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---
license: creativeml-openrail-m
datasets:
- amphora/QwQ-LongCoT-130K
language:
- en
base_model:
- prithivMLmods/QwQ-LCoT-7B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- Qwen2.5
- Llama-Cpp
- Math
- CoT
- Long-CoT
- text-generation-inference
- QwQ
- SFT
---
### QwQ-LCoT-7B-Instruct Model in GGUF Format
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` | 1.79 kB | Tracks files stored with Git LFS. | Uploaded |
| `QwQ-LCoT-7B-Instruct.F16.gguf` | 15.2 GB | Full-precision model file in GGUF format. | Uploaded (LFS) |
| `QwQ-LCoT-7B-Instruct.Q4_K_M.gguf` | 4.68 GB | Quantized model weights (Q4_K_M precision). | Uploaded (LFS) |
| `QwQ-LCoT-7B-Instruct.Q5_K_M.gguf` | 5.44 GB | Quantized model weights (Q5_K_M precision). | Uploaded (LFS) |
| `QwQ-LCoT-7B-Instruct.Q8_0.gguf` | 8.1 GB | Quantized model weights (Q8_0 precision). | Uploaded (LFS) |
| `README.md` | 42 Bytes | Initial commit for project documentation. | Uploaded |
| `config.json` | 29 Bytes | Configuration file for model setup. | Uploaded |
---
### **Sample Long CoT:**
---
### **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.
---
# Run with Ollama [ Ollama Run ]
## Overview
Ollama is a powerful tool that allows you to run machine learning models effortlessly. This guide will help you download, install, and run your own GGUF models in just a few minutes.
## Table of Contents
- [Download and Install Ollama](#download-and-install-ollama)
- [Steps to Run GGUF Models](#steps-to-run-gguf-models)
- [1. Create the Model File](#1-create-the-model-file)
- [2. Add the Template Command](#2-add-the-template-command)
- [3. Create and Patch the Model](#3-create-and-patch-the-model)
- [Running the Model](#running-the-model)
- [Sample Usage](#sample-usage)
## Download and Install Ollama🦙
To get started, download Ollama from [https://ollama.com/download](https://ollama.com/download) and install it on your Windows or Mac system.
## Steps to Run GGUF Models
### 1. Create the Model File
First, create a model file and name it appropriately. For example, you can name your model file `metallama`.
### 2. Add the Template Command
In your model file, include a `FROM` line that specifies the base model file you want to use. For instance:
```bash
FROM Llama-3.2-1B.F16.gguf
```
Ensure that the model file is in the same directory as your script.
### 3. Create and Patch the Model
Open your terminal and run the following command to create and patch your model:
```bash
ollama create metallama -f ./metallama
```
Once the process is successful, you will see a confirmation message.
To verify that the model was created successfully, you can list all models with:
```bash
ollama list
```
Make sure that `metallama` appears in the list of models.
---
## Running the Model
To run your newly created model, use the following command in your terminal:
```bash
ollama run metallama
```
### Sample Usage / Test
In the command prompt, you can execute:
```bash
D:\>ollama run metallama
```
You can interact with the model like this:
```plaintext
>>> write a mini passage about space x
Space X, the private aerospace company founded by Elon Musk, is revolutionizing the field of space exploration.
With its ambitious goals to make humanity a multi-planetary species and establish a sustainable human presence in
the cosmos, Space X has become a leading player in the industry. The company's spacecraft, like the Falcon 9, have
demonstrated remarkable capabilities, allowing for the transport of crews and cargo into space with unprecedented
efficiency. As technology continues to advance, the possibility of establishing permanent colonies on Mars becomes
increasingly feasible, thanks in part to the success of reusable rockets that can launch multiple times without
sustaining significant damage. The journey towards becoming a multi-planetary species is underway, and Space X
plays a pivotal role in pushing the boundaries of human exploration and settlement.
```
---
## Conclusion
With these simple steps, you can easily download, install, and run your own models using Ollama. Whether you're exploring the capabilities of Llama or building your own custom models, Ollama makes it accessible and efficient.
- This README provides clear instructions and structured information to help users navigate the process of using Ollama effectively. Adjust any sections as needed based on your specific requirements or additional details you may want to include.
--- |