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-14B
---

# Triangle104/AceReason-Nemotron-14B-Q8_0-GGUF
This model was converted to GGUF format from [`nvidia/AceReason-Nemotron-14B`](https://huggingface.co/nvidia/AceReason-Nemotron-14B) 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-14B) for more details on the model.

---
We're thrilled to introduce AceReason-Nemotron-14B, a math and code 
reasoning model trained entirely through reinforcement learning (RL), 
starting from the DeepSeek-R1-Distilled-Qwen-14B. It delivers impressive
 results, achieving 78.6% on AIME 2024 (+8.9%), 67.4% on AIME 2025 
(+17.4%), 61.1% on LiveCodeBench v5 (+8%), 54.9% on LiveCodeBench v6 
(+7%), and 2024 on Codeforces (+543). 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-14B-Q8_0-GGUF --hf-file acereason-nemotron-14b-q8_0.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/AceReason-Nemotron-14B-Q8_0-GGUF --hf-file acereason-nemotron-14b-q8_0.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-14B-Q8_0-GGUF --hf-file acereason-nemotron-14b-q8_0.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/AceReason-Nemotron-14B-Q8_0-GGUF --hf-file acereason-nemotron-14b-q8_0.gguf -c 2048
```