Instructions to use OrionStarAI/Orion-14B-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OrionStarAI/Orion-14B-Chat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="OrionStarAI/Orion-14B-Chat", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("OrionStarAI/Orion-14B-Chat", trust_remote_code=True, dtype="auto") - llama-cpp-python
How to use OrionStarAI/Orion-14B-Chat with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="OrionStarAI/Orion-14B-Chat", filename="Orion-14B-Chat.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use OrionStarAI/Orion-14B-Chat with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf OrionStarAI/Orion-14B-Chat # Run inference directly in the terminal: llama-cli -hf OrionStarAI/Orion-14B-Chat
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf OrionStarAI/Orion-14B-Chat # Run inference directly in the terminal: llama-cli -hf OrionStarAI/Orion-14B-Chat
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 OrionStarAI/Orion-14B-Chat # Run inference directly in the terminal: ./llama-cli -hf OrionStarAI/Orion-14B-Chat
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 OrionStarAI/Orion-14B-Chat # Run inference directly in the terminal: ./build/bin/llama-cli -hf OrionStarAI/Orion-14B-Chat
Use Docker
docker model run hf.co/OrionStarAI/Orion-14B-Chat
- LM Studio
- Jan
- vLLM
How to use OrionStarAI/Orion-14B-Chat with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OrionStarAI/Orion-14B-Chat" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OrionStarAI/Orion-14B-Chat", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/OrionStarAI/Orion-14B-Chat
- SGLang
How to use OrionStarAI/Orion-14B-Chat 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 "OrionStarAI/Orion-14B-Chat" \ --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": "OrionStarAI/Orion-14B-Chat", "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 "OrionStarAI/Orion-14B-Chat" \ --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": "OrionStarAI/Orion-14B-Chat", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use OrionStarAI/Orion-14B-Chat with Ollama:
ollama run hf.co/OrionStarAI/Orion-14B-Chat
- Unsloth Studio new
How to use OrionStarAI/Orion-14B-Chat 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 OrionStarAI/Orion-14B-Chat 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 OrionStarAI/Orion-14B-Chat to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for OrionStarAI/Orion-14B-Chat to start chatting
- Docker Model Runner
How to use OrionStarAI/Orion-14B-Chat with Docker Model Runner:
docker model run hf.co/OrionStarAI/Orion-14B-Chat
- Lemonade
How to use OrionStarAI/Orion-14B-Chat with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull OrionStarAI/Orion-14B-Chat
Run and chat with the model
lemonade run user.Orion-14B-Chat-{{QUANT_TAG}}List all available models
lemonade list
Add chat_template to tokenizer_config.json?
I was trying to test out the model (to run JA MT-Bench) and was not getting very good results. One issue may be that the chat formatting is wrong - it appears to be Vicuna like, but the model card does not specify what system prompts should look like. One thing that might help with formatting would be to add a chat_template to the tokenizer_config.json?
Barring that, any parameters that you recommend (if there's a recommended system prompt, repetition penalty or other sampling parameters, etc) would be useful.
Thanks! I'll add it today!
done
@DachengZhang - is there support for a system prompt? Maybe it'd look something like:
"chat_template": "{% for message in messages %}{% if loop.first %}{{ bos_token }}{% endif %}
{% if message['role'] == 'system' %}{{ message['content'] + '\n\n ' }}{% elif message['role'] == 'user' %}{{ 'Human: ' + message['content'] + '\n\nAssistant: ' + eos_token }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token }}{% endif %}{% endfor %}",
Or is it something it's completely untrained for? (If not that's fine, it might be better to train off the Base in any case)
Yes, this model is not trained for the system role. We will add the system role in the next version.