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73951012..08457d5e 100644 --- a/docs/tutorial_slm.md +++ b/docs/tutorial_slm.md @@ -82,3 +82,47 @@ You can also load JSON files containing prompt sequences, like with [`--prompt / •   The model responses are with 4-bit quantization, and are truncated to 256 tokens for brevity. •   These chat questions are from [`/data/prompts/qa.json`](https://github.com/dusty-nv/jetson-containers/blob/master/data/prompts/qa.json){:target="_blank"} (found in jetson-containers) +### Nemotron Mini + +[`Nemotron-Mini-4B-Instruct`](https://huggingface.co/nvidia/Nemotron-Mini-4B-Instruct) is a 4B SLM tuned for chat, RAG, and function calling and is based on [Minitron-4B](https://huggingface.co/nvidia/Minitron-4B-Base) (pruned and distilled from [Nemotron4 15B](https://arxiv.org/abs/2402.16819)) + +It is supported in HuggingFace Transformers and llama.cpp. Here's an example of running a local OpenAI-compatible server with 4-bit quantized GGUF: + +```bash +jetson-containers run $(autotag llama_cpp) \ + llama-server \ + --hf-repo Obenlia/Nemotron-Mini-4B-Instruct-Q4_K_M-GGUF \ + --hf-file nemotron-mini-4b-instruct-q4_k_m.gguf \ + --gpu-layers 34 \ + --seed 42 \ + --host 0.0.0.0 \ + --port 8080 +``` + +For a quick test, you can navigate your browser to `http://JETSON_IP:8080`, connect other clients like [Open WebUI](https://github.com/open-webui/open-webui), or have your application send requests to the server's OpenAI chat completion endpoints (i.e. from [openai-python](https://github.com/openai/openai-python), REST, JavaScript, ect) + + + +You can more easily see the performance with the `llama-cli` tool: + +```bash +jetson-containers run $(autotag llama_cpp) \ + llama-cli \ + --hf-repo Obenlia/Nemotron-Mini-4B-Instruct-Q4_K_M-GGUF \ + --hf-file nemotron-mini-4b-instruct-q4_k_m.gguf \ + --gpu-layers 34 \ + --seed 42 \ + --ignore-eos \ + -n 128 \ + -p "The meaning to life and the universe is" +``` + +``` title="Jetson AGX Orin" +llama_print_timings: load time = 1408.27 ms +llama_print_timings: sample time = 70.05 ms / 128 runs ( 0.55 ms per token, 1827.32 tokens per second) +llama_print_timings: prompt eval time = 120.08 ms / 9 tokens ( 13.34 ms per token, 74.95 tokens per second) +llama_print_timings: eval time = 3303.93 ms / 127 runs ( 26.02 ms per token, 38.44 tokens per second) +llama_print_timings: total time = 3597.17 ms / 136 tokens +``` + +The model can also be previewed in the cloud at [build.nvidia.com](https://build.nvidia.com/nvidia/nemotron-mini-4b-instruct) (example client requests for OpenAI API are also found there) \ No newline at end of file