ericflo/Llama-3.2-3B-COTv3
ericflo/Llama-3.2-3B-COTv3 is a 3.2 billion parameter language model developed by Eric Florenzano, based on the Llama 3.2 architecture. This model is specifically fine-tuned with reinforcement learning using Gemini 1.5 Flash 8B as a judge to enhance output quality across criteria like relevance, accuracy, and clarity. It excels at complex problem-solving by generating multi-level thought chains before responding, making it suitable for tasks requiring logical progression and detailed analysis.
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Overview
ericflo/Llama-3.2-3B-COTv3 is a 3.2 billion parameter model built upon the Llama 3.2 base architecture, developed by Eric Florenzano. This version (v3) introduces a significant advancement through reinforcement learning (RL) fine-tuning using OpenRLHF with REINFORCE, leveraging Gemini 1.5 Flash 8B as a judge model. This process refines the model's outputs for higher quality, evaluating responses on factors such as intent fulfillment, factual accuracy, clarity, style, and completeness.
Key Capabilities
- Enhanced Output Quality: RL fine-tuning improves responses across multiple criteria, including relevance, accuracy, clarity, and completeness.
- Thought Chain Generation: Maintains and enhances the multi-level thought chain capabilities from previous versions, allowing the model to "think" step-by-step before generating a final answer.
- Flexible System Prompts: Supports various system messages, including specifying the number of thoughts the model should generate.
Good For
- Breaking down complex problems into logical steps.
- Generating step-by-step mathematical solutions with clear explanations.
- Performing detailed analysis with well-structured arguments.
- Providing clear and appropriate explanations of complicated concepts.
- Tasks requiring well-reasoned decision-making supported by evidence.