---
license: apache-2.0
tags:
- llama-cpp
- gguf-my-repo
base_model: internlm/OREAL-DeepSeek-R1-Distill-Qwen-7B
---

# Triangle104/OREAL-DeepSeek-R1-Distill-Qwen-7B-Q4_K_S-GGUF
This model was converted to GGUF format from [`internlm/OREAL-DeepSeek-R1-Distill-Qwen-7B`](https://huggingface.co/internlm/OREAL-DeepSeek-R1-Distill-Qwen-7B) 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/internlm/OREAL-DeepSeek-R1-Distill-Qwen-7B) for more details on the model.

---
Introduction
	



We introduce OREAL-7B and OREAL-32B, a mathematical reasoning model series trained using Outcome REwArd-based reinforcement Learning, a novel RL framework designed for tasks where only binary outcome rewards are available. 


With OREAL, a 7B model achieves 94.0 pass@1 accuracy on MATH-500, matching the performance of previous 32B models. OREAL-32B further surpasses previous distillation-trained 32B models, reaching 95.0 pass@1 accuracy on MATH-500.  





Our method leverages best-of-N (BoN) sampling for behavior cloning 
and reshapes negative sample rewards to ensure gradient consistency. 
Also, to address the challenge of sparse rewards in long 
chain-of-thought reasoning, we incorporate an on-policy token-level 
reward model that identifies key tokens in reasoning trajectories for 
importance sampling. For more details, please refer to our paper.

---
## 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/OREAL-DeepSeek-R1-Distill-Qwen-7B-Q4_K_S-GGUF --hf-file oreal-deepseek-r1-distill-qwen-7b-q4_k_s.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/OREAL-DeepSeek-R1-Distill-Qwen-7B-Q4_K_S-GGUF --hf-file oreal-deepseek-r1-distill-qwen-7b-q4_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/OREAL-DeepSeek-R1-Distill-Qwen-7B-Q4_K_S-GGUF --hf-file oreal-deepseek-r1-distill-qwen-7b-q4_k_s.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/OREAL-DeepSeek-R1-Distill-Qwen-7B-Q4_K_S-GGUF --hf-file oreal-deepseek-r1-distill-qwen-7b-q4_k_s.gguf -c 2048
```