argilla/distilabel-intel-orca-dpo-pairs
Viewer • Updated • 12.9k • 23.1k • 183
How to use vilm/Quyen-Mini-v0.1-GGUF with Transformers:
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("vilm/Quyen-Mini-v0.1-GGUF", dtype="auto")How to use vilm/Quyen-Mini-v0.1-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="vilm/Quyen-Mini-v0.1-GGUF", filename="Quyen-Mini-v0.1.Q2_K.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
How to use vilm/Quyen-Mini-v0.1-GGUF with llama.cpp:
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf vilm/Quyen-Mini-v0.1-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf vilm/Quyen-Mini-v0.1-GGUF:Q4_K_M
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf vilm/Quyen-Mini-v0.1-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf vilm/Quyen-Mini-v0.1-GGUF:Q4_K_M
# 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 vilm/Quyen-Mini-v0.1-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf vilm/Quyen-Mini-v0.1-GGUF:Q4_K_M
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 vilm/Quyen-Mini-v0.1-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf vilm/Quyen-Mini-v0.1-GGUF:Q4_K_M
docker model run hf.co/vilm/Quyen-Mini-v0.1-GGUF:Q4_K_M
How to use vilm/Quyen-Mini-v0.1-GGUF with Ollama:
ollama run hf.co/vilm/Quyen-Mini-v0.1-GGUF:Q4_K_M
How to use vilm/Quyen-Mini-v0.1-GGUF with Unsloth Studio:
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 vilm/Quyen-Mini-v0.1-GGUF to start chatting
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 vilm/Quyen-Mini-v0.1-GGUF to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for vilm/Quyen-Mini-v0.1-GGUF to start chatting
How to use vilm/Quyen-Mini-v0.1-GGUF with Docker Model Runner:
docker model run hf.co/vilm/Quyen-Mini-v0.1-GGUF:Q4_K_M
How to use vilm/Quyen-Mini-v0.1-GGUF with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull vilm/Quyen-Mini-v0.1-GGUF:Q4_K_M
lemonade run user.Quyen-Mini-v0.1-GGUF-Q4_K_M
lemonade list
Quyen is our first flagship LLM series based on the Qwen1.5 family. We introduced 6 different versions:
All models were trained with SFT and DPO using the following dataset:
<|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
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)