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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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- llama-3 |
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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/Llama-3.1-Nemotron-Nano-4B-v1.1 |
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datasets: |
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- nvidia/Llama-Nemotron-Post-Training-Dataset |
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--- |
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# Triangle104/Llama-3.1-Nemotron-Nano-4B-v1.1-Q8_0-GGUF |
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This model was converted to GGUF format from [`nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1`](https://huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1) 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/Llama-3.1-Nemotron-Nano-4B-v1.1) for more details on the model. |
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--- |
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Llama-3.1-Nemotron-Nano-4B-v1.1 is a large language model (LLM) which is a derivative of nvidia/Llama-3.1-Minitron-4B-Width-Base, which is created from Llama 3.1 8B using our LLM compression technique and offers improvements in model accuracy and efficiency. It is a reasoning model that is post trained for reasoning, human chat preferences, and tasks, such as RAG and tool calling. |
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Llama-3.1-Nemotron-Nano-4B-v1.1 is a model which offers a great tradeoff between model accuracy and efficiency. The model fits on a single RTX GPU and can be used locally. The model supports a context length of 128K. |
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This model underwent a multi-phase post-training process to enhance both its reasoning and non-reasoning capabilities. This includes a supervised fine-tuning stage for Math, Code, Reasoning, and Tool Calling as well as multiple reinforcement learning (RL) stages using Reward-aware Preference Optimization (RPO) algorithms for both chat and instruction-following. The final model checkpoint is obtained after merging the final SFT and RPO checkpoints |
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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/Llama-3.1-Nemotron-Nano-4B-v1.1-Q8_0-GGUF --hf-file llama-3.1-nemotron-nano-4b-v1.1-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/Llama-3.1-Nemotron-Nano-4B-v1.1-Q8_0-GGUF --hf-file llama-3.1-nemotron-nano-4b-v1.1-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/Llama-3.1-Nemotron-Nano-4B-v1.1-Q8_0-GGUF --hf-file llama-3.1-nemotron-nano-4b-v1.1-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/Llama-3.1-Nemotron-Nano-4B-v1.1-Q8_0-GGUF --hf-file llama-3.1-nemotron-nano-4b-v1.1-q8_0.gguf -c 2048 |
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``` |
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