Update README.md

README.md

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 ---
 ### **QwQ-4B-Instruct-Model-Files**
 
+The **QwQ-4B-Instruct** is a lightweight and efficient fine-tuned language model for instruction-following tasks and reasoning. It is based on a quantized version of the **Qwen2.5-7B** model, optimized for inference speed and reduced memory consumption, while retaining robust capabilities for complex tasks.
+
 | **File Name**                   | **Size**        | **Description**                                    | **Upload Status** |
 |----------------------------------|-----------------|---------------------------------------------------|-------------------|
 | `.gitattributes`                 | 1.57 kB         | Tracks files stored with Git LFS.                 | Uploaded          |
@@ -35,4 +37,55 @@ tags:
 | `tokenizer_config.json`          | 7.73 kB         | Settings for the tokenizer integration.           | Uploaded          |
 | `vocab.json`                     | 2.78 MB         | Vocabulary file containing token-to-id mappings.  | Uploaded          |
 
+### **Key Features:**
+
+1. **Model Size:**
+   - **4.46B parameters.**
+
+2. **Precision Support:**
+   - Available in multiple tensor types:
+     - **FP16**
+     - **F32**
+     - **U8 (Quantized)**
+
+3. **Model Sharding:**
+   - The model weights are stored in two parts for efficient download:
+     - `model-00001-of-00002.safetensors` (4.46 GB)
+     - `model-00002-of-00002.safetensors` (1.09 GB)
+   - Indexed with `model.safetensors.index.json`.
+
+4. **Tokenizer:**
+   - Uses Byte-Pair Encoding (BPE).
+   - Includes:
+     - `vocab.json` (2.78 MB)
+     - `merges.txt` (1.82 MB)
+     - `tokenizer.json` (11.4 MB, pre-trained configuration).
+   - Special tokens mapped in `special_tokens_map.json` (e.g., `<pad>`, `<eos>`).
+
+5. **Configuration Files:**
+   - `config.json`: Defines the architecture, hyperparameters, and settings.
+   - `generation_config.json`: Specifies text generation behavior (e.g., max length, temperature).
+
 ---
+
+### **Training Dataset:**
+- **Dataset Name:** [amphora/QwQ-LongCoT-130K](https://huggingface.co/amphora/QwQ-LongCoT-130K)
+- **Size:** 133k examples.
+- **Focus:** Chain-of-Thought reasoning for detailed and logical outputs.
+
+---
+
+### **Use Cases:**
+1. **Instruction-Following:**
+   - Excels in handling concise and multi-step instructions.
+   
+2. **Reasoning:**
+   - Well-suited for tasks requiring logical deductions and detailed explanations.
+
+3. **Text Generation:**
+   - Generates coherent and contextually aware responses across various domains.
+
+4. **Resource-Constrained Applications:**
+   - Optimized for scenarios requiring lower computational resources due to its smaller model size and quantization.
+
+---