amornpan/openthaigpt-MedChatModelv11

🇹🇭 Model Card for openthaigpt1.5-7b-medical-tuned

ℹ️ This version is optimized for GPU. Please wait for the CPU version, which will be available soon.!!

This model is fine-tuned from openthaigpt1.5-7b-instruct using Supervised Fine-Tuning (SFT) on the Thaweewat/thai-med-pack dataset. The model is designed for medical question-answering tasks in Thai, specializing in providing accurate and contextual answers based on medical information.

Model Description

This model was fine-tuned using Supervised Fine-Tuning (SFT) to optimize it for medical question answering in Thai. The base model is openthaigpt1.5-7b-instruct, and it has been enhanced with domain-specific knowledge using the Thaweewat/thai-med-pack dataset.

• Model type: Causal Language Model (AutoModelForCausalLM)
• Language(s): Thai
• License: Apache License 2.0
• Fine-tuned from model: openthaigpt1.5-7b-instruct
• Dataset used for fine-tuning: Thaweewat/thai-med-pack

Model Sources

• Repository: https://huggingface.co/amornpan
• Citing Repository: https://huggingface.co/Aekanun
• Base Model: https://huggingface.co/openthaigpt/openthaigpt1.5-7b-instruct
• Dataset: https://huggingface.co/datasets/Thaweewat/thai-med-pack

Uses

Direct Use

The model can be directly used for generating medical responses in Thai. It has been optimized for:

• Medical question-answering
• Providing clinical information
• Health-related dialogue generation

Downstream Use

This model can be used as a foundational model for medical assistance systems, chatbots, and applications related to healthcare, specifically in the Thai language.

Out-of-Scope Use

• This model should not be used for real-time diagnosis or emergency medical scenarios.
• Avoid using it for critical clinical decisions without human oversight, as the model is not intended to replace professional medical advice.

Bias, Risks, and Limitations

Bias

• The model might reflect biases present in the dataset, particularly when addressing underrepresented medical conditions or topics.

Risks

• Responses may contain inaccuracies due to the inherent limitations of the model and the dataset used for fine-tuning.
• This model should not be used as the sole source of medical advice.

Limitations

• Limited to the medical domain.
• The model is sensitive to prompts and may generate off-topic responses for non-medical queries.

Model Training Results:

How to Get Started with the Model

Here’s how to load and use the model for generating medical responses in Thai:

Using Google Colab Pro or Pro+ for fine-tuning and inference.

1. Install the Required Packages

First, ensure you have installed the required libraries by running:

pip install torch transformers bitsandbytes

[!pip install bitsandbytes --upgrade]

[!pip install --upgrade transformers huggingface_hub]

2. Load the Model and Tokenizer

You can load the model and tokenizer directly from Hugging Face using the following code:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

Define the model path

model_path = 'amornpan/openthaigpt-MedChatModelv11'

Load the tokenizer and model

tokenizer = AutoTokenizer.from_pretrained(model_path) tokenizer.pad_token = tokenizer.eos_token

3. Prepare Your Input (Custom Prompt)

Create a custom medical prompt that you want the model to respond to:

custom_prompt = "โปรดอธิบายลักษณะช่องปากที่เป็นมะเร็งในระยะเริ่มต้น"
PROMPT = f'[INST] <You are a question answering assistant. Answer the question as truthfully and helpfully as possible. คุณคือผู้ช่วยตอบคำถาม จงตอบคำถามอย่างถูกต้องและมีประโยชน์ที่สุด<>{custom_prompt}[/INST]'

# Tokenize the input prompt
inputs = tokenizer(PROMPT, return_tensors="pt", padding=True, truncation=True)

4. Configure the Model for Efficient Loading (4-bit Quantization)

The model uses 4-bit precision for efficient inference. Here’s how to set up the configuration:

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16
)

5. Load the Model with Quantization Support

Now, load the model with the 4-bit quantization settings:

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    quantization_config=bnb_config,
    trust_remote_code=True
)

6. Move the Model and Inputs to the GPU (prefer GPU)

For faster inference, move the model and input tensors to a GPU, if available:

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
inputs = {k: v.to(device) for k, v in inputs.items()}

7. Generate a Response from the Model

Now, generate the medical response by running the model:

outputs = model.generate(**inputs, max_new_tokens=256, do_sample=True)

8. Decode the Generated Text

Finally, decode and print the response from the model:

generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

9. Output

[INST] <You are a question answering assistant. Answer the question as truthfully and helpfully as possible. 
คุณคือผู้ช่วยตอบคำถาม จงตอบคำถามอย่างถูกต้องและมีประโยชน์ที่สุด<>โปรดอธิบายลักษณะช่องปากที่เป็นมะเร็งในระยะเริ่มต้น[/INST] 
<ช่องปากที่เป็นมะเร็งในระยะเริ่มต้นอาจมีลักษณะต่อไปนี้:

- มีเนื้องอกสีขาวหรือสีเทามีขนาดเล็กอยู่บริเวณเยื่อบุช่องปาก
- มีแผลในช่องปากที่ไม่หายภายในสองสัปดาห์
- มีแผลบริเวณจมูกหรือคอที่มีมานานแต่ไม่หาย
- มีเนื้อ hardness หรือการเปลี่ยนแปลงทางโครงสร้างในบริเวณเยื่อบุของช่องปาก
- มีความผิดปกติในรูปร่าง ขนาด และสีของฟัน
- มีการเปลี่ยนแปลงในการบิดงอของลิ้นหรือมัดกล้ามเนื้อที่รับผิดชอบการบิดงอ

สิ่งจำเป็นคือให้พบแพทย์ผู้เชี่ยวชาญโดยเร็วที่สุดหากมีอาการที่

👤 Authors

• Amornpan Phornchaicharoen ([email protected])
• Aekanun Thongtae ([email protected])
• Montita Somsoo ([email protected])
• Jiranuwat Songpad ([email protected])
• Phongsatorn Somjai ([email protected])
• Benchawan Wangphoomyai ([email protected])