marco-o1:7b-q4_K

Fine-Tuning with CoT Data: We develop Marco-o1-CoT by performing full-parameter fine-tuning on the base model using open-source CoT dataset combined with our self-developed synthetic data.
Solution Space Expansion via MCTS: We integrate LLMs with MCTS (Marco-o1-MCTS), using the model’s output confidence to guide the search and expand the solution space.
Reasoning Action Strategy: We implement novel reasoning action strategies and a reflection mechanism (Marco-o1-MCTS mini-step), including exploring different action granularities within the MCTS framework and prompting the model to self-reflect, thereby significantly enhancing the model’s ability to solve complex problems.
Application in Translation Tasks: We are the first to apply Large Reasoning Models (LRM) to Machine Translation task, exploring inference time scaling laws in the multilingual and translation domain.

Usage

ollama run marco-o1 "How many Rs are in strawberry?"

Parse the resulting string between <Output> and </Output>:

...
<Output>
There are 3 Rs in strawberry.
</Output>

References

GitHub

HuggingFace

* **Fine-Tuning with CoT Data:** We develop <ins>Marco-o1-CoT</ins> by performing full-parameter fine-tuning on the base model using open-source CoT dataset combined with our self-developed synthetic data. 
* **Solution Space Expansion via MCTS:** We integrate LLMs with MCTS (<ins>Marco-o1-MCTS</ins>), using the model's output confidence to guide the search and expand the solution space. 
* **Reasoning Action Strategy:** We implement novel reasoning action strategies and a reflection mechanism (<ins>Marco-o1-MCTS mini-step</ins>), including exploring different action granularities within the MCTS framework and prompting the model to self-reflect, thereby significantly enhancing the model's ability to solve complex problems.
* **Application in Translation Tasks:** We are the first to apply Large Reasoning Models (LRM) to <ins>Machine Translation task</ins>, exploring inference time scaling laws in the multilingual and translation domain.

## Usage

```
ollama run marco-o1 "How many Rs are in strawberry?"
```

Parse the resulting string between `<Output>` and `</Output>`:

```
...
<Output>
There are 3 Rs in strawberry.
</Output>
```

## References

[GitHub](https://github.com/AIDC-AI/Marco-o1?tab=readme-ov-file)

[HuggingFace](https://huggingface.co/AIDC-AI/Marco-o1)

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