Instructions to use Zoyd/01-ai_Yi-1.5-34B-Chat-16K-2_5bpw_exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Zoyd/01-ai_Yi-1.5-34B-Chat-16K-2_5bpw_exl2 with Transformers:

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

pipe = pipeline("text-generation", model="Zoyd/01-ai_Yi-1.5-34B-Chat-16K-2_5bpw_exl2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Zoyd/01-ai_Yi-1.5-34B-Chat-16K-2_5bpw_exl2")
model = AutoModelForCausalLM.from_pretrained("Zoyd/01-ai_Yi-1.5-34B-Chat-16K-2_5bpw_exl2", 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 Zoyd/01-ai_Yi-1.5-34B-Chat-16K-2_5bpw_exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Zoyd/01-ai_Yi-1.5-34B-Chat-16K-2_5bpw_exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Zoyd/01-ai_Yi-1.5-34B-Chat-16K-2_5bpw_exl2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-2_5bpw_exl2

SGLang

How to use Zoyd/01-ai_Yi-1.5-34B-Chat-16K-2_5bpw_exl2 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 "Zoyd/01-ai_Yi-1.5-34B-Chat-16K-2_5bpw_exl2" \
    --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": "Zoyd/01-ai_Yi-1.5-34B-Chat-16K-2_5bpw_exl2",
		"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 "Zoyd/01-ai_Yi-1.5-34B-Chat-16K-2_5bpw_exl2" \
        --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": "Zoyd/01-ai_Yi-1.5-34B-Chat-16K-2_5bpw_exl2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Zoyd/01-ai_Yi-1.5-34B-Chat-16K-2_5bpw_exl2 with Docker Model Runner:
```
docker model run hf.co/Zoyd/01-ai_Yi-1.5-34B-Chat-16K-2_5bpw_exl2
```

Configuration Parsing Warning:In config.json: "quantization_config.bits" must be an integer

Exllamav2 quant (exl2 / 2.5 bpw) made with ExLlamaV2 v0.0.21

Other EXL2 quants:

Quant	Model Size	lm_head
2.2	10049 MB	6
2.5	11195 MB	6
3.0	13193 MB	6
3.5	15187 MB	6
3.75	16186 MB	6
4.0	17183 MB	6
4.25	18179 MB	6
5.0	21171 MB	6
6.0	25231 MB	8
6.5	27111 MB	8
8.0	29540 MB	8

🐙 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, 16K, 32K	3.6T

Models

Chat models

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

Base models

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

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.

Quick Start

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

Downloads last month: 14

Paper for Zoyd/01-ai_Yi-1.5-34B-Chat-16K-2_5bpw_exl2

Yi: Open Foundation Models by 01.AI

Paper • 2403.04652 • Published Mar 7, 2024 • 65