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

Libraries

How to use hiyouga/Qwen-14B-Chat-LLaMAfied with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hiyouga/Qwen-14B-Chat-LLaMAfied")
model = AutoModelForCausalLM.from_pretrained("hiyouga/Qwen-14B-Chat-LLaMAfied", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hiyouga/Qwen-14B-Chat-LLaMAfied with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hiyouga/Qwen-14B-Chat-LLaMAfied"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hiyouga/Qwen-14B-Chat-LLaMAfied",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hiyouga/Qwen-14B-Chat-LLaMAfied

SGLang

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

Docker Model Runner
How to use hiyouga/Qwen-14B-Chat-LLaMAfied with Docker Model Runner:
```
docker model run hf.co/hiyouga/Qwen-14B-Chat-LLaMAfied
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This is the LLaMAfied version of Qwen-14B-Chat model by Alibaba Cloud.

This model is converted with https://github.com/hiyouga/LLaMA-Factory/blob/main/tests/llamafy_qwen.py

The tokenizer is borrowed from https://huggingface.co/CausalLM/72B-preview-llamafied-qwen-llamafy

You may use this model for fine-tuning in downstream tasks, we recommend using our efficient fine-tuning toolkit. https://github.com/hiyouga/LLaMA-Factory

Developed by: Alibaba Cloud.
Language(s) (NLP): Chinese/English
License: Tongyi Qianwen License

Usage:

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

tokenizer = AutoTokenizer.from_pretrained("hiyouga/Qwen-14B-Chat-LLaMAfied")
model = AutoModelForCausalLM.from_pretrained("hiyouga/Qwen-14B-Chat-LLaMAfied", torch_dtype="auto", device_map="auto")
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

messages = [
    {"role": "user", "content": "Who are you?"}
]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
inputs = inputs.to("cuda")
generate_ids = model.generate(inputs, streamer=streamer)

You could also alternatively launch a CLI demo by using the script in LLaMA-Factory

python src/cli_demo.py --template qwen --model_name_or_path hiyouga/Qwen-14B-Chat-LLaMAfied

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	61.60
AI2 Reasoning Challenge (25-Shot)	57.51
HellaSwag (10-Shot)	82.11
MMLU (5-Shot)	65.57
TruthfulQA (0-shot)	51.99
Winogrande (5-shot)	72.93
GSM8k (5-shot)	39.50

Downloads last month: 93

Safetensors

Model size

14B params

Tensor type

BF16

Model tree for hiyouga/Qwen-14B-Chat-LLaMAfied

Quantizations

1 model

Spaces using hiyouga/Qwen-14B-Chat-LLaMAfied 3

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

57.510
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

82.110
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

65.570
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

51.990
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

72.930
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

39.500