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1.1 KiB
1.1 KiB
TRT-LLM with PyTorch
Run the quick start script:
python3 quickstart.py
Run the advanced usage example script:
# BF16
python3 quickstart_advanced.py --model_dir meta-llama/Llama-3.1-8B-Instruct
# FP8
python3 quickstart_advanced.py --model_dir nvidia/Llama-3.1-8B-Instruct-FP8
# BF16 + TP=2
python3 quickstart_advanced.py --model_dir meta-llama/Llama-3.1-8B-Instruct --tp_size 2
# FP8 + TP=2
python3 quickstart_advanced.py --model_dir nvidia/Llama-3.1-8B-Instruct-FP8 --tp_size 2
# FP8(e4m3) kvcache
python3 quickstart_advanced.py --model_dir nvidia/Llama-3.1-8B-Instruct-FP8 --kv_cache_dtype fp8
Run the multimodal example script:
# default inputs
python3 quickstart_multimodal.py --model_dir Efficient-Large-Model/NVILA-8B
# user inputs
python3 quickstart_multimodal.py --model_dir Efficient-Large-Model/NVILA-8B --prompt "Describe the scene" "What do you see in the image?" --data "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/seashore.png" "https://huggingface.co/datasets/Sayali9141/traffic_signal_images/resolve/main/61.jpg" --max_tokens 64