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https://github.com/NVIDIA/TensorRT-LLM.git
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a6a2ae6cc1
* Rename nvsmall to nemotron NAS * Revert nvsmall to nemotron_nas rename in paths in tests that access llm_models_root/nvsmall/tests * Add NemotronNAS to pytorch supported models table Signed-off-by: Amit Zuker <203509407+amitz-nv@users.noreply.github.com>
66 lines
3.1 KiB
Markdown
66 lines
3.1 KiB
Markdown
# TRT-LLM with PyTorch
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Run the quick start script:
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```bash
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python3 quickstart.py
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```
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Run the advanced usage example script:
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```bash
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# BF16
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python3 quickstart_advanced.py --model_dir meta-llama/Llama-3.1-8B-Instruct
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# FP8
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python3 quickstart_advanced.py --model_dir nvidia/Llama-3.1-8B-Instruct-FP8
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# BF16 + TP=2
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python3 quickstart_advanced.py --model_dir meta-llama/Llama-3.1-8B-Instruct --tp_size 2
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# FP8 + TP=2
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python3 quickstart_advanced.py --model_dir nvidia/Llama-3.1-8B-Instruct-FP8 --tp_size 2
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# FP8(e4m3) kvcache
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python3 quickstart_advanced.py --model_dir nvidia/Llama-3.1-8B-Instruct-FP8 --kv_cache_dtype fp8
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```
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Run the multimodal example script:
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```bash
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# default inputs
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python3 quickstart_multimodal.py --model_dir Efficient-Large-Model/NVILA-8B --modality image [--use_cuda_graph]
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# user inputs
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# supported modes:
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# (1) N prompt, N media (N requests are in-flight batched)
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# (2) 1 prompt, N media
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# Note: media should be either image or video. Mixing image and video is not supported.
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python3 quickstart_multimodal.py --model_dir Efficient-Large-Model/NVILA-8B --modality video --prompt "Tell me what you see in the video briefly." "Describe the scene in the video briefly." --media "https://huggingface.co/datasets/Efficient-Large-Model/VILA-inference-demos/resolve/main/OAI-sora-tokyo-walk.mp4" "https://huggingface.co/datasets/Efficient-Large-Model/VILA-inference-demos/resolve/main/world.mp4" --max_tokens 128 [--use_cuda_graph]
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```
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## Supported Models
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| Architecture | Model | HuggingFace Example | Modality |
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|--------------|-------|---------------------|----------|
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| `BertForSequenceClassification` | BERT-based | `textattack/bert-base-uncased-yelp-polarity` | L |
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| `DeciLMForCausalLM` | Nemotron | `nvidia/Llama-3_1-Nemotron-51B-Instruct` | L |
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| `DeepseekV3ForCausalLM` | DeepSeek-V3 | `deepseek-ai/DeepSeek-V3 `| L |
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| `LlavaLlamaModel` | VILA | `Efficient-Large-Model/NVILA-8B` | L + V |
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| `LlavaNextForConditionalGeneration` | LLaVA-NeXT | `llava-hf/llava-v1.6-mistral-7b-hf` | L + V |
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| `LlamaForCausalLM` | Llama 3.1, Llama 3, Llama 2, LLaMA | `meta-llama/Meta-Llama-3.1-70B` | L |
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| `Llama4ForConditionalGeneration` | Llama 4 | `meta-llama/Llama-4-Scout-17B-16E-Instruct` | L |
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| `MistralForCausalLM` | Mistral | `mistralai/Mistral-7B-v0.1` | L |
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| `MixtralForCausalLM` | Mixtral | `mistralai/Mixtral-8x7B-v0.1` | L |
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| `MllamaForConditionalGeneration` | Llama 3.2 | `meta-llama/Llama-3.2-11B-Vision` | L |
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| `NemotronForCausalLM` | Nemotron-3, Nemotron-4, Minitron | `nvidia/Minitron-8B-Base` | L |
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| `NemotronNASForCausalLM` | NemotronNAS | `nvidia/Llama-3_3-Nemotron-Super-49B-v1` | L |
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| `Qwen2ForCausalLM` | QwQ, Qwen2 | `Qwen/Qwen2-7B-Instruct` | L |
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| `Qwen2ForProcessRewardModel` | Qwen2-based | `Qwen/Qwen2.5-Math-PRM-7B` | L |
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| `Qwen2ForRewardModel` | Qwen2-based | `Qwen/Qwen2.5-Math-RM-72B` | L |
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| `Qwen2VLForConditionalGeneration` | Qwen2-VL | `Qwen/Qwen2-VL-7B-Instruct` | L + V |
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| `Qwen2_5_VLForConditionalGeneration` | Qwen2.5-VL | `Qwen/Qwen2.5-VL-7B-Instruct` | L + V |
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Note:
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- L: Language only
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- L + V: Language and Vision multimodal support
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- Llama 3.2 accepts vision input, but our support currently limited to text only.
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