Update README.md

README.md

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 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.
+
+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.
+
+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)