Comments (3)
history=None for i in range(len(raw_prompts)): # len(raw_prompts) = 100 q = raw_prompts[i] response, history = vllm_model.chat(query=q, history=history) print(response) history = history[:10]
之前没有用vllm,100个prompts大约是52s,使用vllm之后仍然是52s左右,似乎没有提速? 请问有人能帮忙看一下吗?
history=None for i in range(len(raw_prompts)): # len(raw_prompts) = 100 q = raw_prompts[i] response, history = vllm_model.chat(query=q, history=history) print(response) history = history[:10]
之前没有用vllm,100个prompts大约是52s,使用vllm之后仍然是52s左右,似乎没有提速? 请问有人能帮忙看一下吗?
你好,想问一下千问模型部署好了再用vllm加速吗
from qwen-vllm.
history=None for i in range(len(raw_prompts)): # len(raw_prompts) = 100 q = raw_prompts[i] response, history = vllm_model.chat(query=q, history=history) print(response) history = history[:10]
之前没有用vllm,100个prompts大约是52s,使用vllm之后仍然是52s左右,似乎没有提速? 请问有人能帮忙看一下吗?
history=None for i in range(len(raw_prompts)): # len(raw_prompts) = 100 q = raw_prompts[i] response, history = vllm_model.chat(query=q, history=history) print(response) history = history[:10]
之前没有用vllm,100个prompts大约是52s,使用vllm之后仍然是52s左右,似乎没有提速? 请问有人能帮忙看一下吗?
你好,想问一下千问模型部署好了再用vllm加速吗
一般都是直接用vllm框架部署qwen的
from qwen-vllm.
from qwen-vllm.
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