trtllm-build --checkpoint_dir ./dummy_llama_converted_ckpt --output_dir ./dummy_llama_engine --max_batch_size 1 --max_input_len 1024 --max_seq_len 2048 --kv_cache_type disabled --gpt_attention_plugin disable --context_fmha disable --remove_input_padding disable --log_level verbose -...
# int8 for example [with inflight batching] python /app/tensorrt_llm/examples/baichuan/build.py \ --model_version v2_13b \ --model_dir /models/download/Baichuan2-13B-Chat \ --output_dir /models/baichuan/tensorrt_llm/1 \ --max_input_len 4096 \ --max_output_len 1024 \ --dtype floa...
(**inputs,max_length=128) total_time = () - start_time generated_tokens = len(outputs[0]) - len(inputs["input_ids"][0]) tokens_per_second = generated_tokens / total_time response = (outputs[0], skip_special_tokens=True) print("\n\n--- Response ---") print(f"{response}"...
It also helps with build time. # --tp_size and --pp_size are the model shard size trtllm-build \ --checkpoint_dir ./phi-checkpoint \ --output_dir ./phi-engine \ --gemm_plugin float16 \ --max_batch_size 8 \ --max_input_len 1024 \ --max_seq_len 2048 \ --tp_size 1 \...
build --checkpoint_dir /tmp/Qwen2.5-7B-Instruct/tllm_checkpoint/ \ --output_dir /tmp/Qwen2.5-7B-Instruct/trt_engines/ \ --gemm_plugin bfloat16 --max_batch_size 16 --paged_kv_cache enable \ --max_input_len 32256 --max_seq_len 32768 --max_num_tokens 32256 # 运行测试 python3 ....
25 + trtllm-build --checkpoint_dir /tmp/QwQ-32B-Preview/tllm_checkpoint/ \ 26 + --output_dir /tmp/QwQ-32B-Preview/trt_engines/ \ 27 + --gemm_plugin bfloat16 --max_batch_size 16 --paged_kv_cache enable --use_paged_context_fmha enable \ 28 + --max_input_len 32256 --...
# 设置max_input_len为6k,max_seq_len为8k(即最大输出为2k)。 rm -rf /tmp/InternVideo2_5_Chat_8B/trt_engines/ trtllm-build --checkpoint_dir /tmp/InternVideo2_5_Chat_8B/tllm_checkpoint/ \ --output_dir /tmp/InternVideo2_5_Chat_8B/trt_engines/ \ --gemm_plugin bfloat16 --max_bat...
#构建引擎,注意这里为了24G单卡可以部署,减小了max_batch_size以及max_seq_len等参数 54+ rm -rf /tmp/QwQ-32B-AWQ/trt_engines/ 55+ trtllm-build --checkpoint_dir /tmp/QwQ-32B-AWQ/tllm_checkpoint/ \ 56+ --output_dir /tmp/QwQ-32B-AWQ/trt_engines/ \ ...
--output_dir ./tmp/llama/8B/trt_engines/bf16/1-gpu \ --gpt_attention_plugin bfloat16 \ --gemm_plugin bfloat16 \ --max_batch_size 2048 \ --max_input_len 2048 \ --max_num_tokens 2048 \ --multiple_profiles enable \ --paged_kv_cache enable \ --use_paged_context_fmha enable ...
trtllm-build --checkpoint_dir $path_engine --output_dir $path_engine/1-gpu/ --gemm_plugin auto --max_batch_size 1 Using the above engine, you can run LLM inference using the following commands: cd TensorRT-LLM/examples/ python run.py --max_output_len 128 --engine_dir $path_engine...