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README.md

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+
+---
+
+license: creativeml-openrail-m
+datasets:
+- HuggingFaceTB/Magpie-Pro-300K-Filtered-H4
+language:
+- en
+base_model:
+- meta-llama/Llama-3.2-3B-Instruct
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- safetensors
+- Magpie
+- Llama
+- Ollama
+- Llama-Cpp
+
+---
+
+[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
+
+
+# QuantFactory/Llama-Magpie-3.2-3B-Instruct-GGUF
+This is quantized version of [prithivMLmods/Llama-Magpie-3.2-3B-Instruct](https://huggingface.co/prithivMLmods/Llama-Magpie-3.2-3B-Instruct) created using llama.cpp
+
+# Original Model Card
+
+
+## Llama-Magpie-3.2-3B-Instruct Model Files
+
+| File Name [ Uploaded Files ]                      | Size       | Description                                     | Upload Status  |
+|------------------------------------|------------|-------------------------------------------------|----------------|
+| `.gitattributes`                   | 1.57 kB    | Git attributes configuration file               | Uploaded       |
+| `README.md`                        | 269 Bytes  | Updated README file                             | Uploaded       |
+| `config.json`                      | 1.04 kB    | Model configuration file                        | Uploaded       |
+| `generation_config.json`           | 248 Bytes  | Generation-specific configuration file          | Uploaded       |
+| `pytorch_model-00001-of-00002.bin` | 4.97 GB    | Part 1 of the PyTorch model weights             | Uploaded (LFS) |
+| `pytorch_model-00002-of-00002.bin` | 1.46 GB    | Part 2 of the PyTorch model weights             | Uploaded (LFS) |
+| `pytorch_model.bin.index.json`     | 21.2 kB    | Index for PyTorch model weights                 | Uploaded       |
+| `special_tokens_map.json`          | 477 Bytes  | Mapping of special tokens                       | Uploaded       |
+| `tokenizer.json`                   | 17.2 MB    | Tokenizer configuration                         | Uploaded (LFS) |
+| `tokenizer_config.json`            | 57.4 kB    | Additional tokenizer settings                   | Uploaded       |
+
+| Model Type | Size | Context Length | Link |
+|------------|------|----------------|------|
+| GGUF | 3B | - | [🤗 Llama-Magpie-3.2-3B-Instruct-GGUF](https://huggingface.co/prithivMLmods/Llama-Magpie-3.2-3B-Instruct-GGUF) |
+
+### **Llama-Magpie-3.2-3B-Instruct**
+
+The **Llama-Magpie-3.2-3B-Instruct** model is a powerful instruction-tuned language model with 3 billion parameters. It is built on the robust **Llama-3.2-3B** architecture and fine-tuned for diverse text generation tasks using the **Magpie-Pro-300K-Filtered-H4** dataset. This model is designed to perform well across a range of instruction-following and conversational scenarios.
+
+---
+
+### **Key Features:**
+
+1. **Instruction-Tuned Precision**  
+   Specifically optimized to handle structured tasks and open-ended instructions effectively.
+   
+2. **Enhanced Context Handling**  
+   With 3B parameters, the model is capable of generating coherent and contextually relevant outputs for long and complex prompts.
+
+3. **Wide Applicability**  
+   Suitable for applications such as content creation, conversational AI, and advanced problem-solving.
+
+---
+
+### **Training Details:**
+- **Base Model**: [meta-llama/Llama-3.2-3B-Instruct](#)
+- **Dataset**: Trained on the **Magpie-Pro-300K-Filtered-H4** dataset, which includes 300k examples filtered for high-quality instruction-following tasks.
+
+---
+
+### **Intended Use Cases:**
+- **Text Generation**: Create summaries, stories, and other forms of text content.  
+- **Conversational AI**: Enhance chatbot interactions with human-like, contextually aware dialogue.  
+- **Instruction Following**: Execute complex tasks by following structured prompts.
+
+---
+
+### **How to Use:**
+1. Download the model files and set up the necessary dependencies (PyTorch).  
+2. Load the model using the configuration files and tokenizer settings provided.  
+3. Deploy the model for inference using Hugging Face, serverless APIs, or local setups.
+
+---
+
+The **Llama-Magpie-3.2-3B-Instruct** model is a versatile and efficient solution for a wide range of NLP applications, offering a balance between scalability and performance.
+
+# 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.