transformers safetensors internlm2 feature-extraction llama-factory full generated_from_trainer custom_code zh en arxiv:2407.19705 base_model:internlm/internlm2_5-7b base_model:finetune:internlm/internlm2_5-7b license:cc-by-sa-4.0 region:us
---
base_model: internlm/internlm2_5-7b
language:
- zh
- en
license: cc-by-sa-4.0
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: CollectiveSFT
  results: []
---


# CollectiveSFT

This model is a fine-tuned version of [internlm/internlm2_5-7b](https://huggingface.co/internlm/internlm2_5-7b) on some medical datasets.

## Model description

[CollectiveSFT](https://arxiv.org/abs/2407.19705): Scaling Large Language Models for Chinese Medical Benchmark with Collective Instructions in Healthcare.

Official Code Repo:[https://github.com/CAS-SIAT-XinHai/CollectiveSFT](https://github.com/CAS-SIAT-XinHai/CollectiveSFT)

## Intended uses & limitations

The model may have limitations in chat functionality.

## Training and evaluation data

**Language: English**
| Dataset Name | Style | Size     |
|:--------------:|:-------:|:----------:|
| PubMedQA | QA    | 273,518  |
| MedMCQA| MCQA  | 182,822  |
| HeadQA  | QA    | 2,657    |
| **Total**    |       | 458,997  |

**Language: Chinese**
| Dataset Name     | Style    | Size     |
|:------------------:|:----------:|:----------:|
| cMedQA2          | QA       | 100,000  |
| cMedDialogu | Dialogue | 792,099  |
| webMedQA    | QA       | 252,850  |
| MedicalDialog| Dialogue| 2,725,989|
| CMID         | NER      | 12,254   |
| NLPEC       | MCQA     | 18,703   |
| CMB         | MCQA     | 269,359  |
| MLEC-QA     | MCQA     | 108,988  |
| DISCMe      | Dialogue | 464,898  |
| **Total**   |          | 4,745,140|

For detailed dataset specifications and access instructions, please refer to our paper.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 3.0

### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1