---
license: apache-2.0
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: mistralai/Mistral-7B-v0.1
model-index:
- name: mimic3-mistral-7B-v0.1
  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/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: `0.4.0`
```yaml
base_model: mistralai/Mistral-7B-v0.1
hub_model_id: chaosIsRythmic/mimic3-mistral-7B-v0.1

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  # This will be the path used for the data when it is saved to the Volume in the cloud.
  - path: data.jsonl
    ds_type: json
    type:
      # JSONL file contains question, context, answer fields per line.
      # This gets mapped to instruction, input, output axolotl tags.
      field_instruction: question
      field_input: context
      field_output: answer
      # Format is used by axolotl to generate the prompt.
      format: |-
        [INST] Using the medical notes below, assign the right ICD-9 codes.
        {input}
        {instruction} [/INST]

tokens: # add new control tokens from the dataset to the model
  - "[INST]"
  - " [/INST]"
  - "[SQL]"
  - " [/SQL]"

dataset_prepared_path: last_run_prepared
val_set_size: 0.2
output_dir: ./lora-out

sequence_len: 4096
sample_packing: false
eval_sample_packing: false
pad_to_sequence_len: false
pad_to_sequence_len: true

adapter: lora
lora_model_dir:
lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_modules_to_save: # required when adding new tokens to LLaMA/Mistral
  - embed_tokens
  - lm_head

wandb_project:  mimic3
wandb_entity:
wandb_watch:
wandb_run_id:

loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3

gradient_accumulation_steps: 1
micro_batch_size: 6
num_epochs: 6
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.0001

bf16: auto
fp16: false
tf32: false
train_on_inputs: false
group_by_length: false

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

warmup_steps: 10
saves_per_epoch: 1
evals_per_epoch: 4
eval_max_new_tokens: 128
debug:
deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"

```

</details><br>

# mimic3-mistral-7B-v0.1

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: 0.6757

## 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: 0.0001
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 12
- total_eval_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 6

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.9923        | 0.0013 | 1    | 2.1006          |
| 0.3728        | 0.2506 | 200  | 0.3790          |
| 0.3122        | 0.5013 | 400  | 0.3571          |
| 0.305         | 0.7519 | 600  | 0.3203          |
| 0.2929        | 1.0025 | 800  | 0.3158          |
| 0.2873        | 1.2531 | 1000 | 0.3000          |
| 0.2654        | 1.5038 | 1200 | 0.2971          |
| 0.3343        | 1.7544 | 1400 | 0.2846          |
| 0.2272        | 2.0050 | 1600 | 0.2901          |
| 0.1976        | 2.2556 | 1800 | 0.2900          |
| 0.2315        | 2.5063 | 2000 | 0.2829          |
| 0.1913        | 2.7569 | 2200 | 0.2852          |
| 0.2578        | 3.0075 | 2400 | 0.2809          |
| 0.1614        | 3.2581 | 2600 | 0.3104          |
| 0.1526        | 3.5088 | 2800 | 0.3171          |
| 0.1712        | 3.7594 | 3000 | 0.3042          |
| 0.1016        | 4.0100 | 3200 | 0.3367          |
| 0.0658        | 4.2607 | 3400 | 0.4388          |
| 0.0636        | 4.5113 | 3600 | 0.4601          |
| 0.0534        | 4.7619 | 3800 | 0.4398          |
| 0.0363        | 5.0125 | 4000 | 0.4785          |
| 0.0016        | 5.2632 | 4200 | 0.6498          |
| 0.0183        | 5.5138 | 4400 | 0.6769          |
| 0.0185        | 5.7644 | 4600 | 0.6757          |


### Framework versions

- PEFT 0.10.0
- Transformers 4.40.2
- Pytorch 2.2.2+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1