Update README.md

README.md

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 ### Qwen-UMLS-7B-Instruct [ Unified Medical Language System ]
 
+The **Qwen-UMLS-7B-Instruct** model is a specialized, instruction-tuned language model designed for medical and healthcare-related tasks. It is fine-tuned on the **Qwen2.5-7B-Instruct** base model using the **UMLS (Unified Medical Language System)** dataset, making it an invaluable tool for medical professionals, researchers, and developers building healthcare applications.
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 | **File Name**                          | **Size**       | **Description**                                  | **Upload Status**  |
 |-----------------------------------------|----------------|-------------------------------------------------|--------------------|
 | `.gitattributes`                        | 1.57 kB        | File to specify LFS rules for large file tracking. | Uploaded           |
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 | `tokenizer_config.json`                 | 7.73 kB        | Configuration file for the tokenizer.           | Uploaded           |
 | `vocab.json`                            | 2.78 MB        | Vocabulary file for tokenization.               | Uploaded           |
 
+### **Key Features:**
+
+1. **Medical Expertise:**  
+   - Trained on the UMLS dataset, ensuring deep domain knowledge in medical terminology, diagnostics, and treatment plans.
+
+2. **Instruction-Following:**  
+   - Designed to handle complex queries with clarity and precision, suitable for diagnostic support, patient education, and research.
+
+3. **High-Parameter Model:**  
+   - Leverages 7 billion parameters to deliver detailed, contextually accurate responses.
+
+---
+
+### **Training Details:**
+
+- **Base Model:** [Qwen2.5-7B-Instruct](#)  
+- **Dataset:** [avaliev/UMLS](#)  
+  - Comprehensive dataset of medical terminologies, relationships, and use cases with 99.1k samples.
+---
+### **Capabilities:**
+
+1. **Clinical Text Analysis:**  
+   - Interpret medical notes, prescriptions, and research articles.
+
+2. **Question-Answering:**  
+   - Answer medical queries, provide explanations for symptoms, and suggest treatments based on user prompts.
+
+3. **Educational Support:**  
+   - Assist in learning medical terminologies and understanding complex concepts.
+
+4. **Healthcare Applications:**  
+   - Integrate into clinical decision-support systems or patient care applications.
+---
+### **Usage Instructions:**
+
+1. **Setup:**
+   Download all files and ensure compatibility with the Hugging Face Transformers library.
+
+2. **Loading the Model:**
+   ```python
+   from transformers import AutoModelForCausalLM, AutoTokenizer
+   
+   model_name = "prithivMLmods/Qwen-UMLS-7B-Instruct"
+   tokenizer = AutoTokenizer.from_pretrained(model_name)
+   model = AutoModelForCausalLM.from_pretrained(model_name)
+   ```
+
+3. **Generate Medical Text:**
+   ```python
+   input_text = "What are the symptoms and treatments for diabetes?"
+   inputs = tokenizer(input_text, return_tensors="pt")
+   outputs = model.generate(**inputs, max_length=200, temperature=0.7)
+   print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+   ```
+
+4. **Customizing Outputs:**
+   Modify `generation_config.json` to optimize output style:
+   - `temperature` for creativity vs. determinism.
+   - `max_length` for concise or extended responses.
+
 ---
 
+### **Applications:**
+
+1. **Clinical Support:**  
+   - Assist healthcare providers with quick, accurate information retrieval.
+
+2. **Patient Education:**  
+   - Provide patients with understandable explanations of medical conditions.
+
+3. **Medical Research:**  
+   - Summarize or analyze complex medical research papers.
+
+4. **AI-Driven Diagnostics:**  
+   - Integrate with diagnostic systems for preliminary assessments.
+
+---