---
license: apache-2.0
base_model: mistralai/Mistral-7B-Instruct-v0.2
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: UTI_M2_1000steps_1e8rate_SFT
  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. -->

# UTI_M2_1000steps_1e8rate_SFT

This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co./mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6170

## 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-08
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1000

### Training results

| Training Loss | Epoch   | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 2.6263        | 0.3333  | 25   | 2.6302          |
| 2.6415        | 0.6667  | 50   | 2.6306          |
| 2.6336        | 1.0     | 75   | 2.6298          |
| 2.6804        | 1.3333  | 100  | 2.6284          |
| 2.5885        | 1.6667  | 125  | 2.6291          |
| 2.542         | 2.0     | 150  | 2.6286          |
| 2.6601        | 2.3333  | 175  | 2.6249          |
| 2.6936        | 2.6667  | 200  | 2.6240          |
| 2.6593        | 3.0     | 225  | 2.6238          |
| 2.6088        | 3.3333  | 250  | 2.6237          |
| 2.542         | 3.6667  | 275  | 2.6208          |
| 2.5635        | 4.0     | 300  | 2.6185          |
| 2.6825        | 4.3333  | 325  | 2.6183          |
| 2.5987        | 4.6667  | 350  | 2.6198          |
| 2.6421        | 5.0     | 375  | 2.6196          |
| 2.6426        | 5.3333  | 400  | 2.6183          |
| 2.6517        | 5.6667  | 425  | 2.6189          |
| 2.5762        | 6.0     | 450  | 2.6190          |
| 2.6801        | 6.3333  | 475  | 2.6190          |
| 2.5918        | 6.6667  | 500  | 2.6169          |
| 2.6354        | 7.0     | 525  | 2.6163          |
| 2.6433        | 7.3333  | 550  | 2.6156          |
| 2.7024        | 7.6667  | 575  | 2.6172          |
| 2.575         | 8.0     | 600  | 2.6176          |
| 2.6109        | 8.3333  | 625  | 2.6165          |
| 2.6088        | 8.6667  | 650  | 2.6195          |
| 2.698         | 9.0     | 675  | 2.6185          |
| 2.612         | 9.3333  | 700  | 2.6166          |
| 2.615         | 9.6667  | 725  | 2.6188          |
| 2.681         | 10.0    | 750  | 2.6165          |
| 2.6118        | 10.3333 | 775  | 2.6190          |
| 2.5875        | 10.6667 | 800  | 2.6168          |
| 2.6859        | 11.0    | 825  | 2.6170          |
| 2.5953        | 11.3333 | 850  | 2.6171          |
| 2.5884        | 11.6667 | 875  | 2.6169          |
| 2.6721        | 12.0    | 900  | 2.6170          |
| 2.5951        | 12.3333 | 925  | 2.6170          |
| 2.6112        | 12.6667 | 950  | 2.6170          |
| 2.5842        | 13.0    | 975  | 2.6170          |
| 2.5962        | 13.3333 | 1000 | 2.6170          |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.0.0+cu117
- Datasets 2.19.2
- Tokenizers 0.19.1