Instructions to use MeiGen-AI/GenEvolve with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MeiGen-AI/GenEvolve with Transformers:

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

pipe = pipeline("image-text-to-text", model="MeiGen-AI/GenEvolve")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("MeiGen-AI/GenEvolve")
model = AutoModelForImageTextToText.from_pretrained("MeiGen-AI/GenEvolve")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
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

vLLM

How to use MeiGen-AI/GenEvolve with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MeiGen-AI/GenEvolve"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MeiGen-AI/GenEvolve",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/MeiGen-AI/GenEvolve

SGLang

How to use MeiGen-AI/GenEvolve 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 "MeiGen-AI/GenEvolve" \
    --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": "MeiGen-AI/GenEvolve",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "MeiGen-AI/GenEvolve" \
        --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": "MeiGen-AI/GenEvolve",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use MeiGen-AI/GenEvolve with Docker Model Runner:
```
docker model run hf.co/MeiGen-AI/GenEvolve
```

Ephemeral182 commited on 3 days ago

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README: remove tech-stack badges (python/pytorch/vllm/cuda/license)

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@@ -38,14 +38,6 @@ datasets:
     <img alt="Dataset" src="https://img.shields.io/badge/🤗_Dataset-GenEvolve--Data-FFD21E"></a>
 </p>
-<p>
-  <img alt="python" src="https://img.shields.io/badge/python-3.11-3776AB?logo=python&logoColor=white">
-  <img alt="pytorch" src="https://img.shields.io/badge/pytorch-2.8-EE4C2C?logo=pytorch&logoColor=white">
-  <img alt="vllm" src="https://img.shields.io/badge/vLLM-0.11-30A14E">
-  <img alt="cuda" src="https://img.shields.io/badge/CUDA-12.x-76B900?logo=nvidia&logoColor=white">
-  <img alt="license" src="https://img.shields.io/badge/license-Apache%202.0-green">
-</p>
 </div>
 This repository hosts the **GenEvolve agent policy** — a Qwen3-VL-8B-Instruct backbone fine-tuned and self-evolved into a tool-orchestrated image-generation agent. Given a user request, the agent issues web/image searches, retrieves visual references, activates internal generation knowledge, and emits an executable **prompt-reference program** `z = (gen_prompt, reference_images)` that drives any reference-conditioned downstream generator (Qwen-Image-Edit, Nano Banana Pro, ...).

     <img alt="Dataset" src="https://img.shields.io/badge/🤗_Dataset-GenEvolve--Data-FFD21E"></a>
 </p>
 </div>
 This repository hosts the **GenEvolve agent policy** — a Qwen3-VL-8B-Instruct backbone fine-tuned and self-evolved into a tool-orchestrated image-generation agent. Given a user request, the agent issues web/image searches, retrieves visual references, activates internal generation knowledge, and emits an executable **prompt-reference program** `z = (gen_prompt, reference_images)` that drives any reference-conditioned downstream generator (Qwen-Image-Edit, Nano Banana Pro, ...).