Update README.md

README.md

@@ -17,7 +17,10 @@ tags:
 - trl
 ---
 
-### **Llama-Chat-Summary-3.2-3B**
+### **Llama-Chat-Summary-3.2-3B: Context-Aware Summarization Model**
+
+**Llama-Chat-Summary-3.2-3B** is a fine-tuned model designed for generating **context-aware summaries** of long conversational or text-based inputs. Built on the **meta-llama/Llama-3.2-3B-Instruct** foundation, this model is optimized to process structured and unstructured conversational data for summarization tasks.
+
 
 | **File Name**                              | **Size**         | **Description**                                    | **Upload Status** |
 |--------------------------------------------|------------------|--------------------------------------------------|-------------------|
@@ -32,4 +35,92 @@ tags:
 | `tokenizer.json`                           | 17.2 MB         | Pre-trained tokenizer file.                      | Uploaded (LFS)    |
 | `tokenizer_config.json`                    | 57.4 kB         | Configuration file for the tokenizer.            | Uploaded          |
 
+### **Key Features**
+
+1. **Conversation Summarization:**  
+   - Generates concise and meaningful summaries of long chats, discussions, or threads.
+
+2. **Context Preservation:**  
+   - Maintains critical points, ensuring important details aren't omitted.
+
+3. **Text Summarization:**  
+   - Works beyond chats; supports summarizing articles, documents, or reports.
+
+4. **Fine-Tuned Efficiency:**  
+   - Trained with *Context-Based-Chat-Summary-Plus* dataset for accurate summarization of chat and conversational data.
+
+---
+### **Training Details**
+
+- **Base Model:** [meta-llama/Llama-3.2-3B-Instruct](#)  
+- **Fine-Tuning Dataset:** [prithivMLmods/Context-Based-Chat-Summary-Plus](#)  
+   - Contains **98.4k** structured and unstructured conversations, summaries, and contextual inputs for robust training.
+
+---
+### **Applications**
+
+1. **Customer Support Logs:**  
+   - Summarize chat logs or support tickets for insights and reporting.
+
+2. **Meeting Notes:**  
+   - Generate concise summaries of meeting transcripts.
+
+3. **Document Summarization:**  
+   - Create short summaries for lengthy reports or articles.
+
+4. **Content Generation Pipelines:**  
+   - Automate summarization for newsletters, blogs, or email digests.
+
+5. **Context Extraction for AI Systems:**  
+   - Preprocess chat or conversation logs for downstream AI applications.
+
+---
+
+### **Usage**
+
+#### **Load the Model**
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Llama-Chat-Summary-3.2-3B"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+```
+
+---
+
+#### **Generate a Summary**
+```python
+prompt = """
+Summarize the following conversation:
+User1: Hey, I need help with my order. It hasn't arrived yet.
+User2: I'm sorry to hear that. Can you provide your order number?
+User1: Sure, it's 12345.
+User2: Let me check... It seems there was a delay. It should arrive tomorrow.
+User1: Okay, thank you!
+"""
+
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=100, temperature=0.7)
+
+summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print("Summary:", summary)
+```
+
+---
+
+### **Expected Output**
+**"The user reported a delayed order (12345), and support confirmed it will arrive tomorrow."**
+
+---
+
+### **Deployment Notes**
+
+- **Serverless API:**  
+   This model currently lacks sufficient usage for serverless endpoints. Use **dedicated endpoints** for deployment.
+
+- **Performance Requirements:**  
+   - GPU with sufficient memory (recommended for large models).  
+   - Optimization techniques like quantization can improve efficiency for inference.
+
 ---