README.md

---
model_name: mistralmed
library_name: peft
base_model: mistralai/Mistral-7B-v0.1
license: mit
datasets:
- keivalya/MedQuad-MedicalQnADataset
language:
- en
tags:
- medical
---

# Model Card for Tonic/MistralMed

This is a medicine-focussed mistral fine tuned using keivalya/MedQuad-MedicalQnADataset


## Model Details

### Model Description

Trying to get better at medical Q & A


- **Developed by:** [Tonic](https://huggingface.co/Tonic)
- **Shared by [optional]:** [Tonic](https://huggingface.co/Tonic)
- **Model type:** Mistral Fine-Tune
- **Language(s) (NLP):** English
- **License:** MIT2.0
- **Finetuned from model [optional]:** [mistralai/Mistral-7B-v0.1](https://huggingface.com/Mistralai/Mistral-7B-v0.1)

### Model Sources [optional]


- **Repository:** [Tonic/mistralmed](https://huggingface.co/Tonic/mistralmed)
- **Code :** [github](https://github.com/Josephrp/mistralmed/blob/main/finetuning.py)
- **Demo :** [Tonic/MistralMed_Chat](https://huggingface.co/Tonic/MistralMed_Chat)

## Uses

This model can be used the same way you normally use mistral

### Direct Use

This model can do better in medical question and answer scenarios.

### Downstream Use [optional]

This model is intended to be further fine tuned.

### Recommendations

- Do Not Use As Is
- Fine Tune This Model Further
- For Educational Purposes Only
- Benchmark your model usage
- Evaluate the model before use

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

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

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

# 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:"):
    """
    Generates text using a large language model, given a user input and a system prompt.
    Args:
        user_input: The user's input text to generate a response for.
        system_prompt: Optional system prompt.
    Returns:
        A string containing the generated text.
    """
    # 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 = "mistralai/Mistral-7B-v0.1"
model_directory = "Tonic/mistralmed"

# 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("Tonic/mistralmed", token="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")
peft_model = MistralForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", trust_remote_code=True)
peft_model = PeftModel.from_pretrained(peft_model, "Tonic/mistralmed", token="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")

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

    def predict(self, 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 user input
        user_input_ids = tokenizer.encode(formatted_input, return_tensors="pt")

        # Concatenate the user input with chat history
        if len(self.history) > 0:
            chat_history_ids = torch.cat([self.history, user_input_ids], dim=-1)
        else:
            chat_history_ids = user_input_ids

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

        # Update chat history
        self.history = chat_history_ids

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

bot = ChatBot()

title = "👋🏻Welcome to Tonic's MistralMed Chat🚀"
description = "You can use this Space to test out the current model (MistralMed) or duplicate this Space and use it for any other model on 🤗HuggingFace. Join me on Discord to build together."
examples = [["What is the proper treatment for buccal herpes?", "Please provide information on the most effective antiviral medications and home remedies for treating buccal herpes."]]

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

### Training Data

[MedQuad](https://huggingface.co/datasets/keivalya/MedQuad-MedicalQnADataset/viewer/default/train)

### Training Procedure 

Dataset({
    features: ['qtype', 'Question', 'Answer'],
    num_rows: 16407
})


#### Preprocessing [optional]

MistralForCausalLM(
  (model): MistralModel(
    (embed_tokens): Embedding(32000, 4096)
    (layers): ModuleList(
      (0-31): 32 x MistralDecoderLayer(
        (self_attn): MistralAttention(
          (q_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)
          (k_proj): Linear4bit(in_features=4096, out_features=1024, bias=False)
          (v_proj): Linear4bit(in_features=4096, out_features=1024, bias=False)
          (o_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)
          (rotary_emb): MistralRotaryEmbedding()
        )
        (mlp): MistralMLP(
          (gate_proj): Linear4bit(in_features=4096, out_features=14336, bias=False)
          (up_proj): Linear4bit(in_features=4096, out_features=14336, bias=False)
          (down_proj): 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)
)


#### Training Hyperparameters

- **Training regime:** 
config = LoraConfig(
    r=8,
    lora_alpha=16,
    target_modules=[
        "q_proj",
        "k_proj",
        "v_proj",
        "o_proj",
        "gate_proj",
        "up_proj",
        "down_proj",
        "lm_head",
    ],
    bias="none",
    lora_dropout=0.05,  # Conventional
    task_type="CAUSAL_LM",
)

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

- trainable params: 21260288 || all params: 3773331456 || trainable%: 0.5634354746703705
- TrainOutput(global_step=1000, training_loss=0.47226515007019043, metrics={'train_runtime': 3143.4141, 'train_samples_per_second': 2.545, 'train_steps_per_second': 0.318, 'total_flos': 1.75274075357184e+17, 'train_loss': 0.47226515007019043, 'epoch': 0.49})


## 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:** A100
- **Hours used:** 1
- **Cloud Provider:** Google
- **Compute Region:** East1
- **Carbon Emitted:** 0.09

## Training Results

 [1000/1000 52:20, Epoch 0/1]
 
| Step  | Training Loss |
|-------|--------------|
| 50    | 0.474200     |
| 100   | 0.523300     |
| 150   | 0.484500     |
| 200   | 0.482800     |
| 250   | 0.498800     |
| 300   | 0.451800     |
| 350   | 0.491800     |
| 400   | 0.488000     |
| 450   | 0.472800     |
| 500   | 0.460400     |
| 550   | 0.464700     |
| 600   | 0.484800     |
| 650   | 0.474600     |
| 700   | 0.477900     |
| 750   | 0.445300     |
| 800   | 0.431300     |
| 850   | 0.461500     |
| 900   | 0.451200     |
| 950   | 0.470800     |
| 1000  | 0.454900     |


### Model Architecture and Objective

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()
      )
    )
  )
)
#### Hardware

A100



## Model Card Authors [optional]

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

## Model Card Contact

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


## Training procedure


The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16

### Framework versions


- PEFT 0.6.0.dev0