---
license: apache-2.0
tags:
- llama-cpp
- gguf-my-repo
base_model: FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-Flash-32B-Preview
---

# Triangle104/FuseO1-DeepSeekR1-QwQ-SkyT1-Flash-32B-Preview-Q3_K_L-GGUF
This model was converted to GGUF format from [`FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-Flash-32B-Preview`](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-Flash-32B-Preview) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-Flash-32B-Preview) for more details on the model.

---
FuseO1-Preview is our initial endeavor to enhance the System-II reasoning capabilities of large language models (LLMs) through innovative model fusion techniques. By employing our advanced SCE merging methodologies, we integrate multiple open-source o1-like LLMs into a unified model. Our goal is to incorporate the distinct knowledge and strengths from different reasoning LLMs into a single, unified model with strong System-II reasoning abilities, particularly in mathematics, coding, and science domains.


To achieve this, we conduct two types of model merging:

    Long-Long Reasoning Merging: This approach involves model fusion across LLMs that utilize long-CoT reasoning, with the goal of enhancing long-CoT reasoning capabilities. The resulted FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview achieves a Pass@1 accuracy of 74.0 on AIME24, demonstrating significant performance improvements compared to the OpenAI o1-preview (44.6) and OpenAI o1-mini (63.4), even approaching OpenAI o1 (79.2).
    Long-Short Reasoning Merging: This approach involves model fusion between long-CoT and short-CoT LLMs, aiming to improve reasoning capabilities in both long and short reasoning processes. The resulted FuseAI/FuseO1-DeepSeekR1-Qwen2.5-Instruct-32B-Preview and FuseAI/FuseO1-DeepSeekR1-Qwen2.5-Coder-32B-Preview is capable of utilizing both long and short reasoning processes and demonstrates relatively strong performance in long reasoning tasks.

---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo Triangle104/FuseO1-DeepSeekR1-QwQ-SkyT1-Flash-32B-Preview-Q3_K_L-GGUF --hf-file fuseo1-deepseekr1-qwq-skyt1-flash-32b-preview-q3_k_l.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/FuseO1-DeepSeekR1-QwQ-SkyT1-Flash-32B-Preview-Q3_K_L-GGUF --hf-file fuseo1-deepseekr1-qwq-skyt1-flash-32b-preview-q3_k_l.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```

Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```

Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/FuseO1-DeepSeekR1-QwQ-SkyT1-Flash-32B-Preview-Q3_K_L-GGUF --hf-file fuseo1-deepseekr1-qwq-skyt1-flash-32b-preview-q3_k_l.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/FuseO1-DeepSeekR1-QwQ-SkyT1-Flash-32B-Preview-Q3_K_L-GGUF --hf-file fuseo1-deepseekr1-qwq-skyt1-flash-32b-preview-q3_k_l.gguf -c 2048
```