EphAsad/Atem-Pharaoh-3B
Atem-Pharaoh-3B by EphAsad is a 3.09 billion parameter chain-of-thought (CoT) fine-tuned language model, built upon Atem-3B. It is specifically trained to generate explicit ... reasoning traces before providing a final answer, distinguishing it from models that offer direct responses. This Stage 2 CoT model excels in tasks requiring detailed, step-by-step reasoning across mathematics, code, science, and general analytical problems, making it suitable for applications where transparent thought processes are crucial.
Loading preview...
Overview
Atem-Pharaoh-3B is a 3.09 billion parameter model developed by EphAsad, representing the Stage 2 release in the Atem 3B series. It is a chain-of-thought (CoT) fine-tune of the Atem-3B base model, specifically designed to produce explicit <think>...</think> reasoning traces. This model was trained on approximately 38,000 examples, with a 75% emphasis on structured reasoning traces and 25% on direct answers, allowing it to operate in both modes.
Key Capabilities
- Explicit Chain-of-Thought Reasoning: Generates detailed, step-by-step thought processes before delivering a final answer, enhancing transparency and explainability.
- Versatile Output Modes: Can produce both verbose reasoning traces and concise direct answers, depending on the prompt.
- Strong Analytical Performance: Excels in tasks requiring logical deduction across domains like coding, mathematics, and general analytical reasoning, often providing more comprehensive solutions than direct-answer models.
- Customizable Behavior: Highly responsive to system prompts, allowing users to control verbosity, CoT depth, and output length to mitigate tendencies for verbose outputs or "think trace runaways" on open-ended questions.
Good For
- Applications requiring explainability: Ideal for use cases where understanding the model's reasoning process is as important as the final answer.
- Complex problem-solving: Particularly effective for tasks in mathematics, code generation, and scientific reasoning where detailed working is beneficial.
- Educational tools: Can be used to demonstrate problem-solving methodologies through its explicit reasoning traces.
- Controlled output generation: With appropriate system prompting, it can be tailored for precise output formats and lengths, balancing detailed reasoning with conciseness.