Model save

README.md

@@ -1,7 +1,7 @@
 ---
-base_model: RyanYr/reflect_ministral8Bit_om2_sft-t2_lr.5-6
+base_model: RyanYr/reflect_mini8Bit_om2-460k_sft-t1
 library_name: transformers
-model_name: reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0
+model_name: reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0
 tags:
 - generated_from_trainer
 - trl
@@ -9,9 +9,9 @@ tags:
 licence: license
 ---
 
-# Model Card for reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0
+# Model Card for reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0
 
-This model is a fine-tuned version of [RyanYr/reflect_ministral8Bit_om2_sft-t2_lr.5-6](https://huggingface.co/RyanYr/reflect_ministral8Bit_om2_sft-t2_lr.5-6).
+This model is a fine-tuned version of [RyanYr/reflect_mini8Bit_om2-460k_sft-t1](https://huggingface.co/RyanYr/reflect_mini8Bit_om2-460k_sft-t1).
 It has been trained using [TRL](https://github.com/huggingface/trl).
 
 ## Quick start
@@ -20,14 +20,14 @@ It has been trained using [TRL](https://github.com/huggingface/trl).
 from transformers import pipeline
 
 question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="RyanYr/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b1.0", device="cuda")
+generator = pipeline("text-generation", model="RyanYr/reflect_mini8B_Om2SftT1-Om2G8kIpsdpIter1T1_b1.0", device="cuda")
 output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
 print(output["generated_text"])
 ```
 
 ## Training procedure
 
-[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/yyr/huggingface/runs/e0pwbi40)
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/yyr/huggingface/runs/ijjbovca)
 
 This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
 

last_checkpoint/config.json

@@ -1,5 +1,5 @@
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+  "_name_or_path": "RyanYr/reflect_mini8Bit_om2-460k_sft-t1",
   "architectures": [
     "MistralForCausalLM"
   ],

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