Text Generation
Transformers
Safetensors
English
qwen2
chat
conversational
Eval Results
text-generation-inference
Instructions to use Qwen/Qwen2-72B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Qwen/Qwen2-72B-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Qwen/Qwen2-72B-Instruct") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-72B-Instruct") model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-72B-Instruct") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Qwen/Qwen2-72B-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Qwen/Qwen2-72B-Instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Qwen/Qwen2-72B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Qwen/Qwen2-72B-Instruct
- SGLang
How to use Qwen/Qwen2-72B-Instruct with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Qwen/Qwen2-72B-Instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Qwen/Qwen2-72B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Qwen/Qwen2-72B-Instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Qwen/Qwen2-72B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Qwen/Qwen2-72B-Instruct with Docker Model Runner:
docker model run hf.co/Qwen/Qwen2-72B-Instruct
The sample code could not run...
#16
by zhiminy - opened
I attempt to run the sample code given by HF:
It throws the following error:
KeyError Traceback (most recent call last)
Cell In[5], line 7
2 from transformers import pipeline
4 messages = [
5 {"role": "user", "content": "Who are you?"},
6 ]
----> 7 pipe = pipeline("text-generation", model="Qwen/Qwen2-72B-Instruct")
8 pipe(messages)
File ~/.local/lib/python3.10/site-packages/transformers/pipelines/__init__.py:751, in pipeline(task, model, config, tokenizer, feature_extractor, image_processor, framework, revision, use_fast, token, device, device_map, torch_dtype, trust_remote_code, model_kwargs, pipeline_class, **kwargs)
748 adapter_config = json.load(f)
749 model = adapter_config["base_model_name_or_path"]
--> 751 config = AutoConfig.from_pretrained(model, _from_pipeline=task, **hub_kwargs, **model_kwargs)
752 hub_kwargs["_commit_hash"] = config._commit_hash
754 custom_tasks = {}
File ~/.local/lib/python3.10/site-packages/transformers/models/auto/configuration_auto.py:1050, in AutoConfig.from_pretrained(cls, pretrained_model_name_or_path, **kwargs)
1048 return config_class.from_pretrained(pretrained_model_name_or_path, **kwargs)
1049 elif "model_type" in config_dict:
-> 1050 config_class = CONFIG_MAPPING[config_dict["model_type"]]
1051 return config_class.from_dict(config_dict, **unused_kwargs)
1052 else:
1053 # Fallback: use pattern matching on the string.
1054 # We go from longer names to shorter names to catch roberta before bert (for instance)
...
--> 748 raise KeyError(key)
749 value = self._mapping[key]
750 module_name = model_type_to_module_name(key)
KeyError: 'qwen2'```
Check your version of transformers. After 4.37, Qwen2 is merged. For pipeline, I checked transformers==4.41.2 and it works
zhiminy changed discussion status to closed