Run Llama-7B
python3 -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000
Run Mixtral-8x7B
(When there is a CUDA out-of-memory error, try to reduce the --mem-fraction-static
)
python3 -m sglang.launch_server --model-path mistralai/Mixtral-8x7B-Instruct-v0.1 --port 30000 --tp-size 8
Benchmark(short output)
python3 bench_sglang.py --tokenizer meta-llama/Llama-2-7b-chat-hf
Benchmark(long output)
python3 bench_sglang.py --tokenizer meta-llama/Llama-2-7b-chat-hf --long
Run Llama-7B
python3 -m vllm.entrypoints.api_server --tokenizer-mode auto --model meta-llama/Llama-2-7b-chat-hf --disable-log-requests --port 21000
Run Mixtral-8x7B
python3 -m vllm.entrypoints.api_server --tokenizer-mode auto --model mistralai/Mixtral-8x7B-Instruct-v0.1 --disable-log-requests --port 21000 --tensor-parallel-size 8
Benchmark(short output)
python3 bench_other.py --tokenizer meta-llama/Llama-2-7b-chat-hf --backend vllm
Benchmark(long output)
python3 bench_other.py --tokenizer meta-llama/Llama-2-7b-chat-hf --backend vllm --long
Benchmark Llama-7B (short output)
python3 bench_other.py --tokenizer meta-llama/Llama-2-7b-chat-hf --backend guidance --parallel 1 --n-ctx 4096 --model-path path/to/gguf
Benchmark Llama-7B (long output)
python3 bench_other.py --tokenizer meta-llama/Llama-2-7b-chat-hf --backend guidance --parallel 1 --n-ctx 4096 --model-path path/to/gguf --long