Instructions to use 01-ai/Yi-1.5-34B-32K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 01-ai/Yi-1.5-34B-32K with Transformers:

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

pipe = pipeline("text-generation", model="01-ai/Yi-1.5-34B-32K")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("01-ai/Yi-1.5-34B-32K")
model = AutoModelForCausalLM.from_pretrained("01-ai/Yi-1.5-34B-32K")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use 01-ai/Yi-1.5-34B-32K with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "01-ai/Yi-1.5-34B-32K"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "01-ai/Yi-1.5-34B-32K",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/01-ai/Yi-1.5-34B-32K

SGLang

How to use 01-ai/Yi-1.5-34B-32K 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 "01-ai/Yi-1.5-34B-32K" \
    --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": "01-ai/Yi-1.5-34B-32K",
		"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 "01-ai/Yi-1.5-34B-32K" \
        --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": "01-ai/Yi-1.5-34B-32K",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use 01-ai/Yi-1.5-34B-32K with Docker Model Runner:
```
docker model run hf.co/01-ai/Yi-1.5-34B-32K
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🐙 GitHub • 👾 Discord • 🐤 Twitter • 💬 WeChat
📝 Paper • 💪 Tech Blog • 🙌 FAQ • 📗 Learning Hub

Intro

Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples.

Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension.

Model	Context Length	Pre-trained Tokens
Yi-1.5	4K, 16K, 32K	3.6T

Models

Chat models

Name	Download
Yi-1.5-34B-Chat	• 🤗 Hugging Face • 🤖 ModelScope • 🟣 wisemodel
Yi-1.5-34B-Chat-16K	• 🤗 Hugging Face • 🤖 ModelScope • 🟣 wisemodel
Yi-1.5-9B-Chat	• 🤗 Hugging Face • 🤖 ModelScope • 🟣 wisemodel
Yi-1.5-9B-Chat-16K	• 🤗 Hugging Face • 🤖 ModelScope • 🟣 wisemodel
Yi-1.5-6B-Chat	• 🤗 Hugging Face • 🤖 ModelScope • 🟣 wisemodel

Base models

Name	Download
Yi-1.5-34B	• 🤗 Hugging Face • 🤖 ModelScope • 🟣 wisemodel
Yi-1.5-34B-32K	• 🤗 Hugging Face • 🤖 ModelScope • 🟣 wisemodel
Yi-1.5-9B	• 🤗 Hugging Face • 🤖 ModelScope • 🟣 wisemodel
Yi-1.5-9B-32K	• 🤗 Hugging Face • 🤖 ModelScope • 🟣 wisemodel
Yi-1.5-6B	• 🤗 Hugging Face • 🤖 ModelScope • 🟣 wisemodel

Benchmarks

Chat models

Yi-1.5-34B-Chat is on par with or excels beyond larger models in most benchmarks.

Yi-1.5-9B-Chat is the top performer among similarly sized open-source models.
Base models

Yi-1.5-34B is on par with or excels beyond larger models in some benchmarks.

Yi-1.5-9B is the top performer among similarly sized open-source models.