README.md

---
license: mit
datasets:
- keivalya/MedQuad-MedicalQnADataset
language:
- en
library_name: adapter-transformers
metrics:
- accuracy
- bertscore
- bleu
pipeline_tag: summarization
tags:
- medical
---

# K23 MiniMed 모델 카드

K23 MiniMed는 Krew x Huggingface 2023 해커톤에서 원형석 멘토의 지도하에 개발된 Mistral 7b Beta Medical Fine Tune 모델입니다.

## 모델 세부사항

- **개발자:** [Tonic](https://huggingface.co/Tonic)

- **후원:** [Tonic](https://huggingface.co/Tonic)
- **공유자:** K23-Krew-Hackathon
- **모델 유형:** Mistral 7B-Beta Medical Fine Tune 
- **언어 (NLP):** 영어
- **라이센스:** MIT
- **Fine-tuning 기반 모델:** [Zephyr 7B-Beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)

### 모델 출처
- **저장소:** [github](https://github.com/Josephrp/AI-challenge-hackathon/blob/master/mistral7b-beta_finetune.ipynb)
- **데모:** [pseudolab/K23MiniMed](https://huggingface.co/spaces/pseudolab/K23MiniMed)
## 사용법
이 모델은 교육 목적으로만 의학 질문 답변을 위한 대화형 애플리케이션용입니다.
### 직접 사용

Gradio 챗봇 앱을 만들어 의학적 질문을 하고 대화식으로 답변을 받습니다.

### 하류 사용

이 모델은 교육용으로만 사용됩니다. 추가적인 Fine-tuning과 사용 예시로는 공중 보건 & 위생, 개인 보건 & 위생, 의학 Q & A가 있습니다.

### 추천사항

사용 전에 항상 이 모델을 평가하고 벤치마킹하십시오. 사용 전에 편향을 평가하십시오. 그대로 사용하지 마시고 추가적으로 Fine-tuning하십시오.

## 훈련 세부사항

모델의 훈련 손실은 다음과 같습니다:
| 단계 | 훈련 손실 |
|------|--------------|
| 50   | 0.993800     |
| 100  | 0.620600     |
| 150  | 0.547100     |
| 200  | 0.524100     |
| 250  | 0.520500     |
| 300  | 0.559800     |
| 350  | 0.535500     |
| 400  | 0.505400     |
### 훈련 데이터
모델의 학습 가능한 매개변수: 21260288, 모든 매개변수: 3773331456, 학습 가능한 %: 0.5634354746703705.

### 결과

global_step=400에서의 훈련 손실은 0.6008514881134033입니다.

## 환경 영향

모델의 환경 영향은 머신러닝 영향 계산기를 사용하여 계산할 수 있습니다. 추정을 제공하기 위해서는 더 많은 세부 정보가 필요합니다.

## 기술 사양 

### 모델 아키텍처와 목표

모델은 특정 설정을 가진 PeftModelForCausalLM을 사용합니다.

### 컴퓨팅 인프라

#### 하드웨어

모델은 A100 하드웨어에서 훈련되었습니다.

#### 소프트웨어

사용된 소프트웨어에는 peft, torch, bitsandbytes, python, 그리고 huggingface가 포함됩니다.

## 모델 카드 작성자

[Tonic](https://huggingface.co/Tonic)

## 모델 카드 연락처

[Tonic](https://huggingface.co/Tonic)

# Model Card for K23 MiniMed

This is a Mistral 7b Beta Medical Fine Tune with a short number of steps , inspired by [Wonhyeong Seo](https://www.huggingface.co/wseo) great mentorship during Krew x Huggingface 2023 hackathon.

