---
license: creativeml-openrail-m
datasets:
- amphora/QwQ-LongCoT-130K
language:
- en
base_model:
- Qwen/Qwen2.5-7B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- Long-CoT
- Qwen2.5
- 7B
- safetensors
- text-generation-inference
- QwQ
- FP16 precision
- SFT
- Math
---

### 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           |
| `README.md`                            | 273 Bytes      | Contains initial documentation, likely minimal. | Updated            |
| `added_tokens.json`                    | 657 Bytes      | Maps additional tokens for the tokenizer.       | Uploaded           |
| `config.json`                          | 848 Bytes      | Model configuration (basic setup).              | Uploaded           |
| `generation_config.json`               | 281 Bytes      | Settings for text generation tasks.             | Uploaded           |
| `merges.txt`                           | 1.82 MB        | Tokenizer merges for byte-pair encoding (BPE).  | Uploaded           |
| `model-00001-of-00004.safetensors`     | 4.88 GB        | First part of model weights (split for LFS).    | Uploaded (LFS)     |
| `model-00002-of-00004.safetensors`     | 4.93 GB        | Second part of model weights.                   | Uploaded (LFS)     |
| `model-00003-of-00004.safetensors`     | 4.33 GB        | Third part of model weights.                    | Uploaded (LFS)     |
| `model-00004-of-00004.safetensors`     | 1.09 GB        | Fourth part of model weights.                   | Uploaded (LFS)     |
| `model.safetensors.index.json`         | 29.5 kB        | Index file for managing model shards.           | Uploaded           |
| `special_tokens_map.json`              | 644 Bytes      | Maps special tokens like `<pad>` or `<eos>`.    | Uploaded           |
| `tokenizer.json`                       | 11.4 MB        | Pre-trained tokenizer file in JSON format.      | Uploaded (LFS)     |
| `tokenizer_config.json`                | 7.73 kB        | Configuration details for the tokenizer.        | Uploaded           |
| `vocab.json`                           | 2.78 MB        | Tokenizer vocabulary.                           | Uploaded           |

### **Sample Long CoT:**

![Screenshot 2024-12-13 211732.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/Mgm9LmQZlFZmglKYwEDYA.png)

---
### **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/datasets/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.
---