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| .. | ||
| _tensorrt_engine | ||
| out_of_tree_example | ||
| extra-llm-api-config.yml | ||
| llm_guided_decoding.py | ||
| llm_inference_async_streaming.py | ||
| llm_inference_async.py | ||
| llm_inference_distributed.py | ||
| llm_inference.py | ||
| llm_kv_cache_connector.py | ||
| llm_kv_cache_offloading.py | ||
| llm_logits_processor.py | ||
| llm_mgmn_llm_distributed.sh | ||
| llm_mgmn_trtllm_bench.sh | ||
| llm_mgmn_trtllm_serve.sh | ||
| llm_multilora.py | ||
| llm_runtime.py | ||
| llm_sampling.py | ||
| llm_sparse_attention.py | ||
| llm_speculative_decoding.py | ||
| quickstart_advanced.py | ||
| quickstart_example.py | ||
| quickstart_multimodal.py | ||
| README.md | ||
| star_attention.py | ||
LLM API Examples
Please refer to the official documentation including customization for detailed information and usage guidelines regarding the LLM API.
Run the advanced usage example script:
# 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
# BF16 + TP=8
python3 quickstart_advanced.py --model_dir nvidia/Llama-3_1-Nemotron-Ultra-253B-v1 --tp_size 8
# Nemotron-H requires disabling cache reuse in kv cache
python3 quickstart_advanced.py --model_dir nvidia/Nemotron-H-8B-Base-8K --disable_kv_cache_reuse --max_batch_size 8
Run the multimodal example script:
# default inputs
python3 quickstart_multimodal.py --model_dir Efficient-Large-Model/NVILA-8B --modality image [--use_cuda_graph]
# user inputs
# supported modes:
# (1) N prompt, N media (N requests are in-flight batched)
# (2) 1 prompt, N media
# Note: media should be either image or video. Mixing image and video is not supported.
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]
Run the speculative decoding script:
# NGram drafter
python3 quickstart_advanced.py \
--model_dir meta-llama/Llama-3.1-8B-Instruct \
--spec_decode_algo NGRAM \
--spec_decode_max_draft_len 4 \
--max_matching_ngram_size 2 \
--disable_overlap_scheduler \
--disable_kv_cache_reuse
# Draft Target
python3 quickstart_advanced.py \
--model_dir meta-llama/Llama-3.1-8B-Instruct \
--spec_decode_algo draft_target \
--spec_decode_max_draft_len 5 \
--draft_model_dir meta-llama/Llama-3.2-1B-Instruct \
--disable_overlap_scheduler \
--disable_kv_cache_reuse