Instructions to use Qwen/Qwen1.5-14B-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Qwen/Qwen1.5-14B-Chat with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Qwen/Qwen1.5-14B-Chat")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-14B-Chat")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen1.5-14B-Chat", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Qwen/Qwen1.5-14B-Chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Qwen/Qwen1.5-14B-Chat"
# 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/Qwen1.5-14B-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Qwen/Qwen1.5-14B-Chat

SGLang

How to use Qwen/Qwen1.5-14B-Chat 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/Qwen1.5-14B-Chat" \
    --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/Qwen1.5-14B-Chat",
		"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/Qwen1.5-14B-Chat" \
        --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/Qwen1.5-14B-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Qwen/Qwen1.5-14B-Chat with Docker Model Runner:
```
docker model run hf.co/Qwen/Qwen1.5-14B-Chat
```

Was unable to convert this in llama.cpp

by vbuhoijymzoi - opened Feb 6, 2024

Discussion

vbuhoijymzoi

Feb 6, 2024

•

edited Feb 6, 2024

Loading model file /content/models/Qwen1.5-14B-Chat/model-00001-of-00008.safetensors
Loading model file /content/models/Qwen1.5-14B-Chat/model-00001-of-00008.safetensors
Loading model file /content/models/Qwen1.5-14B-Chat/model-00002-of-00008.safetensors
Loading model file /content/models/Qwen1.5-14B-Chat/model-00003-of-00008.safetensors
Loading model file /content/models/Qwen1.5-14B-Chat/model-00004-of-00008.safetensors
Loading model file /content/models/Qwen1.5-14B-Chat/model-00005-of-00008.safetensors
Loading model file /content/models/Qwen1.5-14B-Chat/model-00006-of-00008.safetensors
Loading model file /content/models/Qwen1.5-14B-Chat/model-00007-of-00008.safetensors
Loading model file /content/models/Qwen1.5-14B-Chat/model-00008-of-00008.safetensors
params = Params(n_vocab=152064, n_embd=5120, n_layer=40, n_ctx=32768, n_ff=13696, n_head=40, n_head_kv=40, n_experts=None, n_experts_used=None, f_norm_eps=1e-06, rope_scaling_type=None, f_rope_freq_base=1000000.0, f_rope_scale=None, n_orig_ctx=None, rope_finetuned=None, ftype=None, path_model=PosixPath('/content/models/Qwen1.5-14B-Chat'))
Found vocab files: {'tokenizer.model': None, 'vocab.json': PosixPath('/content/models/Qwen1.5-14B-Chat/vocab.json'), 'tokenizer.json': PosixPath('/content/models/Qwen1.5-14B-Chat/tokenizer.json')}
Loading vocab file '/content/models/Qwen1.5-14B-Chat/vocab.json', type 'spm'
Traceback (most recent call last):
  File "/content/llama.cpp/convert.py", line 1478, in <module>
    main()
  File "/content/llama.cpp/convert.py", line 1446, in main
    vocab, special_vocab = vocab_factory.load_vocab(args.vocab_type, model_parent_path)
  File "/content/llama.cpp/convert.py", line 1332, in load_vocab
    vocab = SentencePieceVocab(
  File "/content/llama.cpp/convert.py", line 394, in __init__
    self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer))
  File "/usr/local/lib/python3.10/dist-packages/sentencepiece/__init__.py", line 447, in Init
    self.Load(model_file=model_file, model_proto=model_proto)
  File "/usr/local/lib/python3.10/dist-packages/sentencepiece/__init__.py", line 905, in Load
    return self.LoadFromFile(model_file)
  File "/usr/local/lib/python3.10/dist-packages/sentencepiece/__init__.py", line 310, in LoadFromFile
    return _sentencepiece.SentencePieceProcessor_LoadFromFile(self, arg)
RuntimeError: Internal: src/sentencepiece_processor.cc(1101) [model_proto->ParseFromArray(serialized.data(), serialized.size())]

sentencepiece version: 0.1.99

vbuhoijymzoi changed discussion title from Was unable to convert this in llama.cpp to <delete> Feb 6, 2024

vbuhoijymzoi changed discussion title from <delete> to Was unable to convert this in llama.cpp Feb 6, 2024

kagevazquez

Feb 21, 2024

•

edited Feb 21, 2024

python3 convert-hf-to-gguf.py models/qwen-xyz --outfile models/qwen-xyz/ggml-model-f16.gguf --outtype f16

vbuhoijymzoi

Feb 21, 2024

python3 convert-hf-to-gguf.py models/qwen-xyz --outfile models/qwen-xyz/ggml-model-f16.gguf --outtype f16

This didn't work when I tried at the time.

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