## Model Details

### Model Description

- **Developed by:** [Tonic](https://huggingface.co/Tonic)
- **Funded by [optional]:** [Tonic](https://huggingface.co/Tonic)
- **Shared by [optional]:** K23-Krew-Hackathon
- **Model type:** Mistral 7B-Beta Medical Fine Tune 
- **Language(s) (NLP):** English
- **License:** MIT
- **Finetuned from model [optional]:** [Zephyr 7B-Beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)

### Model Sources [optional]

- **Repository:** [github](https://github.com/Josephrp/AI-challenge-hackathon/blob/master/mistral7b-beta_finetune.ipynb)
- **Demo [optional]:** [pseudolab/K23MiniMed](https://huggingface.co/spaces/pseudolab/K23MiniMed)

## Uses

Use this model for conversational applications for medical question and answering **for educational purposes only** !

### Direct Use

Make a gradio chatbot app to ask medical questions and get answers conversationaly.

### Downstream Use [optional]

This model is **for educational use only** .

Further fine tunes and uses would include :

- public health & sanitation
- personal health & sanitation
- medical Q & A 

### Recommendations

- always evaluate this model before use
- always benchmark this model before use
- always evaluate bias before use
- do not use as is, fine tune further


## How to Get Started with the Model

Use the code below to get started with the model.


```Python

from transformers import AutoConfig, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM, MistralForCausalLM
from peft import PeftModel, PeftConfig
import torch
import gradio as gr
import random
from textwrap import wrap

# Functions to Wrap the Prompt Correctly
def wrap_text(text, width=90):
    lines = text.split('\n')
    wrapped_lines = [textwrap.fill(line, width=width) for line in lines]
    wrapped_text = '\n'.join(wrapped_lines)
    return wrapped_text

def multimodal_prompt(user_input, system_prompt="You are an expert medical analyst:"):
    # Combine user input and system prompt
    formatted_input = f"<s>[INST]{system_prompt} {user_input}[/INST]"

    # Encode the input text
    encodeds = tokenizer(formatted_input, return_tensors="pt", add_special_tokens=False)
    model_inputs = encodeds.to(device)

    # Generate a response using the model
    output = model.generate(
        **model_inputs,
        max_length=max_length,
        use_cache=True,
        early_stopping=True,
        bos_token_id=model.config.bos_token_id,
        eos_token_id=model.config.eos_token_id,
        pad_token_id=model.config.eos_token_id,
        temperature=0.1,
        do_sample=True
    )

    # Decode the response
    response_text = tokenizer.decode(output[0], skip_special_tokens=True)

    return response_text

# Define the device
device = "cuda" if torch.cuda.is_available() else "cpu"

# Use the base model's ID
base_model_id = "HuggingFaceH4/zephyr-7b-beta"
model_directory = "pseudolab/K23_MiniMed"

# Instantiate the Tokenizer
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", trust_remote_code=True, padding_side="left")
# tokenizer = AutoTokenizer.from_pretrained("Tonic/mistralmed", trust_remote_code=True, padding_side="left")
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = 'left'

# Specify the configuration class for the model
#model_config = AutoConfig.from_pretrained(base_model_id)

# Load the PEFT model with the specified configuration
#peft_model = AutoModelForCausalLM.from_pretrained(base_model_id, config=model_config)

# Load the PEFT model
peft_config = PeftConfig.from_pretrained("pseudolab/K23_MiniMed")
peft_model = MistralForCausalLM.from_pretrained("https://huggingface.co/HuggingFaceH4/zephyr-7b-beta", trust_remote_code=True)
peft_model = PeftModel.from_pretrained(peft_model, "pseudolab/K23_MiniMed")

class ChatBot:
    def __init__(self):
        self.history = []

class ChatBot:
    def __init__(self):
        # Initialize the ChatBot class with an empty history
        self.history = []

    def predict(self, user_input, system_prompt="You are an expert medical analyst:"):
        # Combine the user's input with the system prompt
        formatted_input = f"<s>[INST]{system_prompt} {user_input}[/INST]"

        # Encode the formatted input using the tokenizer
        user_input_ids = tokenizer.encode(formatted_input, return_tensors="pt")

        # Generate a response using the PEFT model
        response = peft_model.generate(input_ids=user_input_ids, max_length=512, pad_token_id=tokenizer.eos_token_id)

        # Decode the generated response to text
        response_text = tokenizer.decode(response[0], skip_special_tokens=True)
        
        return response_text  # Return the generated response

bot = ChatBot()

title = "👋🏻토닉의 미스트랄메드 채팅에 오신 것을 환영합니다🚀👋🏻Welcome to Tonic's MistralMed Chat🚀"
description = "이 공간을 사용하여 현재 모델을 테스트할 수 있습니다. [(Tonic/MistralMed)](https://huggingface.co/Tonic/MistralMed) 또는 이 공간을 복제하고 로컬 또는 🤗HuggingFace에서 사용할 수 있습니다. [Discord에서 함께 만들기 위해 Discord에 가입하십시오](https://discord.gg/VqTxc76K3u). You can use this Space to test out the current model [(Tonic/MistralMed)](https://huggingface.co/Tonic/MistralMed) or duplicate this Space and use it locally or on 🤗HuggingFace. [Join me on Discord to build together](https://discord.gg/VqTxc76K3u)."
examples = [["[Question:] What is the proper treatment for buccal herpes?", "You are a medicine and public health expert, you will receive a question, answer the question, and provide a complete answer"]]

iface = gr.Interface(
    fn=bot.predict,
    title=title,
    description=description,
    examples=examples,
    inputs=["text", "text"],  # Take user input and system prompt separately
    outputs="text",
    theme="ParityError/Anime"
)

iface.launch()

