Update README.md

README.md

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 This model was converted to GGUF format from [`prithivMLmods/QwQ-LCoT-3B-Instruct`](https://huggingface.co/prithivMLmods/QwQ-LCoT-3B-Instruct) 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/QwQ-LCoT-3B-Instruct) for more details on the model.
 
+---
+Model details:
+-
+The QwQ-LCoT-3B-Instruct model is a lightweight, instruction-tuned language model designed for complex reasoning and explanation tasks. It is fine-tuned on the Qwen2.5-3B-Instruct base model using the QwQ-LongCoT-130K dataset, focusing on long-chain-of-thought (LCoT) reasoning for enhanced logical comprehension and detailed output generation.
+
+ Key Features:
+
+    Long Chain-of-Thought Reasoning:
+        Specifically designed to generate comprehensive, step-by-step explanations for complex queries.
+
+    Lightweight and Efficient:
+        With only 3 billion parameters, it is optimized for systems with limited computational resources without compromising reasoning capabilities.
+
+    Instruction Optimization:
+        Fine-tuned to follow prompts and provide concise, actionable, and structured responses.
+
+Training Details:
+
+    Base Model: Qwen2.5-3B-Instruct
+    Dataset: amphora/QwQ-LongCoT-130K
+        Comprising 133,000 annotated samples focusing on logical tasks and structured thinking.
+
+Capabilities:
+
+    Text Generation:
+        Provides detailed, structured, and logical text outputs tailored to user prompts.
+
+    Reasoning Tasks:
+        Solves step-by-step problems in math, logic, and science.
+
+    Educational Assistance:
+        Generates coherent explanations for academic and research purposes.
+
+    Dialogue and Summarization:
+        Handles conversational queries and summarizes long documents effectively.
+
+Usage Instructions:
+
+    Setup: Download all model files and ensure compatibility with the Hugging Face Transformers library.
+
+    Loading the Model:
+
+    from transformers import AutoModelForCausalLM, AutoTokenizer
+
+    model_name = "prithivMLmods/QwQ-LCoT-3B-Instruct"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(model_name)
+
+Generate Long-Chain Reasoning Outputs:
+
+input_text = "Explain the process of photosynthesis step-by-step."
+inputs = tokenizer(input_text, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=300, temperature=0.5)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
+Customize Output Generation:
+Modify the generation_config.json file for different scenarios:
+
+    temperature: Controls randomness (lower = deterministic, higher = creative).
+    max_length: Sets response length.
+    top_p: Adjusts sampling for diversity in outputs.
+
+---
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)