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--- |
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library_name: transformers |
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license: other |
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license_name: nvidia-open-model-license |
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license_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/ |
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pipeline_tag: text-generation |
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language: |
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- en |
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tags: |
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- nvidia |
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- reasoning |
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- math |
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- code |
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- reinforcement learning |
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- pytorch |
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- llama-cpp |
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- gguf-my-repo |
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base_model: nvidia/AceReason-Nemotron-14B |
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--- |
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# Triangle104/AceReason-Nemotron-14B-Q8_0-GGUF |
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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. |
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Refer to the [original model card](https://huggingface.co/nvidia/AceReason-Nemotron-14B) for more details on the model. |
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--- |
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We're thrilled to introduce AceReason-Nemotron-14B, a math and code |
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reasoning model trained entirely through reinforcement learning (RL), |
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starting from the DeepSeek-R1-Distilled-Qwen-14B. It delivers impressive |
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results, achieving 78.6% on AIME 2024 (+8.9%), 67.4% on AIME 2025 |
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(+17.4%), 61.1% on LiveCodeBench v5 (+8%), 54.9% on LiveCodeBench v6 |
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(+7%), and 2024 on Codeforces (+543). We systematically study the RL |
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training process through extensive ablations and propose a simple yet |
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effective approach: first RL training on math-only prompts, then RL |
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training on code-only prompts. Notably, we find that math-only RL not |
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only significantly enhances the performance of strong distilled models |
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on math benchmarks, but also code reasoning tasks. In addition, extended |
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code-only RL further improves code benchmark performance while causing |
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minimal degradation in math results. We find that RL not only elicits |
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the foundational reasoning capabilities acquired during pre-training and |
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supervised fine-tuning (e.g., distillation), but also pushes the limits |
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of the model's reasoning ability, enabling it to solve problems that |
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were previously unsolvable. |
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--- |
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## Use with llama.cpp |
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Install llama.cpp through brew (works on Mac and Linux) |
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```bash |
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brew install llama.cpp |
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``` |
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Invoke the llama.cpp server or the CLI. |
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### CLI: |
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```bash |
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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" |
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``` |
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### Server: |
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```bash |
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llama-server --hf-repo Triangle104/AceReason-Nemotron-14B-Q8_0-GGUF --hf-file acereason-nemotron-14b-q8_0.gguf -c 2048 |
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``` |
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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. |
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Step 1: Clone llama.cpp from GitHub. |
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``` |
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git clone https://github.com/ggerganov/llama.cpp |
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``` |
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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). |
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``` |
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cd llama.cpp && LLAMA_CURL=1 make |
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``` |
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Step 3: Run inference through the main binary. |
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``` |
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./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" |
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``` |
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or |
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``` |
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./llama-server --hf-repo Triangle104/AceReason-Nemotron-14B-Q8_0-GGUF --hf-file acereason-nemotron-14b-q8_0.gguf -c 2048 |
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``` |
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