Update README.md

README.md

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 This model was converted to GGUF format from [`prithivMLmods/Llama-Chat-Summary-3.2-3B`](https://huggingface.co/prithivMLmods/Llama-Chat-Summary-3.2-3B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/prithivMLmods/Llama-Chat-Summary-3.2-3B) for more details on the model.
 
+---
+Model details:
+-
+
+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.
+
+ Key Features
+
+    Conversation Summarization:
+        Generates concise and meaningful summaries of long chats, discussions, or threads.
+
+    Context Preservation:
+        Maintains critical points, ensuring important details aren't omitted.
+
+    Text Summarization:
+        Works beyond chats; supports summarizing articles, documents, or reports.
+
+    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
+
+    Customer Support Logs:
+        Summarize chat logs or support tickets for insights and reporting.
+
+    Meeting Notes:
+        Generate concise summaries of meeting transcripts.
+
+    Document Summarization:
+        Create short summaries for lengthy reports or articles.
+
+    Content Generation Pipelines:
+        Automate summarization for newsletters, blogs, or email digests.
+
+    Context Extraction for AI Systems:
+        Preprocess chat or conversation logs for downstream AI applications.
+
+Load the Model
+
+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
+
+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.
+
+---
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)