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

Libraries

How to use BabyChou/Yi-VL-34B with Transformers:

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

pipe = pipeline("text-generation", model="BabyChou/Yi-VL-34B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("BabyChou/Yi-VL-34B")
model = AutoModelForCausalLM.from_pretrained("BabyChou/Yi-VL-34B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BabyChou/Yi-VL-34B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/BabyChou/Yi-VL-34B

SGLang

How to use BabyChou/Yi-VL-34B 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 "BabyChou/Yi-VL-34B" \
    --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": "BabyChou/Yi-VL-34B",
		"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 "BabyChou/Yi-VL-34B" \
        --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": "BabyChou/Yi-VL-34B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Yi-VL-34B / README.md

BabyChou

Update README.md with license information (#1)

1183ed7 verified about 2 years ago

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	---
	license: apache-2.0
	---


	# What is Yi-VL?

	## Architecture

	Yi-VL adopts the [LLaVA](https://github.com/haotian-liu/LLaVA) architecture, which is composed of three primary components:

	- Vision Transformer (ViT): it's initialized with [CLIP ViT-H/14 model](https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K) and used for image encoding.

	- Projection Module: it's designed to align image features with text feature space, consisting of a two-layer Multilayer Perceptron (MLP) with layer normalizations.

	- Large Language Model (LLM): it's initialized with [Yi-34B-Chat](https://huggingface.co/01-ai/Yi-34B-Chat) or [Yi-6B-Chat](https://huggingface.co/01-ai/Yi-6B-Chat), demonstrating exceptional proficiency in understanding and generating both English and Chinese.

	![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/EGVHSWG4kAcX01xDaoeXS.png)


	# How to use Yi-VL?

	## Quick start

	This has been implemented into the SGLang codebase, where you can simply call this model by creating a function like so:
	```
	import sglang as sgl

	@sgl.function
	def image_qa(s, image_path, question):
	s += sgl.user(sgl.image(image_path) + question)
	s += sgl.assistant(sgl.gen("answer"))


	runtime = sgl.Runtime(model_path="BabyChou/Yi-VL-34B",
	tokenizer_path="BabyChou/Yi-VL-34B")
	sgl.set_default_backend(runtime)


	# Single
	state = image_qa.run(
	image_path="images/cat.jpeg",
	question="What is this?",
	max_new_tokens=64)
	print(state["answer"], "\n")
	```

	## License

	Please refer to the [acknowledgments and attributions](#acknowledgments_and_attributions) as well as individual components, for the license of source code.

	The Yi series models are fully open for academic research and free for commercial use, permissions of which are automatically granted upon application.

	All usage must adhere to the [Yi Series Models Community License Agreement 2.1](https://huggingface.co/01-ai/Yi-VL-34B/blob/main/LICENSE).

	For free commercial use, you only need to send an email to get official commercial permission.