transformers safetensors sparse_mistral text-generation generated_from_trainer custom_code base_model:mistralai/Mistral-7B-v0.1 license:apache-2.0 autotrain_compatible region:us
---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- generated_from_trainer
model-index:
- name: Mistral_Sparse_refined_web_relu_2024-02-16
  results: []
---

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# Mistral_Sparse_refined_web_relu_2024-02-16

This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4640

## 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: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- total_eval_batch_size: 2
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 8.7883        | 0.0   | 25   | 8.7175          |
| 8.1895        | 0.01  | 50   | 8.1790          |
| 7.7646        | 0.01  | 75   | 7.8165          |
| 7.5412        | 0.02  | 100  | 7.6103          |
| 7.2131        | 0.02  | 125  | 7.1825          |
| 4.8603        | 0.02  | 150  | 4.8039          |
| 3.7952        | 0.03  | 175  | 3.8330          |
| 3.2884        | 0.03  | 200  | 3.4432          |
| 3.106         | 0.04  | 225  | 3.2520          |
| 3.0004        | 0.04  | 250  | 3.1245          |
| 2.8648        | 0.04  | 275  | 3.0460          |
| 2.8349        | 0.05  | 300  | 2.9954          |
| 2.7982        | 0.05  | 325  | 2.9562          |
| 2.6109        | 0.06  | 350  | 2.9206          |
| 2.7517        | 0.06  | 375  | 2.8975          |
| 2.7817        | 0.06  | 400  | 2.8770          |
| 2.7346        | 0.07  | 425  | 2.8580          |
| 2.7019        | 0.07  | 450  | 2.8443          |
| 2.5852        | 0.08  | 475  | 2.8288          |
| 2.6452        | 0.08  | 500  | 2.8196          |
| 2.7203        | 0.08  | 525  | 2.8109          |
| 2.627         | 0.09  | 550  | 2.8013          |
| 2.7272        | 0.09  | 575  | 2.7899          |
| 2.5443        | 0.1   | 600  | 2.7826          |
| 2.6178        | 0.1   | 625  | 2.7782          |
| 2.656         | 0.1   | 650  | 2.7680          |
| 2.676         | 0.11  | 675  | 2.7593          |
| 2.6061        | 0.11  | 700  | 2.7539          |
| 2.6263        | 0.12  | 725  | 2.7511          |
| 2.5305        | 0.12  | 750  | 2.7474          |
| 2.5344        | 0.12  | 775  | 2.7408          |
| 2.655         | 0.13  | 800  | 2.7377          |
| 2.6113        | 0.13  | 825  | 2.7332          |
| 2.5946        | 0.14  | 850  | 2.7296          |
| 2.4564        | 0.14  | 875  | 2.7270          |
| 2.5591        | 0.14  | 900  | 2.7272          |
| 2.4965        | 0.15  | 925  | 2.7205          |
| 2.6231        | 0.15  | 950  | 2.7195          |
| 2.5395        | 0.16  | 975  | 2.7162          |
| 2.5741        | 0.16  | 1000 | 2.7145          |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
