---
license: apache-2.0
base_model: mistralai/Mistral-7B-Instruct-v0.2
tags:
- generated_from_trainer
model-index:
- name: mistral_fine_out
  results: []
---

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

axolotl version: `0.3.0`
```yaml
base_model: mistralai/Mistral-7B-Instruct-v0.2
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
is_mistral_derived_model: true

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: out/train_alpaca.jsonl
    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: 4
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>

The fine tuning script used for launch was from https://github.com/totallylegitco/healthinsurance-llm w/ run_remote.sh and an INPUT_MODEL=mistral


# TotallyLegitCo/fighthealthinsurance_model_v0.3

This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co./mistralai/Mistral-7B-Instruct-v0.2) on the [syntehtic-appeal](https://huggingface.co./datasets/TotallyLegitCo/synthetic-appeals) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3954

## Model description

Generate health insurance appeals. Early work.

## Intended uses & limitations

Generate health insurance appeals. This is early work and may not be suitable for production.

## Training and evaluation data

The syntehtic appeal dataset was used for training and evaluation. Given how the dataset was produced there is likely cross-contamination of the training and eval datasets so loss values are likely understated.

This model is intended to match the Mistral-7B-Instruct style with ```<s>[INST]Instructions[/INT]``` present (as well as system specific instructions within an extra ```<<SYS><</SYS>```.

## 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: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0506        | 0.0   | 1    | 2.4510          |
| 0.8601        | 0.2   | 58   | 1.1493          |
| 0.8635        | 0.4   | 116  | 1.1356          |
| 0.869         | 0.61  | 174  | 1.1174          |
| 0.7764        | 0.81  | 232  | 1.1173          |
| 0.7803        | 1.01  | 290  | 1.1124          |
| 0.6902        | 1.2   | 348  | 1.1570          |
| 0.6774        | 1.4   | 406  | 1.1591          |
| 0.6859        | 1.6   | 464  | 1.1651          |
| 0.725         | 1.81  | 522  | 1.1677          |
| 0.6525        | 2.01  | 580  | 1.1686          |
| 0.5069        | 2.2   | 638  | 1.2688          |
| 0.4702        | 2.4   | 696  | 1.2767          |
| 0.4888        | 2.6   | 754  | 1.2852          |
| 0.5197        | 2.8   | 812  | 1.2881          |
| 0.4734        | 3.01  | 870  | 1.2851          |
| 0.3586        | 3.2   | 928  | 1.3856          |
| 0.3889        | 3.4   | 986  | 1.3929          |
| 0.3526        | 3.6   | 1044 | 1.3959          |
| 0.3832        | 3.8   | 1102 | 1.3954          |


### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 2.0.1
- Datasets 2.16.1
- Tokenizers 0.15.0