The Llama-3.2-3B-Promptist-Mini is a fine-tuned version of the Llama-3.2-3B-Instruct model, specifically optimized for prompt engineering and enhancement tasks. It is ideal for generating and enhancing various types of text prompts, offering high performance in creative and instructional applications. The model leverages a smaller, more efficient architecture suited for fine-tuning and prompt-based use cases.
File Name
Size
Description
Upload Status
.gitattributes
1.82 kB
Git attributes configuration file
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README.md
286 Bytes
Updated README file
Updated
config.json
29 Bytes
Model configuration settings
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Llama-3.2-3B-Promptist-Mini.F16.gguf
6.43 GB
Full model weights in F16 format
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Llama-3.2-3B-Promptist-Mini.Q4_K_M.gguf
2.02 GB
Quantized model weights (Q4_K_M)
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Llama-3.2-3B-Promptist-Mini.Q5_K_M.gguf
2.32 GB
Quantized model weights (Q5_K_M)
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Llama-3.2-3B-Promptist-Mini.Q8_0.gguf
3.42 GB
Quantized model weights (Q8_0)
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Modelfile
1.98 kB
Model metadata or configuration
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Screenshot 2024-12-04 161304.png
Key Features:
Prompt Engineering and Enhancement:
This model is fine-tuned to generate and improve prompts for various applications, such as question generation, creative writing, and instruction-following tasks.
Text Generation:
It excels in generating coherent and contextually relevant text based on the given prompts. The model can be used for a wide range of text-based applications, including content creation and automated text generation.
Custom Tokenizer:
Includes a tokenizer optimized for handling specialized tokens related to prompt-based tasks, ensuring the model performs well in generating creative and logical text.
Dataset: Trained on Prompt-Enhancement-Mini, a dataset specifically designed to enhance prompt generation, with examples tailored to creative and instructional contexts.
Capabilities:
Prompt Generation and Enhancement:
The model can generate and enhance prompts for various tasks, including machine learning, creative writing, and instructional content.
Text Generation:
It excels at generating coherent, structured, and contextually appropriate text from user inputs, making it suitable for a wide variety of text-based applications.
Usage Instructions:
Model Setup: Download all model files and ensure the PyTorch model weights and tokenizer configurations are included.
Inference: Load the model in a Python environment using frameworks like PyTorch or Hugging Face's Transformers.
Customization: Configure the model with the config.json and generation_config.json files for optimal performance during inference.
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.
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:
plaintext
1>>> write a mini passage about space x
2Space X, the private aerospace company founded by Elon Musk, is revolutionizing the field of space exploration.
3With its ambitious goals to make humanity a multi-planetary species and establish a sustainable human presence in
4the cosmos, Space X has become a leading player in the industry. The company's spacecraft, like the Falcon 9, have
5demonstrated remarkable capabilities, allowing for the transport of crews and cargo into space with unprecedented
6efficiency. As technology continues to advance, the possibility of establishing permanent colonies on Mars becomes
7increasingly feasible, thanks in part to the success of reusable rockets that can launch multiple times without
8sustaining significant damage. The journey towards becoming a multi-planetary species is underway, and Space X
9plays 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.
