
+ {
1698
+ "epoch": 0.84,
1699
+ "learning_rate": 4.2257586348643344e-05,
1700
+ "loss": 0.6191,
1701
+ "step": 282
1702
+ },
1703
+ {
1704
+ "epoch": 0.84,
1705
+ "learning_rate": 4.074023693123235e-05,
1706
+ "loss": 0.7055,
1707
+ "step": 283
1708
+ },
1709
+ {
1710
+ "epoch": 0.84,
1711
+ "learning_rate": 3.924880662109986e-05,
1712
+ "loss": 0.6713,
1713
+ "step": 284
1714
+ },
1715
+ {
1716
+ "epoch": 0.85,
1717
+ "learning_rate": 3.778343140844634e-05,
1718
+ "loss": 0.6718,
1719
+ "step": 285
1720
+ },
1721
+ {
1722
+ "epoch": 0.85,
1723
+ "learning_rate": 3.634424490774111e-05,
1724
+ "loss": 0.6732,
1725
+ "step": 286
1726
+ },
1727
+ {
1728
+ "epoch": 0.85,
1729
+ "learning_rate": 3.4931378345538636e-05,
1730
+ "loss": 0.6792,
1731
+ "step": 287
1732
+ },
1733
+ {
1734
+ "epoch": 0.86,
1735
+ "learning_rate": 3.354496054851349e-05,
1736
+ "loss": 0.6245,
1737
+ "step": 288
1738
+ },
1739
+ {
1740
+ "epoch": 0.86,
1741
+ "learning_rate": 3.218511793171381e-05,
1742
+ "loss": 0.7335,
1743
+ "step": 289
1744
+ },
1745
+ {
1746
+ "epoch": 0.86,
1747
+ "learning_rate": 3.0851974487034374e-05,
1748
+ "loss": 0.6994,
1749
+ "step": 290
1750
+ },
1751
+ {
1752
+ "epoch": 0.87,
1753
+ "learning_rate": 2.9545651771911387e-05,
1754
+ "loss": 0.6036,
1755
+ "step": 291
1756
+ },
1757
+ {
1758
+ "epoch": 0.87,
1759
+ "learning_rate": 2.8266268898238098e-05,
1760
+ "loss": 0.6887,
1761
+ "step": 292
1762
+ },
1763
+ {
1764
+ "epoch": 0.87,
1765
+ "learning_rate": 2.70139425215046e-05,
1766
+ "loss": 0.6458,
1767
+ "step": 293
1768
+ },
1769
+ {
1770
+ "epoch": 0.87,
1771
+ "learning_rate": 2.578878683016061e-05,
1772
+ "loss": 0.662,
1773
+ "step": 294
1774
+ },
1775
+ {
1776
+ "epoch": 0.88,
1777
+ "learning_rate": 2.4590913535204267e-05,
1778
+ "loss": 0.6584,
1779
+ "step": 295
1780
+ },
1781
+ {
1782
+ "epoch": 0.88,
1783
+ "learning_rate": 2.3420431859995406e-05,
1784
+ "loss": 0.6985,
1785
+ "step": 296
1786
+ },
1787
+ {
1788
+ "epoch": 0.88,
1789
+ "learning_rate": 2.227744853029714e-05,
1790
+ "loss": 0.6331,
1791
+ "step": 297
1792
+ },
1793
+ {
1794
+ "epoch": 0.89,
1795
+ "learning_rate": 2.1162067764543974e-05,
1796
+ "loss": 0.6036,
1797
+ "step": 298
1798
+ },
1799
+ {
1800
+ "epoch": 0.89,
1801
+ "learning_rate": 2.007439126433962e-05,
1802
+ "loss": 0.6444,
1803
+ "step": 299
1804
+ },
1805
+ {
1806
+ "epoch": 0.89,
1807
+ "learning_rate": 1.9014518205183142e-05,
1808
+ "loss": 0.7185,
1809
+ "step": 300
1810
+ }
1811
+ ],
1812
+ "logging_steps": 1,
1813
+ "max_steps": 336,
1814
+ "num_input_tokens_seen": 0,
1815
+ "num_train_epochs": 1,
1816
+ "save_steps": 50,
1817
+ "total_flos": 3.800476775719895e+17,
1818
+ "train_batch_size": 6,
1819
+ "trial_name": null,
1820
+ "trial_params": null
1821
+ }
checkpoint-300/training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:3a720d2daf4afe192195297ee83d1a8834602d7e8eda493eaba5ee762dd57a90
3
+ size 4664
checkpoint-50/README.md ADDED
@@ -0,0 +1,204 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ library_name: peft
3
+ base_model: unsloth/llama-2-7b
4
+ ---
5
+
6
+ # Model Card for Model ID
7
+
8
+ <!-- Provide a quick summary of what the model is/does. -->
9
+
10
+
11
+
12