```

## Training Details

| Step | Training Loss |
|------|--------------|
| 50   | 0.993800     |
| 100  | 0.620600     |
| 150  | 0.547100     |
| 200  | 0.524100     |
| 250  | 0.520500     |
| 300  | 0.559800     |
| 350  | 0.535500     |
| 400  | 0.505400     |

### Training Data


```json

{trainable params: 21260288 || all params: 3773331456 || trainable%: 0.5634354746703705}

```

### Training Procedure 



#### Preprocessing [optional]

Lora32bits


#### Speeds, Sizes, Times [optional]

```json
 metrics={'train_runtime': 1700.1608, 'train_samples_per_second': 1.882, 'train_steps_per_second': 0.235, 'total_flos': 9.585300996096e+16, 'train_loss': 0.6008514881134033, 'epoch': 0.2})
```

### Results

```json
TrainOutput

global_step=400, training_loss=0.6008514881134033
```

#### Summary

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** {{ hardware | default("[More Information Needed]", true)}}
- **Hours used:** {{ hours_used | default("[More Information Needed]", true)}}
- **Cloud Provider:** {{ cloud_provider | default("[More Information Needed]", true)}}
- **Compute Region:** {{ cloud_region | default("[More Information Needed]", true)}}
- **Carbon Emitted:** {{ co2_emitted | default("[More Information Needed]", true)}}

