DavidAU/Mistral-Nemo-Instruct-2407-12B-Thinking-M-Claude-Opus-High-Reasoning
DavidAU/Mistral-Nemo-Instruct-2407-12B-Thinking-M-Claude-Opus-High-Reasoning is a 12 billion parameter instruction-tuned model based on Mistral Nemo, fine-tuned by DavidAU using Unsloth. It is specifically optimized for "thinking/reasoning" tasks, incorporating Claude Opus 4.5 High Reasoning capabilities. This model aims to provide compact and precise reasoning, with a notable context length of 32768 tokens, making it suitable for complex analytical prompts.
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Model Overview: Mistral-Nemo-Instruct-2407-12B-Thinking-M-Claude-Opus-High-Reasoning
This 12 billion parameter model, fine-tuned by DavidAU using Unsloth, is an instruction-tuned variant of Mistral Nemo. Its primary differentiator is a specialized fine-tuning for "thinking/reasoning" tasks, leveraging a dataset derived from Claude Opus 4.5 High Reasoning. The "M" in its name signifies a medium level of this reasoning tune, offering a balance between reasoning depth and a lighter computational footprint compared to its "HI" counterpart.
Key Capabilities & Features
- Enhanced Reasoning: Designed to produce compact and precise reasoning outputs, rather than verbose explanations.
- Temperature Agnostic Reasoning: The model's thinking activation is not significantly affected by temperature settings (recommended range: 0.1 to 2.5+).
- Flexible Output Control: Adjusting the repetition penalty (e.g., to 1.0) can lead to longer reasoning blocks and potentially higher quality output.
- Context Length: Supports a substantial context window of 32768 tokens, with a minimum suggested context of 4k, ideally 8k+.
- No System Prompt Required: Thinking tags/blocks are self-generated by the model.
- Optimized for Specific Quants: Recommends Q4KS (non-imatrix) or IQ3_M (imatrix) or higher for optimal reasoning performance.
Performance & Benchmarks
While specific benchmarks for the "reasoning" fine-tuning are not yet available, the base Mistral Nemo model demonstrates strong performance across various benchmarks:
- MMLU (5-shot): 68.0%
- HellaSwag (0-shot): 83.5%
- Multilingual MMLU: Scores ranging from 59.0% (Chinese, Japanese) to 64.6% (Spanish).
Good For
- Applications requiring concise and logical reasoning.
- Use cases where complex problem-solving and analytical thought are crucial.
- Scenarios benefiting from a large context window for detailed input analysis.
- Developers looking for a model that can self-generate thought processes without explicit system prompts.