+ ## Model Details
13
+
14
+ ### Model Description
15
+
16
+ <!-- Provide a longer summary of what this model is. -->
17
+
18
+
19
+
20
+ - **Developed by:** [More Information Needed]
21
+ - **Funded by [optional]:** [More Information Needed]
22
+ - **Shared by [optional]:** [More Information Needed]
23
+ - **Model type:** [More Information Needed]
24
+ - **Language(s) (NLP):** [More Information Needed]
25
+ - **License:** [More Information Needed]
26
+ - **Finetuned from model [optional]:** [More Information Needed]
27
+
28
+ ### Model Sources [optional]
29
+
30
+ <!-- Provide the basic links for the model. -->
31
+
32
+ - **Repository:** [More Information Needed]
33
+ - **Paper [optional]:** [More Information Needed]
34
+ - **Demo [optional]:** [More Information Needed]
35
+
36
+ ## Uses
37
+
38
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
39
+
40
+ ### Direct Use
41
+
42
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
43
+
44
+ [More Information Needed]
45
+
46
+ ### Downstream Use [optional]
47
+
48
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
49
+
50
+ [More Information Needed]
51
+
52
+ ### Out-of-Scope Use
53
+
54
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
55
+
56
+ [More Information Needed]
57
+
58
+ ## Bias, Risks, and Limitations
59
+
60
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
61
+
62
+ [More Information Needed]
63
+
64
+ ### Recommendations
65
+
66
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
67
+
68
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
69
+
70
+ ## How to Get Started with the Model
71
+
72
+ Use the code below to get started with the model.
73
+
74
+ [More Information Needed]
75
+
76
+ ## Training Details
77
+
78
+ ### Training Data
79
+
80
+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
81
+
82
+ [More Information Needed]
83
+
84
+ ### Training Procedure
85
+
86
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
87
+
88
+ #### Preprocessing [optional]
89
+
90
+ [More Information Needed]
91
+
92
+
93
+ #### Training Hyperparameters
94
+
95
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
96
+
97
+ #### Speeds, Sizes, Times [optional]
98
+
99
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
100
+
101
+ [More Information Needed]
102
+
103
+ ## Evaluation
104
+
105
+ <!-- This section describes the evaluation protocols and provides the results. -->
106
+
107
+ ### Testing Data, Factors & Metrics
108
+
109
+ #### Testing Data
110
+
111
+ <!-- This should link to a Dataset Card if possible. -->
112
+
113
+ [More Information Needed]
114
+
115
+ #### Factors
116
+
117
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
118
+
119
+ [More Information Needed]
120
+
121
+ #### Metrics
122
+
123
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
124
+
125
+ [More Information Needed]
126
+
127
+ ### Results
128
+
129
+ [More Information Needed]
130
+
131
+ #### Summary
132
+
133
+
134
+
135
+ ## Model Examination [optional]
136
+
137
+ <!-- Relevant interpretability work for the model goes here -->
138
+
139
+ [More Information Needed]
140
+
141
+ ## Environmental Impact
142
+
143
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
144
+
145
