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# Cerebras-GPT-1.3B-lora-s-t3000-v300-v1

This model is a fine-tuned version of [cerebras/Cerebras-GPT-1.3B](https://huggingface.co/cerebras/Cerebras-GPT-1.3B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2409

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.4608        | 0.11  | 20   | 2.4030          |
| 2.2475        | 0.21  | 40   | 2.2757          |
| 2.2432        | 0.32  | 60   | 2.2579          |
| 2.3011        | 0.43  | 80   | 2.2467          |
| 2.2293        | 0.53  | 100  | 2.2478          |
| 2.1398        | 0.64  | 120  | 2.2436          |
| 2.2571        | 0.75  | 140  | 2.2413          |
| 2.1577        | 0.85  | 160  | 2.2349          |
| 2.2442        | 0.96  | 180  | 2.2371          |
| 2.2592        | 1.07  | 200  | 2.2342          |
| 2.2082        | 1.17  | 220  | 2.2352          |
| 2.1402        | 1.28  | 240  | 2.2345          |
| 2.1216        | 1.39  | 260  | 2.2345          |
| 2.1758        | 1.49  | 280  | 2.2320          |
| 2.1625        | 1.6   | 300  | 2.2329          |
| 2.1491        | 1.71  | 320  | 2.2311          |
| 2.2307        | 1.81  | 340  | 2.2286          |
| 2.1102        | 1.92  | 360  | 2.2300          |
| 2.2054        | 2.03  | 380  | 2.2278          |
| 2.157         | 2.13  | 400  | 2.2345          |
| 2.0643        | 2.24  | 420  | 2.2359          |
| 2.2134        | 2.35  | 440  | 2.2343          |
| 2.1296        | 2.45  | 460  | 2.2347          |
| 2.1001        | 2.56  | 480  | 2.2346          |
| 2.1401        | 2.67  | 500  | 2.2327          |
| 2.091         | 2.77  | 520  | 2.2328          |
| 2.1365        | 2.88  | 540  | 2.2359          |
| 2.1201        | 2.99  | 560  | 2.2295          |
| 2.1359        | 3.09  | 580  | 2.2338          |
| 2.0979        | 3.2   | 600  | 2.2427          |
| 2.2025        | 3.31  | 620  | 2.2345          |
| 2.1001        | 3.41  | 640  | 2.2368          |
| 2.0228        | 3.52  | 660  | 2.2350          |
| 2.1174        | 3.63  | 680  | 2.2362          |
| 2.0688        | 3.73  | 700  | 2.2372          |
| 2.0368        | 3.84  | 720  | 2.2328          |
| 2.1409        | 3.95  | 740  | 2.2341          |
| 2.0675        | 4.05  | 760  | 2.2377          |
| 2.1805        | 4.16  | 780  | 2.2392          |
| 2.0844        | 4.27  | 800  | 2.2417          |
| 2.0834        | 4.37  | 820  | 2.2395          |
| 2.1396        | 4.48  | 840  | 2.2400          |
| 2.1121        | 4.59  | 860  | 2.2394          |
| 2.0195        | 4.69  | 880  | 2.2391          |
| 2.0564        | 4.8   | 900  | 2.2391          |
| 1.9447        | 4.91  | 920  | 2.2396          |
| 2.2122        | 5.01  | 940  | 2.2384          |
| 2.0482        | 5.12  | 960  | 2.2404          |
| 2.051         | 5.23  | 980  | 2.2411          |
| 2.0345        | 5.33  | 1000 | 2.2409          |


### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "Cerebras-GPT-1.3B-lora-s-t3000-v300-v1", "results": []}]}
pytorch tensorboard generated_from_trainer license:apache-2.0 region:us
TAGS
#pytorch #tensorboard #generated_from_trainer #license-apache-2.0 #region-us 

Cerebras-GPT-1.3B-lora-s-t3000-v300-v1
======================================


This model is a fine-tuned version of cerebras/Cerebras-GPT-1.3B on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 2.2409


Model description
-----------------


More information needed


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
----------------------------


More information needed


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 10
* training\_steps: 1000
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 10
* training\_steps: 1000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2
TAGS
#pytorch #tensorboard #generated_from_trainer #license-apache-2.0 #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 10
* training\_steps: 1000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2
TAGS
#pytorch #tensorboard #generated_from_trainer #license-apache-2.0 #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 10
* training\_steps: 1000
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.11.0
* Tokenizers 0.13.2