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# layer_4,5,6,7,8

This model is a fine-tuned version of [P1ayer-1/pythia-deduped-1b-chat-base](https://huggingface.co/P1ayer-1/pythia-deduped-1b-chat-base) on the pile-instruct/ dataset.
It achieves the following results on the evaluation set:
- Loss: 4.9648
- Accuracy: 0.3842

## 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: 0.0001
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 96
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 6000

### Training results

| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 7.4574        | 0.1   | 200  | 0.1688   | 7.4961          |
| 7.0445        | 0.2   | 400  | 0.1997   | 7.0547          |
| 6.7483        | 0.3   | 600  | 0.2190   | 6.7930          |
| 6.4568        | 0.4   | 800  | 0.2376   | 6.5703          |
| 6.2865        | 0.5   | 1000 | 0.2552   | 6.375           |
| 6.1028        | 0.6   | 1200 | 0.2793   | 6.1484          |
| 5.8888        | 0.7   | 1400 | 0.2982   | 5.9570          |
| 5.7362        | 0.8   | 1600 | 0.3121   | 5.8008          |
| 5.6507        | 0.9   | 1800 | 0.3238   | 5.6797          |
| 5.565         | 1.0   | 2000 | 0.3318   | 5.5781          |
| 5.4688        | 1.1   | 2200 | 0.3392   | 5.4961          |
| 5.4044        | 1.2   | 2400 | 0.3456   | 5.4219          |
| 5.3323        | 1.3   | 2600 | 0.3516   | 5.3594          |
| 5.2598        | 1.4   | 2800 | 0.3562   | 5.3047          |
| 5.2159        | 1.5   | 3000 | 0.3596   | 5.2578          |
| 5.1992        | 1.6   | 3200 | 0.3638   | 5.2148          |
| 5.1429        | 1.69  | 3400 | 0.3672   | 5.1797          |
| 5.095         | 1.79  | 3600 | 0.3696   | 5.1445          |
| 5.0646        | 1.89  | 3800 | 0.3715   | 5.1172          |
| 5.059         | 1.99  | 4000 | 0.3742   | 5.0859          |
| 5.0152        | 2.09  | 4200 | 0.3756   | 5.0664          |
| 5.0251        | 2.19  | 4400 | 0.3769   | 5.0469          |
| 5.022         | 2.29  | 4600 | 0.3790   | 5.0273          |
| 4.9939        | 2.39  | 4800 | 0.3798   | 5.0156          |
| 4.924         | 2.49  | 5000 | 0.3811   | 5.0             |
| 4.9335        | 2.59  | 5200 | 0.3821   | 4.9883          |
| 4.9231        | 2.69  | 5400 | 0.3829   | 4.9805          |
| 4.8886        | 2.79  | 5600 | 0.3835   | 4.9727          |
| 4.9419        | 2.89  | 5800 | 0.3843   | 4.9648          |
| 4.9227        | 2.99  | 6000 | 0.3842   | 4.9648          |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3


## Wandb Report
 https://wandb.ai/ontocord/pythia-1b-deduped-layer-test-min-pile-instruct/runs/zad9qli2
{"tags": ["generated_from_trainer"], "datasets": ["pile-instruct/"], "metrics": ["accuracy"], "model-index": [{"name": "layer_4,5,6,7,8", "results": [{"task": {"type": "text-generation", "name": "Causal Language Modeling"}, "dataset": {"name": "pile-instruct/", "type": "pile-instruct/", "split": "None"}, "metrics": [{"type": "accuracy", "value": 0.38424293893426953, "name": "Accuracy"}]}]}]}
transformers pytorch gpt_neox text-generation generated_from_trainer autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #gpt_neox #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

layer\_4,5,6,7,8
================


This model is a fine-tuned version of P1ayer-1/pythia-deduped-1b-chat-base on the pile-instruct/ dataset.
It achieves the following results on the evaluation set:


* Loss: 4.9648
* Accuracy: 0.3842


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: 0.0001
* train\_batch\_size: 12
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* total\_train\_batch\_size: 96
* total\_eval\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 6000


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.3


Wandb Report
------------


URL



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 12
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* total\_train\_batch\_size: 96
* total\_eval\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 6000 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.3


Wandb Report
------------


URL
TAGS
#transformers #pytorch #gpt_neox #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 12
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* total\_train\_batch\_size: 96
* total\_eval\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 6000 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.3


Wandb Report
------------


URL