+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
146
+
147
+ - **Hardware Type:** [More Information Needed]
148
+ - **Hours used:** [More Information Needed]
149
+ - **Cloud Provider:** [More Information Needed]
150
+ - **Compute Region:** [More Information Needed]
151
+ - **Carbon Emitted:** [More Information Needed]
152
+
153
+ ## Technical Specifications [optional]
154
+
155
+ ### Model Architecture and Objective
156
+
157
+ [More Information Needed]
158
+
159
+ ### Compute Infrastructure
160
+
161
+ [More Information Needed]
162
+
163
+ #### Hardware
164
+
165
+ [More Information Needed]
166
+
167
+ #### Software
168
+
169
+ [More Information Needed]
170
+
171
+ ## Citation [optional]
172
+
173
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
174
+
175
+ **BibTeX:**
176
+
177
+ [More Information Needed]
178
+
179
+ **APA:**
180
+
181
+ [More Information Needed]
182
+
183
+ ## Glossary [optional]
184
+
185
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
186
+
187
+ [More Information Needed]
188
+
189
+ ## More Information [optional]
190
+
191
+ [More Information Needed]
192
+
193
+ ## Model Card Authors [optional]
194
+
195
+ [More Information Needed]
196
+
197
+ ## Model Card Contact
198
+
199
+ [More Information Needed]
200
+
201
+
202
+ ### Framework versions
203
+
204
+ - PEFT 0.7.1
checkpoint-50/adapter_config.json ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alpha_pattern": {},
3
+ "auto_mapping": null,
4
+ "base_model_name_or_path": "unsloth/llama-2-7b",
5
+ "bias": "none",
6
+ "fan_in_fan_out": false,
7
+ "inference_mode": true,
8
+ "init_lora_weights": true,
9
+ "layers_pattern": null,
10
+ "layers_to_transform": null,
11
+ "loftq_config": {},
12
+ "lora_alpha": 16,
13
+ "lora_dropout": 0,
14
+ "megatron_config": null,
15
+ "megatron_core": "megatron.core",
16
+ "modules_to_save": null,
17
+ "peft_type": "LORA",
18
+ "r": 128,
19
+ "rank_pattern": {},
20
+ "revision": "unsloth",
21
+ "target_modules": [
22
+ "up_proj",
23
+ "k_proj",
24
+ "v_proj",
25
+ "o_proj",
26
+ "q_proj",
27
+ "down_proj",
28
+ "gate_proj"
29
+ ],
30
+ "task_type": "CAUSAL_LM"
31
+ }
checkpoint-50/adapter_model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:29f6075899ff428d745025d721d5150fc5b677ae119c479974ab1b9312db3bd6
3
+ size 1279323952
checkpoint-50/optimizer.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:2c6c5c285e853e5988709fbeb6db6f8d8e6db8b9c52e9f310135086a0b87768c
3
+ size 641407572
checkpoint-50/rng_state.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:fe2e145b09b2faab2c45440a9233e2700f8dca5428319c1eb306332f174e4af7
3
+ size 14244
checkpoint-50/scheduler.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:90aed7a0fe2e312a9d0bcd1d5272104980cb392470311479e119f6325d970464
3
+ size 1064
checkpoint-50/trainer_state.json ADDED
@@ -0,0 +1,321 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "best_metric": null,
3
+ "best_model_checkpoint": null,
4
+ "epoch": 0.148720999405116,
5
+ "eval_steps": 500,
6
+ "global_step": 50,
7
+ "is_hyper_param_search": false,
8
+ "is_local_process_zero": true,
9
+ "is_world_process_zero": true,
10
+ "log_history": [
11
+ {
12
+ "epoch": 0.0,
13
+ "learning_rate": 9.285714285714285e-05,
