nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16开源了新模型

NVIDIA nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16 nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16 https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16

nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16 · Hugging Face Hugging Face (https://huggingface.co/) Models (https://huggingface.co/models) Datasets (https://huggingface.co/datasets) Spaces (https://huggingface.co/spaces) Buckets new (https://huggingface.co/storage) Docs (https://huggingface.co/docs) Enterprise (https://huggingface.co/enterprise) Pricing (https://huggingface.co/pricing) Website Tasks (https://huggingface.co/tasks) HuggingChat (https://huggingface.co/chat) Collections (https://huggingface.co/collections) Languages (https://huggingface.co/languages) Organizations (https://huggingface.co/organizations) Community Blog (https://huggingface.co/blog) Posts (https://huggingface.co/posts) Daily Papers (https://huggingface.co/papers) Hardware (https://huggingface.co/hardware) Learn (https://huggingface.co/learn) Discord (https://huggingface.co/join/discord) Forum (https://discuss.huggingface.co/) GitHub (https://github.com/huggingface) Solutions Team & Enterprise (https://huggingface.co/enterprise) Hugging Face PRO (https://huggingface.co/pro) Enterprise Support (https://huggingface.co/support) Inference Providers (https://huggingface.co/inference/models) Inference Endpoints (https://huggingface.co/inference-endpoints) Storage Buckets (https://huggingface.co/storage) Log In (https://huggingface.co/login) Sign Up (https://huggingface.co/join) (https://huggingface.co/nvidia) nvidia (https://huggingface.co/nvidia) / NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16 (https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16) like 14 Follow NVIDIA 65.5k Text Generation (https://huggingface.co/models?pipeline_tag=text-generation) Transformers (https://huggingface.co/models?library=transformers) Safetensors (https://huggingface.co/models?library=safetensors) PyTorch (https://huggingface.co/models?library=pytorch) nvidia/nemotron-pre-training-datasets 6 languages nemotron_h (https://huggingface.co/models?other=nemotron_h) nvidia (https://huggingface.co/models?other=nvidia) nemotron-3.5 (https://huggingface.co/models?other=nemotron-3.5) mtp (https://huggingface.co/models?other=mtp) License: openmdw-1.1 Model card (https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16) Files Files and versions xet (https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16/tree/main) Community 1 (https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16/discussions) Deploy Copy to bucket new Use this model Instructions to use nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started. Libraries Transformers (https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16?library=transformers) How to use nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16 with Transformers: # Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16") # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16") model = AutoModelForCausalLM.from_pretrained("nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16", device_map="auto") Notebooks Google Colab (https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16/colab) Kaggle (https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16/kaggle) Local Apps Settings (https://huggingface.co/settings/local-apps) vLLM (https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16?local-app=vllm) How to use nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16 with vLLM: Install from pip and serve model # Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve