Deepthink Reasoning Adapter

Introduction

The Deepthink-Reasoning-Adapter is a fine-tuned model based on the Qwen2.5-7B-Instruct, designed for complex text generation tasks that require deep reasoning and logical structuring. It is particularly suitable for applications in education, programming, and creative writing, providing accurate and contextually relevant outputs.

Architecture

Deepthink-Reasoning-Adapter integrates advanced natural language processing capabilities, enabling it to excel in generating step-by-step solutions, creative content, and logical analyses. It supports multilingual text generation across 29 languages and offers enhancements in coding, mathematics, long-text generation, and structured data understanding. The model can handle contexts up to 128K tokens and generate outputs up to 8K tokens.

Training

The model is fine-tuned to improve instruction following, handle structured data, and produce structured outputs like JSON. It is resilient to diverse system prompts and enhances role-play and condition-setting for chatbot applications.

Guide: Running Locally

Basic Steps

1. Install Ollama: Download and install Ollama from their website.
2. Create Your Model File:
   • Name the file after your model, e.g., metallama.
   • Specify the base model with the line: FROM Llama-3.2-1B.F16.gguf.
   • Ensure the base model file is in the same directory.
3. Create and Verify Your Model:
   • Use the command: ollama create metallama -f ./metallama.
   • Verify with: ollama list.
4. Run the Model:
   • Start the model with: ollama run metallama.
5. Interact with the Model:
   • Use the interactive prompt to query the model, e.g., >>> Tell me about Space X.

Cloud GPUs

For enhanced performance, consider using cloud GPUs like AWS EC2, Google Cloud, or Azure, which provide scalable resources for running large models.

License

The Deepthink-Reasoning-Adapter is released under the CreativeML Open RAIL-M license.