14
+ "loss": 2.4427,
15
+ "step": 1
16
+ },
17
+ {
18
+ "epoch": 0.01,
19
+ "learning_rate": 0.0001857142857142857,
20
+ "loss": 2.3973,
21
+ "step": 2
22
+ },
23
+ {
24
+ "epoch": 0.01,
25
+ "learning_rate": 0.00027857142857142854,
26
+ "loss": 2.341,
27
+ "step": 3
28
+ },
29
+ {
30
+ "epoch": 0.01,
31
+ "learning_rate": 0.0003714285714285714,
32
+ "loss": 2.1281,
33
+ "step": 4
34
+ },
35
+ {
36
+ "epoch": 0.01,
37
+ "learning_rate": 0.0004642857142857143,
38
+ "loss": 1.4346,
39
+ "step": 5
40
+ },
41
+ {
42
+ "epoch": 0.02,
43
+ "learning_rate": 0.0005571428571428571,
44
+ "loss": 1.1715,
45
+ "step": 6
46
+ },
47
+ {
48
+ "epoch": 0.02,
49
+ "learning_rate": 0.00065,
50
+ "loss": 1.086,
51
+ "step": 7
52
+ },
53
+ {
54
+ "epoch": 0.02,
55
+ "learning_rate": 0.0006499851830773117,
56
+ "loss": 0.9921,
57
+ "step": 8
58
+ },
59
+ {
60
+ "epoch": 0.03,
61
+ "learning_rate": 0.00064994073366027,
62
+ "loss": 0.9231,
63
+ "step": 9
64
+ },
65
+ {
66
+ "epoch": 0.03,
67
+ "learning_rate": 0.0006498666558018197,
68
+ "loss": 0.9343,
69
+ "step": 10
70
+ },
71
+ {
72
+ "epoch": 0.03,
73
+ "learning_rate": 0.0006497629562564588,
74
+ "loss": 0.9191,
75
+ "step": 11
76
+ },
77
+ {
78
+ "epoch": 0.04,
79
+ "learning_rate": 0.0006496296444796219,
80
+ "loss": 0.8791,
81
+ "step": 12
82
+ },
83
+ {
84
+ "epoch": 0.04,
85
+ "learning_rate": 0.0006494667326268186,
86
+ "loss": 0.8632,
87
+ "step": 13
88
+ },
89
+ {
90
+ "epoch": 0.04,
91
+ "learning_rate": 0.0006492742355525248,
92
+ "loss": 0.9267,
93
+ "step": 14
94
+ },
95
+ {
96
+ "epoch": 0.04,
97
+ "learning_rate": 0.0006490521708088281,
98
+ "loss": 0.8644,
99
+ "step": 15
100
+ },
101
+ {
102
+ "epoch": 0.05,
103
+ "learning_rate": 0.000648800558643828,
104
+ "loss": 0.8353,
105
+ "step": 16
106
+ },
107
+ {
108
+ "epoch": 0.05,
109
+ "learning_rate": 0.0006485194219997891,
110
+ "loss": 0.8482,
111
+ "step": 17
112
+ },
113
+ {
114
+ "epoch": 0.05,
115
+ "learning_rate": 0.0006482087865110493,
116
+ "loss": 0.8587,
117
+ "step": 18
118
+ },
119
+ {
120
+ "epoch": 0.06,
121
+ "learning_rate": 0.0006478686805016826,
122
+ "loss": 0.9134,
123
+ "step": 19
124
+ },
125
+ {
126
+ "epoch": 0.06,
127
+ "learning_rate": 0.0006474991349829163,
128
+ "loss": 0.8238,
129
+ "step": 20
130
+ },
131
+ {
132
+ "epoch": 0.06,
133
+ "learning_rate": 0.0006471001836503035,
134
+ "loss": 0.8329,
135
+ "step": 21
136
+ },
137
+ {
138
+ "epoch": 0.07,
139
+ "learning_rate": 0.0006466718628806508,
140
+ "loss": 0.7995,
141
+ "step": 22
142
+ },
143
+ {
144
+ "epoch": 0.07,
145
+ "learning_rate": 0.0006462142117287011,
146
+ "loss": 0.8363,
147
+ "step": 23
148
+ },
149
+ {
150
+ "epoch": 0.07,
151
+ "learning_rate": 0.0006457272719235728,
152
+ "loss": 0.7942,
153
+ "step": 24
154
+ },
155
+ {
156
+ "epoch": 0.07,
157
+ "learning_rate": 0.0006452110878649547,
158
+ "loss": 0.858,
159
+ "step": 25
160
+ },
161
+ {
162
+ "epoch": 0.08,
163
+ "learning_rate": 0.0006446657066190579,
164
+ "loss": 0.8474,
165
+ "step": 26
166
+ },
167
+ {
168
+ "epoch": 0.08,
169
+ "learning_rate": 0.000644091177914324,
170
+ "loss": 0.8175,
171
+ "step": 27
172
+ },
173
+ {
174
+ "epoch": 0.08,
175
