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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("OrionStarAI/OrionStar-Yi-34B-Chat-Llama")
model = AutoModelForMultimodalLM.from_pretrained("OrionStarAI/OrionStar-Yi-34B-Chat-Llama")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

Docker Model Runner
How to use OrionStarAI/OrionStar-Yi-34B-Chat-Llama with Docker Model Runner:
```
docker model run hf.co/OrionStarAI/OrionStar-Yi-34B-Chat-Llama
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

OrionStarAI/OrionStar-Yi-34B-Chat-Llama

This model is identical to OrionStarAI/OrionStar-Yi-34B with the only difference being that the tensors have been renamed to follow the LLaMA format for automatic evaluation on the HF leaderboard.

Model Introduction

OrionStar-Yi-34B-Chat from OrionStarAI is based on the open-source Yi-34B model, fine-tuned on a high-quality corpus of over 15 million sentences. OrionStar-Yi-34B-Chat aims to provide an excellent interactive experience for users in the large model community.
The Yi series models, open-sourced by the 01-ai team, have shown impressive performance on various benchmarks in Chinese, English, and general domains. OrionStar-Yi-34B-Chat further explores the potential of Yi-34B. Through extensive fine-tuning on a large and high-quality corpus, OrionStar-Yi-34B-Chat performs exceptionally well on evaluation data. We strive to make it an outstanding open-source alternative in the ChatGPT domain!
Our fine-tuned model is completely open for academic research, but please adhere to the agreement and the Yi License.
Model Evaluation Results

We use opencompass to perform 5-shot on the following general domain datasets Testing. The evaluation results of other models are taken from opencompass leaderboard.

	C-Eval	MMLU	CMMLU
GPT-4	69.9	83	71
ChatGPT	52.5	69.1	53.9
Claude-1	52	65.7	-
TigerBot-70B-Chat-V2	57.7	65.9	59.9
WeMix-LLaMA2-70B	55.2	71.3	56
LLaMA-2-70B-Chat	44.3	63.8	43.3
Qwen-14B-Chat	71.7	66.4	70
Baichuan2-13B-Chat	56.7	57	58.4
OrionStar-Yi-34B-Chat	77.71	78.32	73.52

Discord Link: https://discord.gg/zumjDWgdAs

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Safetensors

Model size

34B params

Tensor type

F16

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Quantizations

6 models

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OrionStar-Yi-34B-Chat-Llama

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