README.md

---
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`

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<!-- Provide a quick summary of what the model is/does. -->
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:

```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] <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:

```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 (if available)

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=200, 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] <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```