README.md

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

# Triangle104/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview-Q3_K_S-GGUF
This model was converted to GGUF format from [`FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview`](https://huggingface.co./FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-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-SkyT1-32B-Preview) for more details on the model.

---
Overview
-



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.

o 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.

Long-Long Reasoning Merging
	



We conduct experiments on these folloing long-cot LLMs.


deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
Qwen/QwQ-32B-Preview
NovaSky-AI/Sky-T1-32B-Preview


To reproduce the merged FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview model, using the script below.


cd FuseAI/FuseO1-Preview/mergekit
pip3 install -e .
model_save_dir=xx # your path to save the merged models
mergekit-yaml fuseo1_configs/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview.yaml ${model_save_dir}/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview --cudas



To reproduce the merged FuseAI/FuseO1-DeepSeekR1-QwQ-32B-Preview model, using the script below.


cd FuseAI/FuseO1-Preview/mergekit
pip3 install -e .
model_save_dir=xxx # your path to save the merged models
mergekit-yaml fuseo1_configs/FuseO1-DeepSeekR1-QwQ-32B-Preview.yaml ${model_save_dir}/FuseO1-DeepSeekR1-QwQ-32B-Preview --cuda



We provide the example code to use FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview.


from vllm import LLM, SamplingParams

llm = LLM(model="FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview", tensor_parallel_size=8)
sampling_params = SamplingParams(max_tokens=32768, temperature=0.7, stop=["<|im_end|>", "<｜end▁of▁sentence｜>"], stop_token_ids=[151645, 151643])

conversations = [
    [
        {"role": "system", "content": "Please reason step by step, and put your final answer within \\boxed{{}}."},
        {"role": "user", "content": "Quadratic polynomials $P(x)$ and $Q(x)$ have leading coefficients $2$ and $-2,$ respectively. The graphs of both polynomials pass through the two points $(16,54)$ and $(20,53).$ Find $P(0) + Q(0).$."},
    ],
]

responses = llm.chat(messages=conversations, sampling_params=sampling_params, use_tqdm=True)

for response in responses:
    print(response.outputs[0].text.strip())




	
		
	

		Long-Short Reasoning Merging
	



We conduct experiments on these folloing long-cot and short-cot LLMs.


deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
Qwen/Qwen2.5-32B-Instruct
Qwen/Qwen2.5-32B-Coder


To reproduce the merged FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-Flash-32B-Preview model, using the script below.


cd FuseAI/FuseO1-Preview/mergekit
pip3 install -e .
model_save_dir=xxx # your path to save the merged models
mergekit-yaml fuseo1_configs/FuseO1-DeepSeekR1-QwQ-SkyT1-Flash-32B-Preview.yaml ${model_save_dir}/FuseO1-DeepSeekR1-QwQ-SkyT1-Flash-32B-Preview --cuda



To reproduce the merged FuseAI/FuseO1-DeepSeekR1-Qwen2.5-Instruct-32B-Preview model, using the script below.


cd FuseAI/FuseO1-Preview/mergekit
pip3 install -e .
model_save_dir=xxx # your path to save the merged models
mergekit-yaml fuseo1_configs/FuseO1-DeepSeekR1-Qwen2.5-Instruct-32B-Preview.yaml ${model_save_dir}/FuseO1-DeepSeekR1-Qwen2.5-Instruct-32B-Preview --cuda



To reproduce the merged FuseAI/FuseO1-DeepSeekR1-Qwen2.5-Coder-32B-Preview model, using the script below.


cd FuseAI/FuseO1-Preview/mergekit
pip3 install -e .
model_save_dir=xxx # your path to save the merged models
mergekit-yaml fuseo1_configs/FuseO1-DeepSeekR1-Qwen2.5-Coder-32B-Preview.yaml ${model_save_dir}/FuseO1-DeepSeekR1-Qwen2.5-Coder-32B-Preview --cuda



We provide the code to use FuseAI/FuseO1-DeepSeekR1-Qwen2.5-Instruct-32B-Preview.


from vllm import LLM, SamplingParams

llm = LLM(model="FuseAI/FuseO1-DeepSeekR1-Qwen2.5-Instruct-32B-Preview", tensor_parallel_size=8)
sampling_params = SamplingParams(max_tokens=32768, temperature=0.7, stop=["<|im_end|>", "<｜end▁of▁sentence｜>"], stop_token_ids=[151645, 151643])

conversations = [
    [
        {"role": "system", "content": "Please reason step by step, and put your final answer within \\boxed{{}}."},
        {"role": "user", "content": "Quadratic polynomials $P(x)$ and $Q(x)$ have leading coefficients $2$ and $-2,$ respectively. The graphs of both polynomials pass through the two points $(16,54)$ and $(20,53).$ Find $P(0) + Q(0).$."},
    ],
]

responses = llm.chat(messages=conversations, sampling_params=sampling_params, use_tqdm=True)

for response in responses:
    print(response.outputs[0].text.strip())

---
## 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-SkyT1-32B-Preview-Q3_K_S-GGUF --hf-file fuseo1-deepseekr1-qwq-skyt1-32b-preview-q3_k_s.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview-Q3_K_S-GGUF --hf-file fuseo1-deepseekr1-qwq-skyt1-32b-preview-q3_k_s.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-SkyT1-32B-Preview-Q3_K_S-GGUF --hf-file fuseo1-deepseekr1-qwq-skyt1-32b-preview-q3_k_s.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview-Q3_K_S-GGUF --hf-file fuseo1-deepseekr1-qwq-skyt1-32b-preview-q3_k_s.gguf -c 2048
```