Update README.md

README.md

@@ -30,10 +30,8 @@ This repository contains a fine-tuned version of **Meta’s Llama 3.1 3B Instruc
 The fine-tuning process involves using **QLoRA** to adapt the pre-trained model while maintaining memory efficiency and computational feasibility. This technique allows for fine-tuning large-scale models on consumer-grade GPUs by leveraging **NF4** 4-bit quantization.
 
 - **Developed by [FineTuned]:** Karthik Manjunath Hadagali
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
 - **Model type:** Text-Generation
-- **Language(s) (NLP):** [More Information Needed]
+- **Language(s) (NLP):** Python
 - **License:** [More Information Needed]
 - **Fine-Tuned from model [optional]:** Meta Llama 3.1 3B Instruct
 - **Fine-Tuning Method:** QLoRA
@@ -87,7 +85,21 @@ Users (both direct and downstream) should be made aware of the risks, biases and
 
 Use the code below to get started with the model.
 
-[More Information Needed]
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Load the fine-tuned model
+model_id = "your-hf-username/llama-3.1-3b-medical-qlora"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
+
+# Example query
+input_text = "What is the medical definition of pneumonia?"
+inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=100)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
 
 ## Training Details
 
@@ -97,9 +109,9 @@ Use the code below to get started with the model.
 
 The model has been fine-tuned on the **dmedhi/wiki_medical_terms** dataset. This dataset is designed to improve medical terminology comprehension and consists of:
 
-- Medical definitions and terminologies
-- Disease symptoms and conditions
-- Healthcare and clinical knowledge from Wikipedia's medical section
+✅ Medical definitions and terminologies
+✅ Disease symptoms and conditions
+✅ Healthcare and clinical knowledge from Wikipedia's medical section
 
 This dataset ensures that the fine-tuned model performs well in understanding and responding to medical queries with enhanced accuracy.
 
@@ -140,43 +152,6 @@ This dataset ensures that the fine-tuned model performs well in understanding an
 - **Training Time:** ~3-4 hours per epoch on A100 40GB GPU
 - **Final Checkpoint Size:** ~2.8GB (with LoRA adapters stored separately)
 
-## 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
 
@@ -190,50 +165,26 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 - **Compute Region:** US-East
 - **Carbon Emitted:** [More Information Needed]
 
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
+## Limitations & Considerations
 
-#### Software
+❗ Not a substitute for professional medical advice
+❗ May contain biases from training data
+❗ Limited knowledge scope (not updated in real-time)
 
-[More Information Needed]
-
-## Citation [optional]
+## Citation
 
 <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+If you use this model, please consider citing:
 
-**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]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
+@article{llama3.1_medical_qlora,
+  title={Fine-tuned Llama 3.1 3B Instruct for Medical Knowledge with QLoRA},
+  author={Karthik Manjunath Hadagali},
+  year={2024},
+  journal={Hugging Face Model Repository}
+}
 
-## Model Card Contact
+## Acknowledgments
 
-[More Information Needed]
+- Meta AI for the Llama 3.1 3B Instruct Model.
+- Hugging Face PEFT for QLoRA implementation.
+- dmedhi/wiki_medical_terms dataset contributors.