Instructions to use LoneStriker/Quyen-Pro-Max-v0.1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LoneStriker/Quyen-Pro-Max-v0.1-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LoneStriker/Quyen-Pro-Max-v0.1-GGUF") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("LoneStriker/Quyen-Pro-Max-v0.1-GGUF", dtype="auto") - llama-cpp-python
How to use LoneStriker/Quyen-Pro-Max-v0.1-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="LoneStriker/Quyen-Pro-Max-v0.1-GGUF", filename="Quyen-Pro-Max-v0.1-Q2_K.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use LoneStriker/Quyen-Pro-Max-v0.1-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf LoneStriker/Quyen-Pro-Max-v0.1-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf LoneStriker/Quyen-Pro-Max-v0.1-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf LoneStriker/Quyen-Pro-Max-v0.1-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf LoneStriker/Quyen-Pro-Max-v0.1-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf LoneStriker/Quyen-Pro-Max-v0.1-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf LoneStriker/Quyen-Pro-Max-v0.1-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf LoneStriker/Quyen-Pro-Max-v0.1-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf LoneStriker/Quyen-Pro-Max-v0.1-GGUF:Q4_K_M
Use Docker
docker model run hf.co/LoneStriker/Quyen-Pro-Max-v0.1-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use LoneStriker/Quyen-Pro-Max-v0.1-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LoneStriker/Quyen-Pro-Max-v0.1-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LoneStriker/Quyen-Pro-Max-v0.1-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/LoneStriker/Quyen-Pro-Max-v0.1-GGUF:Q4_K_M
- SGLang
How to use LoneStriker/Quyen-Pro-Max-v0.1-GGUF 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 "LoneStriker/Quyen-Pro-Max-v0.1-GGUF" \ --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": "LoneStriker/Quyen-Pro-Max-v0.1-GGUF", "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 "LoneStriker/Quyen-Pro-Max-v0.1-GGUF" \ --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": "LoneStriker/Quyen-Pro-Max-v0.1-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use LoneStriker/Quyen-Pro-Max-v0.1-GGUF with Ollama:
ollama run hf.co/LoneStriker/Quyen-Pro-Max-v0.1-GGUF:Q4_K_M
- Unsloth Studio
How to use LoneStriker/Quyen-Pro-Max-v0.1-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for LoneStriker/Quyen-Pro-Max-v0.1-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for LoneStriker/Quyen-Pro-Max-v0.1-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for LoneStriker/Quyen-Pro-Max-v0.1-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use LoneStriker/Quyen-Pro-Max-v0.1-GGUF with Docker Model Runner:
docker model run hf.co/LoneStriker/Quyen-Pro-Max-v0.1-GGUF:Q4_K_M
- Lemonade
How to use LoneStriker/Quyen-Pro-Max-v0.1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull LoneStriker/Quyen-Pro-Max-v0.1-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Quyen-Pro-Max-v0.1-GGUF-Q4_K_M
List all available models
lemonade list
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.
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