+ "learning_rate": 0.0006434875541368907,
176
+ "loss": 0.7821,
177
+ "step": 28
178
+ },
179
+ {
180
+ "epoch": 0.09,
181
+ "learning_rate": 0.0006428548903258156,
182
+ "loss": 0.8583,
183
+ "step": 29
184
+ },
185
+ {
186
+ "epoch": 0.09,
187
+ "learning_rate": 0.0006421932441680574,
188
+ "loss": 0.8071,
189
+ "step": 30
190
+ },
191
+ {
192
+ "epoch": 0.09,
193
+ "learning_rate": 0.0006415026759932158,
194
+ "loss": 0.805,
195
+ "step": 31
196
+ },
197
+ {
198
+ "epoch": 0.1,
199
+ "learning_rate": 0.0006407832487680309,
200
+ "loss": 0.881,
201
+ "step": 32
202
+ },
203
+ {
204
+ "epoch": 0.1,
205
+ "learning_rate": 0.0006400350280906415,
206
+ "loss": 0.8302,
207
+ "step": 33
208
+ },
209
+ {
210
+ "epoch": 0.1,
211
+ "learning_rate": 0.0006392580821846041,
212
+ "loss": 0.8456,
213
+ "step": 34
214
+ },
215
+ {
216
+ "epoch": 0.1,
217
+ "learning_rate": 0.0006384524818926723,
218
+ "loss": 0.8067,
219
+ "step": 35
220
+ },
221
+ {
222
+ "epoch": 0.11,
223
+ "learning_rate": 0.0006376183006703367,
224
+ "loss": 0.8307,
225
+ "step": 36
226
+ },
227
+ {
228
+ "epoch": 0.11,
229
+ "learning_rate": 0.0006367556145791275,
230
+ "loss": 0.8347,
231
+ "step": 37
232
+ },
233
+ {
234
+ "epoch": 0.11,
235
+ "learning_rate": 0.0006358645022796795,
236
+ "loss": 0.8086,
237
+ "step": 38
238
+ },
239
+ {
240
+ "epoch": 0.12,
241
+ "learning_rate": 0.0006349450450245589,
242
+ "loss": 0.8726,
243
+ "step": 39
244
+ },
245
+ {
246
+ "epoch": 0.12,
247
+ "learning_rate": 0.0006339973266508556,
248
+ "loss": 0.78,
249
+ "step": 40
250
+ },
251
+ {
252
+ "epoch": 0.12,
253
+ "learning_rate": 0.0006330214335725379,
254
+ "loss": 0.7902,
255
+ "step": 41
256
+ },
257
+ {
258
+ "epoch": 0.12,
259
+ "learning_rate": 0.0006320174547725736,
260
+ "loss": 0.7823,
261
+ "step": 42
262
+ },
263
+ {
264
+ "epoch": 0.13,
265
+ "learning_rate": 0.0006309854817948169,
266
+ "loss": 0.8211,
267
+ "step": 43
268
+ },
269
+ {
270
+ "epoch": 0.13,
271
+ "learning_rate": 0.0006299256087356603,
272
+ "loss": 0.8656,
273
+ "step": 44
274
+ },
275
+ {
276
+ "epoch": 0.13,
277
+ "learning_rate": 0.000628837932235456,
278
+ "loss": 0.7767,
279
+ "step": 45
280
+ },
281
+ {
282
+ "epoch": 0.14,
283
+ "learning_rate": 0.0006277225514697028,
284
+ "loss": 0.7542,
285
+ "step": 46
286
+ },
287
+ {
288
+ "epoch": 0.14,
289
+ "learning_rate": 0.0006265795681400046,
290
+ "loss": 0.8254,
291
+ "step": 47
292
+ },
293
+ {
294
+ "epoch": 0.14,
295
+ "learning_rate": 0.0006254090864647957,
296
+ "loss": 0.8099,
297
+ "step": 48
298
+ },
299
+ {
300
+ "epoch": 0.15,
301
+ "learning_rate": 0.0006242112131698394,
302
+ "loss": 0.7786,
303
+ "step": 49
304
+ },
305
+ {
306
+ "epoch": 0.15,
307
+ "learning_rate": 0.0006229860574784954,
308
+ "loss": 0.7895,
309
+ "step": 50
310
+ }
311
+ ],
312
+ "logging_steps": 1,
313
+ "max_steps": 336,
314
+ "num_input_tokens_seen": 0,
315
+ "num_train_epochs": 1,
316
+ "save_steps": 50,
317
+ "total_flos": 6.338982512767795e+16,
318
+ "train_batch_size": 6,
319
+ "trial_name": null,
320
+ "trial_params": null
321
+ }
checkpoint-50/training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:3a720d2daf4afe192195297ee83d1a8834602d7e8eda493eaba5ee762dd57a90
3
+ size 4664