---
license: apache-2.0
base_model: mistralai/Mistral-7B-Instruct-v0.3
tags:
- generated_from_trainer
model-index:
- name: mistral_fine_out
  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. -->

[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.1`
```yaml
base_model: mistralai/Mistral-7B-Instruct-v0.3
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
is_mistral_derived_model: true

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - data_files: out/train.jsonl
    path: out/
    ds_type: json
    type:
      alpaca
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./mistral_fine_out

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

wandb_project:
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.000005

train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false

gradient_checkpointing: true
early_stopping_patience:
auto_resume_from_checkpoint: true
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 10
eval_steps: 0.05
eval_table_size:
eval_table_max_new_tokens: 128
save_steps:
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"
model_config:
  sliding_window: 4096

```

</details><br>

# mistral_fine_out

This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) on a synthetic appeals dataset.
See the [health insurance fine tuning repo](https://github.com/totallylegitco/healthinsurance-llm) for details.
An earlier [version of this dataset is availabile](https://huggingface.co/datasets/TotallyLegitCo/synthetic-appeals).

It achieves the following results on the evaluation set:
- Loss: 0.7984

## Model description

Generate health insurance appeals. Early work.


## Intended uses & limitations


It is intended to be used as part of the [fight health insurance web app](https://www.fighthealthinsurance.com/) [who's repo is at https://github.com/totallylegitco/fighthealthinsurance](https://github.com/totallylegitco/fighthealthinsurance)

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 2

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.0397        | 0.0004 | 1    | 1.1590          |
| 0.6084        | 0.1002 | 230  | 0.7272          |
| 0.5195        | 0.2003 | 460  | 0.7141          |
| 0.4713        | 0.3005 | 690  | 0.7090          |
| 0.3973        | 0.4007 | 920  | 0.7097          |
| 0.3306        | 0.5009 | 1150 | 0.7145          |
| 0.3507        | 0.6010 | 1380 | 0.7136          |
| 0.3125        | 0.7012 | 1610 | 0.7200          |
| 0.3055        | 0.8014 | 1840 | 0.7227          |
| 0.2027        | 0.9016 | 2070 | 0.7301          |
| 0.2632        | 1.0017 | 2300 | 0.7471          |
| 0.2077        | 1.0851 | 2530 | 0.7662          |
| 0.0992        | 1.1853 | 2760 | 0.7744          |
| 0.236         | 1.2855 | 2990 | 0.7844          |
| 0.1572        | 1.3857 | 3220 | 0.7915          |
| 0.192         | 1.4858 | 3450 | 0.7921          |
| 0.1812        | 1.5860 | 3680 | 0.7968          |
| 0.1973        | 1.6862 | 3910 | 0.7979          |
| 0.1422        | 1.7864 | 4140 | 0.7982          |
| 0.1315        | 1.8865 | 4370 | 0.7984          |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1