## Technical Specifications 

### Model Architecture and Objective

```python

PeftModelForCausalLM(
  (base_model): LoraModel(
    (model): MistralForCausalLM(
      (model): MistralModel(
        (embed_tokens): Embedding(32000, 4096)
        (layers): ModuleList(
          (0-31): 32 x MistralDecoderLayer(
            (self_attn): MistralAttention(
              (q_proj): Linear4bit(
                (lora_dropout): ModuleDict(
                  (default): Dropout(p=0.05, inplace=False)
                )
                (lora_A): ModuleDict(
                  (default): Linear(in_features=4096, out_features=8, bias=False)
                )
                (lora_B): ModuleDict(
                  (default): Linear(in_features=8, out_features=4096, bias=False)
                )
                (lora_embedding_A): ParameterDict()
                (lora_embedding_B): ParameterDict()
                (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)
              )
              (k_proj): Linear4bit(
                (lora_dropout): ModuleDict(
                  (default): Dropout(p=0.05, inplace=False)
                )
                (lora_A): ModuleDict(
                  (default): Linear(in_features=4096, out_features=8, bias=False)
                )
                (lora_B): ModuleDict(
                  (default): Linear(in_features=8, out_features=1024, bias=False)
                )
                (lora_embedding_A): ParameterDict()
                (lora_embedding_B): ParameterDict()
                (base_layer): Linear4bit(in_features=4096, out_features=1024, bias=False)
              )
              (v_proj): Linear4bit(
                (lora_dropout): ModuleDict(
                  (default): Dropout(p=0.05, inplace=False)
                )
                (lora_A): ModuleDict(
                  (default): Linear(in_features=4096, out_features=8, bias=False)
                )
                (lora_B): ModuleDict(
                  (default): Linear(in_features=8, out_features=1024, bias=False)
                )
                (lora_embedding_A): ParameterDict()
                (lora_embedding_B): ParameterDict()
                (base_layer): Linear4bit(in_features=4096, out_features=1024, bias=False)
              )
              (o_proj): Linear4bit(
                (lora_dropout): ModuleDict(
                  (default): Dropout(p=0.05, inplace=False)
                )
                (lora_A): ModuleDict(
                  (default): Linear(in_features=4096, out_features=8, bias=False)
                )
                (lora_B): ModuleDict(
                  (default): Linear(in_features=8, out_features=4096, bias=False)
                )
                (lora_embedding_A): ParameterDict()
                (lora_embedding_B): ParameterDict()
                (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)
              )
              (rotary_emb): MistralRotaryEmbedding()
            )
            (mlp): MistralMLP(
              (gate_proj): Linear4bit(
                (lora_dropout): ModuleDict(
                  (default): Dropout(p=0.05, inplace=False)
                )
                (lora_A): ModuleDict(
                  (default): Linear(in_features=4096, out_features=8, bias=False)
                )
                (lora_B): ModuleDict(
                  (default): Linear(in_features=8, out_features=14336, bias=False)
                )
                (lora_embedding_A): ParameterDict()
                (lora_embedding_B): ParameterDict()
                (base_layer): Linear4bit(in_features=4096, out_features=14336, bias=False)
              )
              (up_proj): Linear4bit(
                (lora_dropout): ModuleDict(
                  (default): Dropout(p=0.05, inplace=False)
                )
                (lora_A): ModuleDict(
                  (default): Linear(in_features=4096, out_features=8, bias=False)
                )
                (lora_B): ModuleDict(
                  (default): Linear(in_features=8, out_features=14336, bias=False)
                )
                (lora_embedding_A): ParameterDict()
                (lora_embedding_B): ParameterDict()
                (base_layer): Linear4bit(in_features=4096, out_features=14336, bias=False)
              )
              (down_proj): Linear4bit(
                (lora_dropout): ModuleDict(
                  (default): Dropout(p=0.05, inplace=False)
                )
                (lora_A): ModuleDict(
                  (default): Linear(in_features=14336, out_features=8, bias=False)
                )
                (lora_B): ModuleDict(
                  (default): Linear(in_features=8, out_features=4096, bias=False)
                )
                (lora_embedding_A): ParameterDict()
                (lora_embedding_B): ParameterDict()
                (base_layer): Linear4bit(in_features=14336, out_features=4096, bias=False)
              )
              (act_fn): SiLUActivation()
            )
            (input_layernorm): MistralRMSNorm()
            (post_attention_layernorm): MistralRMSNorm()
          )
        )
        (norm): MistralRMSNorm()
      )
      (lm_head): Linear(
        in_features=4096, out_features=32000, bias=False
        (lora_dropout): ModuleDict(
          (default): Dropout(p=0.05, inplace=False)
        )
        (lora_A): ModuleDict(
          (default): Linear(in_features=4096, out_features=8, bias=False)
        )
        (lora_B): ModuleDict(
          (default): Linear(in_features=8, out_features=32000, bias=False)
        )
        (lora_embedding_A): ParameterDict()
        (lora_embedding_B): ParameterDict()
      )
    )
  )
)

```

### Compute Infrastructure

#### Hardware

A100

#### Software

peft , torch, bitsandbytes, python, huggingface

## Model Card Authors [optional]

[Tonic](https://huggingface.co/Tonic)

## Model Card Contact

[Tonic](https://huggingface.co/Tonic)