Instructions to use OrionStarAI/OrionStar-Yi-34B-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OrionStarAI/OrionStar-Yi-34B-Chat with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="OrionStarAI/OrionStar-Yi-34B-Chat", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("OrionStarAI/OrionStar-Yi-34B-Chat", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OrionStarAI/OrionStar-Yi-34B-Chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OrionStarAI/OrionStar-Yi-34B-Chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OrionStarAI/OrionStar-Yi-34B-Chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/OrionStarAI/OrionStar-Yi-34B-Chat

SGLang

How to use OrionStarAI/OrionStar-Yi-34B-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/OrionStar-Yi-34B-Chat" \
    --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": "OrionStarAI/OrionStar-Yi-34B-Chat",
		"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 "OrionStarAI/OrionStar-Yi-34B-Chat" \
        --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": "OrionStarAI/OrionStar-Yi-34B-Chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use OrionStarAI/OrionStar-Yi-34B-Chat with Docker Model Runner:
```
docker model run hf.co/OrionStarAI/OrionStar-Yi-34B-Chat
```

OrionStar-Yi-34B-Chat / model-00005-of-00007.safetensors

DachengZhang

upload safetensors

6f60ce3 over 2 years ago

Download

History Blame

9.75 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: aa308b3bdb660f7d2fdcc8e342d9a90c7fe7e413ebacf7fb3bf371bc5db9d640
Size of remote file:: 9.75 GB
SHA256:: a3bc49898e6266b3d75760e6cb298377ff7bdbad25e3f66535c26bb44a5db24a

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.