README.md

---
library_name: transformers
license: other
license_name: nvidia-open-model-license
license_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
pipeline_tag: text-generation
language:
- en
tags:
- nvidia
- reasoning
- math
- code
- reinforcement learning
- pytorch
- llama-cpp
- gguf-my-repo
base_model: nvidia/AceReason-Nemotron-7B
---

# Triangle104/AceReason-Nemotron-7B-Q4_K_S-GGUF
This model was converted to GGUF format from [`nvidia/AceReason-Nemotron-7B`](https://huggingface.co/nvidia/AceReason-Nemotron-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/nvidia/AceReason-Nemotron-7B) for more details on the model.

---
We're thrilled to introduce AceReason-Nemotron-7B, a math and code reasoning model trained entirely through reinforcement learning (RL), starting from the DeepSeek-R1-Distilled-Qwen-7B. It delivers impressive results, achieving 69.0% on AIME 2024 (+14.5%), 53.6% on AIME 2025 (+17.4%), 51.8% on LiveCodeBench v5 (+8%), 44.1% on LiveCodeBench v6 (+7%). We systematically study the RL training process through extensive ablations and propose a simple yet effective approach: first RL training on math-only prompts, then RL training on code-only prompts. Notably, we find that math-only RL not only significantly enhances the performance of strong distilled models on math benchmarks, but also code reasoning tasks. In addition, extended code-only RL further improves code benchmark performance while causing minimal degradation in math results. We find that RL not only elicits the foundational reasoning capabilities acquired during pre-training and supervised fine-tuning (e.g., distillation), but also pushes the limits of the model's reasoning ability, enabling it to solve problems that were previously unsolvable.

---
## 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/AceReason-Nemotron-7B-Q4_K_S-GGUF --hf-file acereason-nemotron-7b-q4_k_s.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/AceReason-Nemotron-7B-Q4_K_S-GGUF --hf-file acereason-nemotron-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/AceReason-Nemotron-7B-Q4_K_S-GGUF --hf-file acereason-nemotron-7b-q4_k_s.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/AceReason-Nemotron-7B-Q4_K_S-GGUF --hf-file acereason-nemotron-7b-q4_k_s.gguf -c 2048
```