Update README.md

README.md

@@ -1,202 +1,209 @@
 ---
-base_model: Qwen/Qwen2.5-32B-Instruct
-library_name: peft
+base_model:
+- Qwen/Qwen2.5-32B-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
+- medical
+- medtech
+- HealthGPT
+- minddatatech.com
+- NT Academy
+new_version: amornpan/openthaigpt-MedChatModelv11
 ---
 
-# Model Card for Model ID
-
+# 🇹🇭 **Model Card for Qwen2.5-32B-Instruct-medical-tuned**
 <!-- Provide a quick summary of what the model is/does. -->
 
+## <font color="blue">ℹ️ This version is significantly better than OpenThaiGPT!.</font>
 
+## Qwen2.5-32B-Instruct for Thai Medical QA
 
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
+This model is fine-tuned from `Qwen2.5-32B-Instruct` using Supervised Fine-Tuning (SFT) on the `Thaweewat/thai-med-pack` dataset. It is designed for medical question-answering tasks in Thai, providing accurate and contextual answers based on medical information.
 
+## Model Description
+This model was fine-tuned using Supervised Fine-Tuning (SFT) to enhance its capabilities for medical question answering in Thai. The base model is `Qwen2.5-32B-Instruct`, which has been optimized 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:** Qwen2.5-32B-Instruct
+- **Dataset used for fine-tuning:** Thaweewat/thai-med-pack
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+### Model Sources
 
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** https://huggingface.co/amornpan
+- **Citing Repository:** https://huggingface.co/Aekanun
+- **Base Model:** https://huggingface.co/Qwen/Qwen2.5-32B-Instruct
+- **Dataset:** https://huggingface.co/datasets/Thaweewat/thai-med-pack
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ### Direct Use
+The model can be used directly for generating medical responses in Thai. It has been optimized for:
+- Medical question-answering
+- Providing clinical information
+- Health-related dialogue generation
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+### Downstream Use
+This model serves as a foundational model for medical assistance systems, chatbots, and applications related to healthcare in the Thai language.
 
 ### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+- This model should not be used for real-time diagnosis or emergency medical scenarios.
+- It should not be relied upon for critical clinical decisions without human oversight, as it is not intended to replace professional medical advice.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+### Bias
+- The model may reflect biases present in the dataset, especially regarding underrepresented medical conditions or topics.
 
-[More Information Needed]
+### Risks
+- Responses may contain inaccuracies due to the model's inherent limitations and the dataset used for fine-tuning.
+- The model should not be used as the sole source of medical advice.
 
-### Recommendations
+### Limitations
+- Primarily limited to the medical domain.
+- Sensitive to prompts and may generate off-topic responses for non-medical queries.
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+## Model Training Results:
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/663ce15f197afc063058dc3a/umzKEBp8lxBCp4nEieIIl.png)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/663ce15f197afc063058dc3a/Z0pU0MVz4AhSq3B5dT_fn.png)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/663ce15f197afc063058dc3a/c_lqB3jiJl_Os-l7j-7NB.png)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/663ce15f197afc063058dc3a/IwyhjvmDO5WdQZvZEWT9J.png)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/663ce15f197afc063058dc3a/gXjnNDSPw01VEWTBbr-2Z.png)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/663ce15f197afc063058dc3a/V5WPYa27EOiEcxelgBJEd.png)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/663ce15f197afc063058dc3a/KKM2qxjbnsu-ixImTWJgu.png)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/663ce15f197afc063058dc3a/sTE-lYpLR9YLG3b8OdCdt.png)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/663ce15f197afc063058dc3a/jYV5qz_ZPFvZW7P-Q1BGy.png)
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
+This section provides a step-by-step guide to loading and using the model for generating medical responses in Thai.
 
-<!-- Relevant interpretability work for the model goes here -->
+### 1. Install the Required Packages
 
-[More Information Needed]
+Ensure that you have installed the required libraries:
 
-## Environmental Impact
+```python
+pip install torch transformers accelerate bitsandbytes
+pip install --upgrade transformers huggingface_hub
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+[!pip install bitsandbytes --upgrade]
 
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+[!pip install --upgrade transformers huggingface_hub]
+```
+## 2. Load the Model and Tokenizer
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+You can load the model and tokenizer directly from Hugging Face using the following code:
 
-## Technical Specifications [optional]
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+```
+# Define the model path
+model_path = 'amornpan/Qwen2.5-32B-MedChatModel'
 
-### Model Architecture and Objective
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+tokenizer.pad_token = tokenizer.eos_token
 
-[More Information Needed]
+## 3. Prepare Your Input (Custom Prompt)
 
