Update README.md

README.md

@@ -16,8 +16,9 @@ tags:
 - Instruct
 - QwQ
 ---
+### **QwQ-LCoT-3B-Instruct Model Card**
 
-### QwQ-LCoT-3B-Instruct Model File
+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.
 
 | **File Name**                         | **Size**       | **Description**                                  | **Upload Status**  |
 |----------------------------------------|----------------|-------------------------------------------------|--------------------|
@@ -34,3 +35,71 @@ tags:
 | `tokenizer.json`                       | 11.4 MB        | Serialized tokenizer data.                      | Uploaded (LFS)     |
 | `tokenizer_config.json`                | 7.73 kB        | Tokenizer configuration settings.               | Uploaded           |
 | `vocab.json`                           | 2.78 MB        | Vocabulary file for the tokenizer.              | Uploaded           |
+
+---
+
+### **Key Features:**
+
+1. **Long Chain-of-Thought Reasoning:**  
+   - Specifically designed to generate comprehensive, step-by-step explanations for complex queries.
+
+2. **Lightweight and Efficient:**  
+   - With only 3 billion parameters, it is optimized for systems with limited computational resources without compromising reasoning capabilities.
+
+3. **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:**
+
+1. **Text Generation:**  
+   - Provides detailed, structured, and logical text outputs tailored to user prompts.
+
+2. **Reasoning Tasks:**  
+   - Solves step-by-step problems in math, logic, and science.
+
+3. **Educational Assistance:**  
+   - Generates coherent explanations for academic and research purposes.
+
+4. **Dialogue and Summarization:**  
+   - Handles conversational queries and summarizes long documents effectively.
+
+---
+
+### **Usage Instructions:**
+
+1. **Setup:**
+   Download all model files and ensure compatibility with the Hugging Face Transformers library.
+
+2. **Loading the Model:**
+   ```python
+   from transformers import AutoModelForCausalLM, AutoTokenizer
+   
+   model_name = "prithivMLmods/QwQ-LCoT-3B-Instruct"
+   tokenizer = AutoTokenizer.from_pretrained(model_name)
+   model = AutoModelForCausalLM.from_pretrained(model_name)
+   ```
+
+3. **Generate Long-Chain Reasoning Outputs:**
+   ```python
+   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))
+   ```
+
+4. **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.
+
+---