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, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use OrionStarAI/Orion-14B-Chat with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -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 serve -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 Desktop
- 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
- Atomic Chat
File size: 1,702 Bytes
f46de21 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | from typing import List
from queue import Queue
# build chat input prompt
def build_chat_input(tokenizer, messages: List[dict]):
# chat format:
# single-turn: <s>Human: Hello!\n\nAssistant: </s>
# multi-turn: <s>Human: Hello!\n\nAssistant: </s>Hi!</s>Human: How are you?\n\nAssistant: </s>I'm fine</s>
prompt = "<s>"
for msg in messages:
role = msg["role"]
message = msg["content"]
if message is None :
continue
if role == "user":
prompt += "Human: " + message + "\n\nAssistant: </s>"
if role == "assistant":
prompt += message + "</s>"
input_tokens = tokenizer.encode(prompt)
return input_tokens
class TextIterStreamer:
def __init__(self, tokenizer, skip_prompt=False, skip_special_tokens=False):
self.tokenizer = tokenizer
self.skip_prompt = skip_prompt
self.skip_special_tokens = skip_special_tokens
self.tokens = []
self.text_queue = Queue()
self.next_tokens_are_prompt = True
def put(self, value):
if self.skip_prompt and self.next_tokens_are_prompt:
self.next_tokens_are_prompt = False
else:
if len(value.shape) > 1:
value = value[0]
self.tokens.extend(value.tolist())
self.text_queue.put(
self.tokenizer.decode(self.tokens, skip_special_tokens=self.skip_special_tokens))
def end(self):
self.text_queue.put(None)
def __iter__(self):
return self
def __next__(self):
value = self.text_queue.get()
if value is None:
raise StopIteration()
else:
return value
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