Update README.md

README.md

@@ -20,6 +20,8 @@ tags:
 
 ### QwQ-LCoT-7B-Instruct Model File
 
+The **QwQ-LCoT-7B-Instruct** is a fine-tuned language model designed for advanced reasoning and instruction-following tasks. It leverages the **Qwen2.5-7B** base model and has been fine-tuned on the **amphora/QwQ-LongCoT-130K** dataset, focusing on chain-of-thought (CoT) reasoning.
+
 | **File Name**                         | **Size**       | **Description**                                  | **Upload Status**  |
 |----------------------------------------|----------------|-------------------------------------------------|--------------------|
 | `.gitattributes`                       | 1.57 kB        | Tracks large files with Git LFS.                | Uploaded           |
@@ -39,3 +41,46 @@ tags:
 | `vocab.json`                           | 2.78 MB        | Tokenizer vocabulary.                           | Uploaded           |
 
 ---
+### **Key Features:**
+
+1. **Model Size:**  
+   - **7.62B parameters** (FP16 precision).  
+
+2. **Model Sharding:**  
+   - The model weights are split into 4 shards (`safetensors`) for efficient storage and download:
+     - `model-00001-of-00004.safetensors` (4.88 GB)
+     - `model-00002-of-00004.safetensors` (4.93 GB)
+     - `model-00003-of-00004.safetensors` (4.33 GB)
+     - `model-00004-of-00004.safetensors` (1.09 GB)
+
+3. **Tokenizer:**  
+   - Byte-pair encoding (BPE) based.
+   - Files included:
+     - `vocab.json` (2.78 MB)
+     - `merges.txt` (1.82 MB)
+     - `tokenizer.json` (11.4 MB)
+   - Special tokens mapped in `special_tokens_map.json` (e.g., `<pad>`, `<eos>`).
+
+4. **Configuration Files:**  
+   - `config.json`: Defines model architecture and hyperparameters.
+   - `generation_config.json`: Settings for inference and text generation tasks.
+
+---
+
+### **Training Dataset:**  
+- **Dataset Name:** [amphora/QwQ-LongCoT-130K](https://huggingface.co/amphora/QwQ-LongCoT-130K)  
+- **Size:** 133k examples.  
+- **Focus:** Chain-of-Thought reasoning for complex tasks.
+
+---
+
+### **Use Cases:**
+1. **Instruction Following:**  
+   Handle user instructions effectively, even for multi-step tasks.
+   
+2. **Reasoning Tasks:**  
+   Perform logical reasoning and generate detailed step-by-step solutions.
+   
+3. **Text Generation:**  
+   Generate coherent, context-aware responses.
+---