metadata

library_name: transformers
tags:
  - text-generation-inference
  - sft
  - trl
  - 4-bit precision
  - bitsandbytes
  - LoRA
  - Fine-Tuning with LoRA
  - LLM
  - GenAI
  - NT GenAI
  - ntgenai
  - lahnmah
  - NT Thai GPT
  - ntthaigpt
datasets:
  - Thaweewat/thai-med-pack
language:
  - th
base_model:
  - openthaigpt/openthaigpt1.5-7b-instruct
pipeline_tag: text-generation
license: apache-2.0
new_version: Aekanun/openthaigpt-MedChatModelv5.1

Model Card for `openthaigpt1.5-7b-medical-tuned`

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 Details

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.

Developed by: Amornpan Phornchaicharoen
Fine-tuned by: Amornpan Phornchaicharoen
Model type: Causal Language Model (AutoModelForCausalLM)
Language(s): Thai
License: Amornpan Phornchaicharoen
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.

How to Get Started with the Model

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

1. Install the Required Packages

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

pip install torch transformers bitsandbytes

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 (if available)

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=200, 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] [ANS] ช่องปากมะเร็งในระยะเริ่มต้น อาจไม่มีอาการชัดเจน แต่ผู้คนบางกลุ่มอาจสังเกตเห็นอาการต่อไปนี้:

- มีการกัดหรือกระแทกบริเวณช่องปากโดยไม่มีสาเหตุ
- มีจุด ฝี เมล็ด หรือความไม่เท่าเทียมภายในช่องปากที่ไม่หายวื้อ
- ปวดหรือเจ็บบริเวณช่องปาก
- เปลี่ยนแปลงสีของเนื้อเยื่อในช่องปาก (อาจเป็นสีขาว หรือ黑马)
- มีตุ่มที่ไม่หาย ภายในช่องปาก
- มีความลำบากในการกิน มี

More Information

amornpan@gmail.com

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