---
base_model: UCLAML/mistral-7b-expert-iteration-iter3
datasets:
- synthetic_data_mistral-7b-instruct-expert-iteration-iter3_score
tags:
- alignment-handbook
- generated_from_trainer
- autoquant
- gptq
model-index:
- name: UCLAML/mistral-7b-expert-iteration-iter3
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Mistral-7B-Instruct-EI-Iter3

This model is a GPTQ version of [UCLAML/mistral-7b-expert-iteration-iter3](UCLAML/mistral-7b-expert-iteration-iter3) 

Created with [AutoQuant](https://colab.research.google.com/drive/1b6nqC7UZVt8bx4MksX7s656GXPM-eWw4?usp=sharing)

## Model description

I like the GPTQ format, this is 8bit, GROUP_SIZE 32.

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1.0

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6652        | 1.0   | 106  | 0.4722          |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.14.6
- Tokenizers 0.19.1