Update README.md

README.md

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-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Model Card for `openthaigpt1.5-7b-medical-tuned`
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/663ce15f197afc063058dc3a/U0TIiWGdNaxl_9TH90gIx.png)
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/663ce15f197afc063058dc3a/mAZBm9Dk7-S-FQ4srj3aG.png)
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 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/663ce15f197afc063058dc3a/ijHMzw9Zrpm23o89vzsSc.png)
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/663ce15f197afc063058dc3a/hOIyuIA_zT7_s8SG-ZDWQ.png)
 
+<!-- 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.
 
-<!-- Provide a longer summary of what this model is. -->
+- **Developed by:** [Your Name or Organization]
+- **Fine-tuned by:** [Your Name or Organization]
+- **Model type:** Causal Language Model (AutoModelForCausalLM)
+- **Language(s):** Thai
+- **License:** [Your Chosen License]
+- **Fine-tuned from model:** `openthaigpt1.5-7b-instruct`
+- **Dataset used for fine-tuning:** `Thaweewat/thai-med-pack`
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+### Model Sources
 
-- **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 [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **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
 
-<!-- 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 directly used 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 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 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.
+- 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
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+### Bias
+- The model might reflect biases present in the dataset, particularly when addressing underrepresented medical conditions or topics.
 
-[More Information Needed]
+### 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.
 
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+### 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
 
-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]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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).
-
-- **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]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
+Here’s how to load and use the model for generating medical responses in Thai:
 
-[More Information Needed]
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-## Model Card Authors [optional]
+# Load the fine-tuned model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("amornpan/openthaigpt-MedChatModelv11")
+model = AutoModelForCausalLM.from_pretrained("amornpan/openthaigpt-MedChatModelv11")
 
-[More Information Needed]
+# Input your medical question or prompt in Thai
+input_text = "ใส่คำถามทางการแพทย์ที่นี่"
+inputs = tokenizer(input_text, return_tensors="pt")
 
-## Model Card Contact
+# Generate the output
+output = model.generate(**inputs)
 
-[More Information Needed]
+# Decode and print the generated response, skipping special tokens
+print(tokenizer.decode(output[0], skip_special_tokens=True))