Nvidia Nemotron 3.5 Lightning

like 9 Follow NVIDIA 65.2k Text Generation Transformers Safetensors PyTorch 6 languages nemotron_h nvidia nemotron-3.5 conversational 8-bit precision modelopt License: openmdw-1.1 Model card Files Files and versions xet Community 2 Deploy Copy to bucket new Use this model Instructions to use nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started. Libraries Transformers How to use nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 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-NVFP4") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages) # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4") model = AutoModelForCausalLM.from_pretrained("nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) Notebooks Google Colab Kaggle Local Apps Settings vLLM How to use nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 with vLLM: