Instructions to use 01-ai/Yi-1.5-9B-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 01-ai/Yi-1.5-9B-Chat with Transformers:

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

pipe = pipeline("text-generation", model="01-ai/Yi-1.5-9B-Chat")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("01-ai/Yi-1.5-9B-Chat")
model = AutoModelForCausalLM.from_pretrained("01-ai/Yi-1.5-9B-Chat", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use 01-ai/Yi-1.5-9B-Chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "01-ai/Yi-1.5-9B-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": "01-ai/Yi-1.5-9B-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/01-ai/Yi-1.5-9B-Chat

SGLang

How to use 01-ai/Yi-1.5-9B-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 "01-ai/Yi-1.5-9B-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": "01-ai/Yi-1.5-9B-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 "01-ai/Yi-1.5-9B-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": "01-ai/Yi-1.5-9B-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use 01-ai/Yi-1.5-9B-Chat with Docker Model Runner:
```
docker model run hf.co/01-ai/Yi-1.5-9B-Chat
```

Yi-1.5-9B-Chat / README.md

anonymitaet

Update README.md

22b0ac8 verified over 2 years ago

preview code

Raw

History Blame

4.06 kB

🤗 HuggingFace • 🤖 ModelScope • ✡️ WiseModel
🐙 GitHub • 👾 Discord • 🐤 Twitter • 💬 WeChat
📝 Paper • 🙌 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	3.6T

Models

Chat models

Model	Download
Yi-1.5-34B-Chat	• 🤗 Hugging Face
Yi-1.5-9B-Chat	• 🤗 Hugging Face
Yi-1.5-6B-Chat	• 🤗 Hugging Face

Base models

Model	Download
Yi-1.5-34B	• 🤗 Hugging Face
Yi-1.5-9B	• 🤗 Hugging Face
Yi-1.5-6B	• 🤗 Hugging Face

Benchmarks

Chat models

Waiting for benchmark results.

Base models

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

Model	MMLU	CMMLU	BBH	AGIEval	HumanEva(+)	MBPP(+)	GSM8k	MATH
Mistral 8*22B	77.8	60.4	61.9	58.6	45.1(34.1)	71.2(-)	81.7	41.8
DeepSeek-V2	78.5	84.0	78.9	-	48.8(-)	66.6(-)	79.2	43.6
Qwen1.5-32B	73.7	83	66.8	72.6	37.8(35.4)	49.4(44.4)	79.5	37.7
Qwen1.5-72B	77.5	84.2	65.5	71.7	41.5(39.0)	53.4(43.6)	81.4	41.8
Llama3_70B_base	78.7	68.9	65.0	52.8	38.4(34.8)	69.7(52.9)	82.4	42.4
Yi 1.5-34B	77.1	84.8	76.4	71.1	46.3(40.2)	65.5(55.4)	82.7	41.0

Yi-1.5-9B is a strong performer among similarly sized open-source models.

Model	MMLU	CMMLU	BBH	AGIEval	HumanEval(+)	MBPP(+)	GSM8k	Math
Gemma-7B	64.3	48.4	41.1	46.0	33.5(28.0)	45.8(32.8)	55.7	24.8
Qwen1.5-7B	61.0	73.4	33.4	61.6	36.0(31.1)	46.1(37.6)	70.1	20.3
Mistral-7B	62.5	44.6	45.0	42.4	29.3(22.6)	50.2(32.1)	47.5	15.5
Mistral 8*7B	70.6	53.0	52.4	49.5	40.2(31.1)	60.7(31.1)	65.7	28.4
Llama3-8B_Base	66.6	50.9	47.9	44.7	34.7(31.7)	48.0(44.9)	54.7	21.16
Yi 1.5-6B	63.5	70.8	45.7	56.0	36.5(28.7)	56.8(46.9)	62.2	28.42
Yi 1.5-9B	69.5	74.8	50.9	62.7	41.4(34.1)	61.1(53.6)	73.7	32.6

Quick Start

For getting up and running with Yi-1.5 models quickly, see README.