metadata

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/Llama-Magpie-3.2-3B-Instruct-GGUF

This is quantized version of 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

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:

Instruction-Tuned Precision
Specifically optimized to handle structured tasks and open-ended instructions effectively.
Enhanced Context Handling
With 3B parameters, the model is capable of generating coherent and contextually relevant outputs for long and complex prompts.
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:

Download the model files and set up the necessary dependencies (PyTorch).
Load the model using the configuration files and tokenizer settings provided.
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.

Download and Install Ollama
Steps to Run GGUF Models
Running the Model
Sample Usage

Download and Install Ollama🦙

To get started, download Ollama from 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:

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:

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:

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:

ollama run metallama

Sample Usage / Test

In the command prompt, you can execute:

D:\>ollama run metallama

You can interact with the model like this:

>>> 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.

QuantFactory
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Llama-Magpie-3.2-3B-Instruct-GGUF