---
base_model: meta-llama/Llama-2-7b-hf
tags:
- generated_from_trainer
model-index:
- name: sparse_llama_7b_hf_refined_web_70p_2024-03-25
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# sparse_llama_7b_hf_refined_web_70p_2024-03-25

This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1856

## 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: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 0
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.3676        | 0.01  | 25   | 2.5933          |
| 2.3572        | 0.02  | 50   | 2.5793          |
| 2.4503        | 0.02  | 75   | 2.5568          |
| 2.3803        | 0.03  | 100  | 2.5265          |
| 2.4451        | 0.04  | 125  | 2.4951          |
| 2.2793        | 0.05  | 150  | 2.4778          |
| 2.2444        | 0.06  | 175  | 2.4667          |
| 2.406         | 0.06  | 200  | 2.4572          |
| 2.3583        | 0.07  | 225  | 2.4508          |
| 2.3262        | 0.08  | 250  | 2.4538          |
| 2.258         | 0.09  | 275  | 2.4476          |
| 2.2841        | 0.1   | 300  | 2.4456          |
| 2.3232        | 0.1   | 325  | 2.4379          |
| 2.2974        | 0.11  | 350  | 2.4353          |
| 2.2216        | 0.12  | 375  | 2.4379          |
| 2.3179        | 0.13  | 400  | 2.4340          |
| 2.3006        | 0.14  | 425  | 2.4333          |
| 2.2603        | 0.14  | 450  | 2.4333          |
| 2.3371        | 0.15  | 475  | 2.4384          |
| 2.3453        | 0.16  | 500  | 2.4328          |
| 2.254         | 0.17  | 525  | 2.4306          |
| 2.2423        | 0.18  | 550  | 2.4298          |
| 2.3666        | 0.18  | 575  | 2.4293          |
| 2.259         | 0.19  | 600  | 2.4298          |
| 2.2786        | 0.2   | 625  | 2.4290          |
| 2.3493        | 0.21  | 650  | 2.4275          |
| 2.2532        | 0.22  | 675  | 2.4255          |
| 2.2698        | 0.22  | 700  | 2.4233          |
| 2.2949        | 0.23  | 725  | 2.4277          |
| 2.1918        | 0.24  | 750  | 2.4268          |
| 2.2762        | 0.25  | 775  | 2.4243          |
| 2.3221        | 0.26  | 800  | 2.4256          |
| 2.278         | 0.26  | 825  | 2.4273          |
| 2.2406        | 0.27  | 850  | 2.4223          |
| 2.2466        | 0.28  | 875  | 2.4252          |
| 2.2199        | 0.29  | 900  | 2.4247          |
| 2.4064        | 0.3   | 925  | 2.4259          |
| 2.3672        | 0.3   | 950  | 2.4237          |
| 2.3096        | 0.31  | 975  | 2.4226          |
| 2.1979        | 0.32  | 1000 | 2.4257          |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.2