Text Generation
Transformers
Safetensors
English
qwen2
code
codeqwen
chat
qwen
qwen-coder
conversational
text-generation-inference
Instructions to use Qwen/Qwen2.5-Coder-14B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Qwen/Qwen2.5-Coder-14B-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Qwen/Qwen2.5-Coder-14B-Instruct") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Coder-14B-Instruct") model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-14B-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
- vLLM
How to use Qwen/Qwen2.5-Coder-14B-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Qwen/Qwen2.5-Coder-14B-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/Qwen2.5-Coder-14B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Qwen/Qwen2.5-Coder-14B-Instruct
- SGLang
How to use Qwen/Qwen2.5-Coder-14B-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/Qwen2.5-Coder-14B-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/Qwen2.5-Coder-14B-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/Qwen2.5-Coder-14B-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/Qwen2.5-Coder-14B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Qwen/Qwen2.5-Coder-14B-Instruct with Docker Model Runner:
docker model run hf.co/Qwen/Qwen2.5-Coder-14B-Instruct
Commit ·
ec98bf0
1
Parent(s): 1a62978
update tokenizer_config.json,config.json,generation_config.json
Browse files- config.json +1 -1
- generation_config.json +2 -2
- tokenizer_config.json +1 -1
config.json
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"initializer_range": 0.02,
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"intermediate_size": 13824,
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"max_position_embeddings": 32768,
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"max_window_layers":
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"model_type": "qwen2",
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"num_attention_heads": 40,
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"num_hidden_layers": 48,
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"initializer_range": 0.02,
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"intermediate_size": 13824,
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"max_position_embeddings": 32768,
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"max_window_layers": 48,
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"model_type": "qwen2",
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"num_attention_heads": 40,
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"num_hidden_layers": 48,
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generation_config.json
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"temperature": 0.7,
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"top_p": 0.8,
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"top_k": 20,
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"transformers_version": "4.
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}
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"temperature": 0.7,
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"top_p": 0.8,
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"top_k": 20,
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"transformers_version": "4.43.1"
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}
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tokenizer_config.json
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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}
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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}
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