SethBurkart/llama-3.2-3b-thinking
SethBurkart/llama-3.2-3b-thinking is a 3.2 billion parameter LLaMA-3.2-3B fine-tune, developed by SethBurkart, designed to emulate Claude's reasoning process. Trained on a dataset of Claude 3.5 Sonnet generated examples, this model aims to replicate structured thinking and reasoning in a smaller footprint. It incorporates reasoning tags like , , and to enhance its output structure, making it suitable for tasks requiring detailed analytical responses.
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Claude-Inspired LLaMA Fine-tune
This model, developed by SethBurkart, is a fine-tuned version of LLaMA-3.2-3B, specifically engineered to mimic the reasoning capabilities of Claude 3.5 Sonnet. With 3.2 billion parameters and a 32768 token context length, it aims to provide Claude-like structured thinking in a more compact model.
Key Capabilities & Features
- Claude-like Reasoning: Fine-tuned on the Claude Thinking Dataset to emulate Claude's analytical and reasoning processes.
- Structured Output: Utilizes special reasoning tags (
<thinking>,<reflection>,<output>) to organize its thought process and final response. - LLaMA-3.2-3B Architecture: Built upon the LLaMA-3.2-3B base model, offering a balance of performance and efficiency.
- GGUF Format: Available in GGUF format with F16 and Q8_0 quantizations, compatible with frameworks like Ollama.
When to Use This Model
This model is particularly well-suited for applications where a smaller, efficient model is needed to generate structured, thoughtful, and analytical responses, similar to those produced by larger Claude models. It's ideal for tasks requiring a clear breakdown of reasoning, self-critique, and a refined final output. Users should be aware of its limitations compared to the full-sized Claude models in terms of knowledge breadth and reasoning depth due to its smaller parameter count.