---
base_model: TheBloke/Mistral-7B-Instruct-v0.2-GPTQ
library_name: peft
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: DocGPT-ft
  results: []
datasets:
- lavita/ChatDoctor-HealthCareMagic-100k
---

<!-- 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. -->

# DocGPT-ft

This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.2-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ) on the lavita/ChatDoctor-HealthCareMagic-100k dataset.


## Model description

Uses parameter efficient fine-tuning for QLora

## Intended uses & limitations

The intended use is just for fun. 

## Training and evaluation data

The training set was 90% of the data and testing set was 10%. Only a small percentage of the data was used to reduce training time.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.4174        | 0.9412 | 12   | 2.2924          |
| 2.1327        | 1.9608 | 25   | 2.2750          |
| 2.0864        | 2.9804 | 38   | 2.2745          |
| 2.0362        | 4.0    | 51   | 2.2761          |
| 2.1357        | 4.9412 | 63   | 2.2849          |
| 1.942         | 5.9608 | 76   | 2.2961          |
| 1.8904        | 6.9804 | 89   | 2.3165          |
| 1.8585        | 8.0    | 102  | 2.3295          |
| 1.9923        | 8.9412 | 114  | 2.3390          |
| 1.6331        | 9.4118 | 120  | 2.3387          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1