Thought-Ranked Llama 3.2 3B
ericflo/Llama-3.2-3B-COT is a specialized fine-tuned version of Meta's Llama 3.2 3B (Base) model, designed to generate explicit, high-quality thought processes before providing answers. This model underwent four rounds of supervised fine-tuning using a unique thought-chain ranking approach.
Key Features & Training
- Thought Chain Generation: The model is trained to produce explicit thought processes, up to 128 tokens, before generating a final answer.
- Answer Generation: It then generates complete answers, up to 2048 tokens, based on these thought chains.
- Ranking & Selection: During training, an external LLM ranked the quality of generated answers (without seeing the thoughts), and the model was subsequently fine-tuned on the highest-ranked thought-answer pairs.
- Greedy Sampling: Uses greedy sampling for both thought and answer generation.
- Architecture: Based on the Llama 3.2 3B Transformer architecture with approximately 3.2 billion parameters.
Intended Use Cases
This model is particularly well-suited for tasks that benefit from transparent and explicit reasoning chains:
- Problem-solving: Breaking down complex problems into manageable steps.
- Mathematical Reasoning: Solving equations and explaining the process.
- Logical Deduction: Deriving conclusions through a series of logical steps.
- Step-by-step Explanations: Providing detailed, sequential instructions or justifications.
- Complex Decision Making: Outlining the thought process behind a decision.
Limitations
Users should be aware that the model's performance is limited by the capabilities of the base Llama 3.2 3B model. It may not always generate optimal thought chains, and its effectiveness depends on the quality of the LLM ranking system used during its training. Human oversight is recommended for critical applications.