Instructions to use vilm/Quyen-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vilm/Quyen-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="vilm/Quyen-v0.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vilm/Quyen-v0.1")
model = AutoModelForCausalLM.from_pretrained("vilm/Quyen-v0.1")
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 vilm/Quyen-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vilm/Quyen-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vilm/Quyen-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vilm/Quyen-v0.1

SGLang

How to use vilm/Quyen-v0.1 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 "vilm/Quyen-v0.1" \
    --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": "vilm/Quyen-v0.1",
		"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 "vilm/Quyen-v0.1" \
        --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": "vilm/Quyen-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use vilm/Quyen-v0.1 with Docker Model Runner:
```
docker model run hf.co/vilm/Quyen-v0.1
```

Quyen

Model Description

Quyen is our first flagship LLM series based on the Qwen1.5 family. We introduced 6 different versions:

Quyen-SE (0.5B)
Quyen-Mini (1.8B)
Quyen (4B)
Quyen-Plus (7B)
Quyen-Pro (14B)
Quyen-Pro-Max (72B)

All models were trained with SFT and DPO using the following dataset:

OpenHermes-2.5 by Teknium
Capyabara by LDJ
argilla/distilabel-capybara-dpo-7k-binarized by argilla
orca_dpo_pairs by Intel
and Private Data by Ontocord & BEE-spoke-data

Prompt Template

All Quyen models use ChatML as the default template:

<|im_start|>system
You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<|im_end|>
<|im_start|>user
Hello world.<|im_end|>
<|im_start|>assistant

You can also use apply_chat_template:

messages = [
    {"role": "system", "content": "You are a sentient, superintelligent artificial general intelligence, here to teach and assist me."},
    {"role": "user", "content": "Hello world."}
]
gen_input = tokenizer.apply_chat_template(message, return_tensors="pt")
model.generate(**gen_input)

Benchmarks:

Coming Soon! We will update the benchmarks later

Acknowledgement

We're incredibly grateful to Tensoic and Ontocord for their generous support with compute and data preparation.
Special thanks to the Qwen team for letting us access the models early for these amazing finetunes.

Downloads last month: 877

Safetensors

Model size

4B params

Tensor type

BF16

Model tree for vilm/Quyen-v0.1

Finetunes

1 model

Datasets used to train vilm/Quyen-v0.1

Collection including vilm/Quyen-v0.1

Quyen

Collection

State-of-the-arts General LLMs - based on Qwen1.5 • 20 items • Updated Mar 2 • 12