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README.md

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 ### 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.
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+---
+### **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/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.
+
 ---