NVIDIA 发布 Nemotron 教师竞赛编码模型

NVIDIA nvidia/NVIDIA-Nemotron-Labs-Teacher-Competition-Coding nvidia/NVIDIA-Nemotron-Labs-Teacher-Competition-Coding https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-Teacher-Competition-Coding

nvidia/NVIDIA-Nemotron-Labs-Teacher-Competition-Coding · 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-Labs-Teacher-Competition-Coding (https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-Teacher-Competition-Coding) like 5 Follow NVIDIA 66.2k 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-post-training-v3 nvidia/nemotron-pre-training-datasets 12 languages nemotron_h (https://huggingface.co/models?other=nemotron_h) nvidia (https://huggingface.co/models?other=nvidia) nemotron-3 (https://huggingface.co/models?other=nemotron-3) latent-moe (https://huggingface.co/models?other=latent-moe) mtp (https://huggingface.co/models?other=mtp) conversational (https://huggingface.co/models?other=conversational) custom_code (https://huggingface.co/models?other=custom_code) License: openmdw-1.1 Model card (https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-Teacher-Competition-Coding) Files Files and versions xet (https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-Teacher-Competition-Coding/tree/main) Community (https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-Teacher-Competition-Coding/discussions) Deploy Copy to bucket new Use this model Instructions to use nvidia/NVIDIA-Nemotron-Labs-Teacher-Competition-Coding with libraries, inference providers, notebooks, and local apps. Follow these links to get started. Libraries Transformers (https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-Teacher-Competition-Coding?library=transformers) How to use nvidia/NVIDIA-Nemotron-Labs-Teacher-Competition-Coding with Transformers: # Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nvidia/NVIDIA-Nemotron-Labs-Teacher-Competition-Coding", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages) # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("nvidia/NVIDIA-Nemotron-Labs-Teacher-Competition-Coding", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("nvidia/NVIDIA-Nemotron-Labs-Teacher-Competition-Coding", trust_remote_code=True, 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