Acrux-500M-o1-Journey-GGUF

Acrux-500M-o1-Journey-GGUF Model Files

The Acrux-500M-o1-Journey is a lightweight, instruction-tuned language model fine-tuned from the Qwen2.5-0.5B-Instruct base model. With a size of 500 million parameters, it is designed for cost-effective deployment and fast text generation while maintaining quality performance for instruction-following tasks.

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Key Features:

1. Compact Size with Efficient Performance:
   The smaller parameter count (500M) ensures faster inference and reduced hardware requirements.

2. Instruction Optimization:
   Fine-tuned to follow prompts effectively, making it suitable for interactive applications and prompt-based tasks.

3. Domain-Specific Training:
   Trained on the GAIR/o1-journey dataset, providing tailored capabilities for specific use cases.

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Training Details:

• Base Model: Qwen2.5-0.5B-Instruct
• Dataset Used for Fine-Tuning: GAIR/o1-journey
  • A compact dataset focusing on instruction-driven generation with 1.42k samples.

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Capabilities:

1. Instruction Following:

   • Generates accurate and coherent responses to user instructions.
   • Handles summarization, question-answering, and conversational tasks.

2. Fast Inference:

   • Ideal for real-time applications due to reduced latency from its smaller size.

3. Interactive AI Development:

   • Suitable for chatbots, virtual assistants, and instructional interfaces.

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Usage Instructions:

1. Setup:
   Download all model files, ensuring compatibility with the Hugging Face Transformers library.

2. Loading the Model:

   from transformers import AutoModelForCausalLM, AutoTokenizer
   
   model_name = "prithivMLmods/Acrux-500M-o1-Journey"
   tokenizer = AutoTokenizer.from_pretrained(model_name)
   model = AutoModelForCausalLM.from_pretrained(model_name)
   

3. Sample Generate Text:

   input_text = "Explain the concept of machine learning in simple terms."
   inputs = tokenizer(input_text, return_tensors="pt")
   outputs = model.generate(**inputs, max_length=100, temperature=0.7)
   print(tokenizer.decode(outputs[0], skip_special_tokens=True))
   

4. Optimize Generation:
   Adjust parameters in generation_config.json for better control of output, such as:

   • temperature for randomness.
   • top_p for sampling diversity.
   • max_length for output size.

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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
• Steps to Run GGUF Models
  • 1. Create the Model File
  • 2. Add the Template Command
  • 3. Create and Patch the Model
• 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.

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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.

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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.

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