SimpleBerry/LLaMA-O1-Supervised-1129
SimpleBerry/LLaMA-O1-Supervised-1129 is an 8 billion parameter causal language model developed by SimpleBerry, fine-tuned from LLaMA-O1-Base-1127. It is specifically trained on the OpenLongCoT-SFT dataset, which emphasizes complex reasoning and multi-step problem-solving. This model excels at generating detailed, structured chains of thought for intricate problems, making it suitable for tasks requiring logical decomposition and step-by-step reasoning.
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Model Overview
SimpleBerry/LLaMA-O1-Supervised-1129 is an 8 billion parameter language model, fine-tuned by SimpleBerry from its LLaMA-O1-Base-1127 variant. This model is specifically trained on the SimpleBerry/OpenLongCoT-SFT dataset, which focuses on developing robust chain-of-thought reasoning capabilities.
Key Capabilities
- Advanced Chain-of-Thought Reasoning: The model is designed to generate detailed, step-by-step reasoning processes, breaking down complex problems into manageable sub-problems and expansions.
- Structured Output: It utilizes a unique XML-like tagging system (
<start_of_thought>,<problem>,<expansion>,<sub_problem>,<conclusion>,<critic>,<refine>) to structure its reasoning, as demonstrated in the provided examples. - Problem Solving: Excels at mathematical word problems and other tasks requiring logical deduction and sequential thinking.
- Long Context Handling: With a context length of 32768 tokens, it can process and reason over extensive inputs.
Use Cases
- Complex Problem Solving: Ideal for applications that require the model to show its work, such as educational tools, automated tutors, or systems needing verifiable reasoning steps.
- Reasoning and Logic Tasks: Suitable for tasks demanding a structured approach to derive solutions, like scientific problem-solving or logical puzzles.
- Interactive AI: The structured output can facilitate better human-AI interaction by making the model's thought process transparent.
Inference and Deployment
The model can be run using the transformers library and is also available in GGUF format for CPU-only devices, enhancing accessibility for various deployment scenarios. Example Python code is provided for easy integration and inference.