---
base_model:
- openthaigpt/openthaigpt1.5-7b-instruct
datasets:
- Thaweewat/thai-med-pack
language:
- th
- en
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
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
- medical
- medtech
- HealthGPT
new_version: Aekanun/openthaigpt-MedChatModelv5.1
---
# 🇹🇭 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.
## 👤 **Developed and Fine-tuned by:**
- **Amornpan Phornchaicharoen**
- **Aekanun Thongtae**
## 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:
## 1. Install the Required Packages
First, ensure you have installed the required libraries by running:
```python
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:
```python
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:
```python
custom_prompt = "โปรดอธิบายลักษณะช่องปากที่เป็นมะเร็งในระยะเริ่มต้น"
PROMPT = f'[INST] {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:
```python
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:
```python
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:
```python
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:
```python
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:
```python
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)
```
## 9. Output
```python
[INST]
โปรดอธิบายลักษณะช่องปากที่เป็นมะเร็งในระยะเริ่มต้น[/INST]
<ช่องปากที่เป็นมะเร็งในระยะเริ่มต้นอาจจะไม่มีอาการรุนแรง แต่บางครั้งก็อาจจะมีลักษณะต่อไปนี้:
1. ตุ่มน้ำลายหรือแผลที่ไม่หายซึ่งคงอยู่นานกว่า 2 สัปดาห์
2. ความเสียหายหรือเปลี่ยนแปลงของช่องปากที่เกิดขึ้นอย่างช้าๆ เช่น แต้มขนาดเล็ก เจล หรือลักษณะของชิ้นผิวที่เปลี่ยนแปลง
3. ความยากในการกัดนิ้ว คำ หรืออาหาร
4. บวมหรือเปลี่ยนแปลงของเยื่อบุในช่องปาก
5. คำที่หายไปจากชั้นหรือแกลบต่างๆ
6. ปัญหาในการให้อาหาร ตัวอย่างเช่น การเคี้ยวอาหารและกลืน
7. เข้าใกล้ริม
```
### Authors
* Amornpan Phornchaicharoen (amornpan@gmail.com)
* Aekanun Thongtae (cto@bangkokfirsttech.com)
* Montita Somsoo (montita.fonn@gmail.com)
* Jiranuwat Songpad (jiranuwat.song64@gmail.com)
* Phongsatorn Somjai (ipongdev@gmail.com)
* Benchawan Wangphoomyai (benchaa.27@gmail.com)