Instructions to use llmware/dragon-yi-6b-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use llmware/dragon-yi-6b-v0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="llmware/dragon-yi-6b-v0", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("llmware/dragon-yi-6b-v0", trust_remote_code=True, dtype="auto") - llama-cpp-python
How to use llmware/dragon-yi-6b-v0 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="llmware/dragon-yi-6b-v0", filename="dragon-yi-6b-q4_k_m.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use llmware/dragon-yi-6b-v0 with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf llmware/dragon-yi-6b-v0:Q4_K_M # Run inference directly in the terminal: llama-cli -hf llmware/dragon-yi-6b-v0:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf llmware/dragon-yi-6b-v0:Q4_K_M # Run inference directly in the terminal: llama-cli -hf llmware/dragon-yi-6b-v0: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 llmware/dragon-yi-6b-v0:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf llmware/dragon-yi-6b-v0: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 llmware/dragon-yi-6b-v0:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf llmware/dragon-yi-6b-v0:Q4_K_M
Use Docker
docker model run hf.co/llmware/dragon-yi-6b-v0:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use llmware/dragon-yi-6b-v0 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "llmware/dragon-yi-6b-v0" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "llmware/dragon-yi-6b-v0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/llmware/dragon-yi-6b-v0:Q4_K_M
- SGLang
How to use llmware/dragon-yi-6b-v0 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 "llmware/dragon-yi-6b-v0" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "llmware/dragon-yi-6b-v0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "llmware/dragon-yi-6b-v0" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "llmware/dragon-yi-6b-v0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use llmware/dragon-yi-6b-v0 with Ollama:
ollama run hf.co/llmware/dragon-yi-6b-v0:Q4_K_M
- Unsloth Studio new
How to use llmware/dragon-yi-6b-v0 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 llmware/dragon-yi-6b-v0 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 llmware/dragon-yi-6b-v0 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for llmware/dragon-yi-6b-v0 to start chatting
- Docker Model Runner
How to use llmware/dragon-yi-6b-v0 with Docker Model Runner:
docker model run hf.co/llmware/dragon-yi-6b-v0:Q4_K_M
- Lemonade
How to use llmware/dragon-yi-6b-v0 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull llmware/dragon-yi-6b-v0:Q4_K_M
Run and chat with the model
lemonade run user.dragon-yi-6b-v0-Q4_K_M
List all available models
lemonade list
Update README.md with license information
Hi, I'm Chen, a DevRel specialist from 01.AI.
Today I'm sending you the PR to help you update the model license, and give a recommendation according to apache-2.0.
License Update:
Since license of all Yi Series models has been updated from yi-license to apache-2.0, this PR is to help you update it.
License under apache-2.0 enables more free and flexible use and distribution, promoting open collaboration and innovation.
It can be a good choice to make your models widely available and provide access which is reliable and high-quality. (https://www.apache.org/licenses/LICENSE-2.0)
If it looks good to you, you can choose to update other yi derivatives (if you have) license to apache-2.0 on your own if I miss out.
Recommendation for Yi Derivatives:
All Yi Series models are now licensed under apache-2.0. It is recomended that Yi derivatives mention the specific Yi models they're based on in any place (e.g., in the Model Card) to align with the requirement of apache-2.0.
Thanks for your continued support and contributions to Yi models.
Hi, have you reviewed this PR? If it looks good to you, you can merge it! π
@Chen-01AI - thank you for sharing this update with us. We appreciate it - as well as the great technology from 01.AI. We will update the license terms accordingly. Apologies for the delay in seeing this message. All the best - Darren