-### Compute Infrastructure
+Create a custom medical prompt that you want the model to respond to:
 
-[More Information Needed]
+```python
+custom_prompt = "อาการของโรคเบาหวานมีอะไรบ้าง"
+PROMPT = f'[INST] <คุณเป็นผู้ช่วยตอบคำถามทางการแพทย์ จงตอบคำถามอย่างถูกต้องและให้ข้อมูลที่เป็นประโยชน์ที่สุด<> {custom_prompt} [/INST]'
 
-#### Hardware
+# Tokenize the input prompt
+inputs = tokenizer(PROMPT, return_tensors="pt", padding=True, truncation=True)
+```
 
-[More Information Needed]
+## 4. Configure the Model for Efficient Loading (4-bit Quantization)
 
-#### Software
+The model uses 4-bit precision for efficient inference. Here’s how to set up the configuration:
 
-[More Information Needed]
+```python
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.float16
+)
+```
 
-## Citation [optional]
+## 5. Load the Model with Quantization Support
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+Now, load the model with the 4-bit quantization settings:
 
-**BibTeX:**
+```python
+model = AutoModelForCausalLM.from_pretrained(
+    model_path,
+    quantization_config=bnb_config,
+    trust_remote_code=True
+)
+```
 
-[More Information Needed]
+## 6. Move the Model and Inputs to the GPU (prefer GPU)
 
-**APA:**
+For faster inference, move the model and input tensors to a GPU, if available:
 
-[More Information Needed]
+```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()}
+```
 
-## Glossary [optional]
+## 7. Generate a Response from the Model
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+Now, generate the medical response by running the model:
 
-[More Information Needed]
+```python
+outputs = model.generate(**inputs, max_new_tokens=256, do_sample=True)
+```
 
-## More Information [optional]
+## 8. Decode the Generated Text
 
-[More Information Needed]
+Finally, decode and print the response from the model:
 
-## Model Card Authors [optional]
+```python
+generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(generated_text)
+```
 
-[More Information Needed]
+## 9. Output
+```python
+คำถาม: การรักษาโรคความดันโลหิตสูงทำอย่างไร
+คำตอบ: สวัสดี ความดันโลหิตสูงสามารถรักษาได้โดยการใช้ยาหลายชนิด เช่น เบนโซเพอรีซิน, อะโมโลนิด, ลิโซโปรตาซอล, อีลาฟอร์เท็ต,
+อัลฟูราลิท, อะเซติซิลดิโพราเมต, อาราคานา, อาเนอโรนิก, อาเซติซิลสัมพันธ์, อาเนอโรนิก, อะเซติซิลสัมพันธ์ เป็นต้น
+คุณสามารถปรึกษาแพทย์ผู้เชี่ยวชาญในเรื่องนี้เพื่อทราบข้อมูลเพิ่มเติมเกี่ยวกับยาดังกล่าว หวังว่าคำตอบของฉันจะเป็นประโยชน์สำหรับคุณ
+ขอให้คุณมีสุขภาพที่ดี ขอบคุณที่เลือกใช้บริการของเรา หากคุณมีคำถามใด ๆ
 
-## Model Card Contact
+คำถาม: ยา Paracetamol มีผลข้างเคียงอะไรบ้าง
+คำตอบ: Paracetamol เป็นยาแก้ปวดและลดไข้ที่ใช้กันอย่างแพร่หลาย ซึ่งมีผลข้างเคียงน้อยกว่ายาแก้ปวดชนิดอื่นๆ โดยทั่วไปจะไม่มีผลข้างเคียงใดๆ
+หากใช้ในขนาดที่แนะนำ แต่อาจพบได้ เช่น ปวดท้อง อาเจียน และรู้สึกคลื่นไส้ นอกจากนี้ หากใช้ในปริมาณที่มากเกินไป อาจทำให้มีอาการปัสสาวะขุ่น
+มีสีเหลืองเข้ม เบื่ออาหาร คลื่นไส้ อาเจียน ปวดท้อง ปวดหัว ตาเหลือง หรือปัสสาวะสีเข้มเป็นสีชาโคล่า
+หากมีอาการดังกล่าวควรหยุดการใช้ยาและรีบไปพบแพทย์เพื่อตรวจหาความเสียหายของตับจากยา
+โดยการตรวจการทำงานของตับ ซึ่งหากพบว่ามีอาการของโรคตับวายเฉียบพลัน
+```
 
-[More Information Needed]
-### Framework versions
+### 👤 **Authors**
 
-- PEFT 0.13.3.dev0
+* Amornpan Phornchaicharoen (amornpan@gmail.com)
+* Aekanun Thongtae ([email protected])
+* Montita Somsoo ([email protected])