Update README.md

README.md

@@ -33,168 +33,64 @@ library_name: peft
 - **Paper [optional]:** [More Information Needed]
 - **Demo [optional]:** [More Information Needed]
 
-## Uses
+## How to Use
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+# Install required libraries
+!pip install unsloth peft bitsandbytes accelerate transformers
 
-### Direct Use
+# Import necessary modules
+from transformers import AutoTokenizer
+from unsloth import FastLanguageModel
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+# Define the MedQA prompt
+medqa_prompt = """You are a medical QA system. Answer the following medical question clearly and in detail with complete sentences.
 
-[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]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### 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.
-
-## 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]
+### Question:
+{}
 
-#### Factors
+### Answer:
+"""
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+# Load the model and tokenizer using unsloth
+model_name = "Vijayendra/Phi4-MedQA"  # Replace with your Hugging Face model name
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name=model_name,
+    max_seq_length=2048,
+    dtype=None,  # Use default precision
+    load_in_4bit=True,  # Enable 4-bit quantization
+    device_map="auto"  # Automatically map model to available devices
+)
 
-[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]
+# Enable faster inference
+FastLanguageModel.for_inference(model)
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+# Prepare the medical question
+medical_question = "What are the common symptoms of diabetes?"  # Replace with your medical question
+inputs = tokenizer(
+    [medqa_prompt.format(medical_question)],
+    return_tensors="pt",
+    padding=True,
+    truncation=True,
+    max_length=1024
+).to("cuda")  # Ensure inputs are on the GPU
 
-**BibTeX:**
+# Generate the output
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=512,  # Allow for detailed responses
+    use_cache=True  # Speeds up generation
+)
 
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
+# Decode and clean the response
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+# Extract and print the generated answer
+answer_text = response.split("### Answer:")[1].strip() if "### Answer:" in response else response.strip()
 
-[More Information Needed]
+print(f"Question: {medical_question}")
+print(f"Answer: {answer_text}")
 
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
 
-## Model Card Contact
 
 [More Information Needed]
 ### Framework versions