Open wangschang opened 11 months ago
same question here
Same issue. I think this is because model_executor/models/qwen.py
is only for Qwen-7B-Chat, not compatible with Qwen-VL.
https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/qwen.py
same issue
有解决了没
same issue
Please stop saying same issue, just react to the original message to show your support
For text-only inputs, we can run the model with this patch https://github.com/vllm-project/vllm/pull/5710 .
I am looking into adding support for image inputs for Qwen-VL/Qwen-VL-Chat 😄
when i use Qwen/Qwen-VL-Chat I do not know why!
throw a error
Traceback (most recent call last): File "test.py", line 20, in <module> model = LLM(model=model_path, tokenizer=model_path,tokenizer_mode='slow',tensor_parallel_size=1,trust_remote_code=True) File "/usr/local/miniconda3/lib/python3.8/site-packages/vllm/entrypoints/llm.py", line 66, in __init__ self.llm_engine = LLMEngine.from_engine_args(engine_args) File "/usr/local/miniconda3/lib/python3.8/site-packages/vllm/engine/llm_engine.py", line 220, in from_engine_args engine = cls(*engine_configs, File "/usr/local/miniconda3/lib/python3.8/site-packages/vllm/engine/llm_engine.py", line 101, in __init__ self._init_workers(distributed_init_method) File "/usr/local/miniconda3/lib/python3.8/site-packages/vllm/engine/llm_engine.py", line 133, in _init_workers self._run_workers( File "/usr/local/miniconda3/lib/python3.8/site-packages/vllm/engine/llm_engine.py", line 470, in _run_workers output = executor(*args, **kwargs) File "/usr/local/miniconda3/lib/python3.8/site-packages/vllm/worker/worker.py", line 67, in init_model self.model = get_model(self.model_config) File "/usr/local/miniconda3/lib/python3.8/site-packages/vllm/model_executor/model_loader.py", line 57, in get_model model.load_weights(model_config.model, model_config.download_dir, File "/usr/local/miniconda3/lib/python3.8/site-packages/vllm/model_executor/models/qwen.py", line 308, in load_weights param = state_dict[name] KeyError: 'transformer.visual.positional_embedding'
the code is
`from vllm import LLM, SamplingParams from transformers import AutoModelForCausalLM, AutoTokenizer,AutoConfig import time
model_path="Qwen/Qwen-VL-Chat"
model = LLM(model=model_path, tokenizer=model_path,tokenizer_mode='slow',tensor_parallel_size=1,trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained(model_path, legacy=True, trust_remote_code=True)
sampling_params = SamplingParams(temperature=0,max_tokens=8096) start=time.time() prompts = ["你好!"] outputs = model.generate(prompts, sampling_params) end = time.time() for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text length = len(generated_text) print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") print(end-start) cost = end-start print(f"{length/cost}tokens/s")`