Text Generation
Transformers
Safetensors
Thai
English
qwen2
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
หลานม่า
NT Academy
conversational
Inference Endpoints
4-bit precision
| library_name: transformers | |
| tags: | |
| - text-generation-inference | |
| - sft | |
| - trl | |
| - 4-bit precision | |
| - bitsandbytes | |
| - LoRA | |
| - Fine-Tuning with LoRA | |
| - LLM | |
| - NT-GenAI | |
| - lahnmah | |
| datasets: | |
| - Thaweewat/thai-med-pack | |
| language: | |
| - th | |
| base_model: | |
| - openthaigpt/openthaigpt1.5-7b-instruct | |
| pipeline_tag: text-generation | |
| # 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``` |