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library_name: transformers
tags: []
---

# 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:** [Link to your Hugging Face model repository]
- **Base Model:** [Link to `openthaigpt1.5-7b-instruct` repository]
- **Dataset:** [Link to `Thaweewat/thai-med-pack` repository]

## 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:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the fine-tuned model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("amornpan/openthaigpt-MedChatModelv11")
model = AutoModelForCausalLM.from_pretrained("amornpan/openthaigpt-MedChatModelv11")

# Input your medical question or prompt in Thai
input_text = "ใส่คำถามทางการแพทย์ที่นี่"
inputs = tokenizer(input_text, return_tensors="pt")

# Generate the output with a higher max length or max new tokens
output = model.generate(**inputs, max_new_tokens=100, do_sample=True, temperature=0.7)

# Decode and print the generated response, skipping special tokens
print(tokenizer.decode(output[0], skip_special_tokens=True))

```
โปรดอธิบายลักษณะช่องปากที่เป็นมะเร็งในระยะเริ่มต้น

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

### More Information
```amornpan@gmail.com```