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

Commit

428a055

verified ·

1 Parent(s): 7116613

README: drop the 3-tool table and the Out-of-scope bullet

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README.md +0 -7

README.md CHANGED Viewed

@@ -95,12 +95,6 @@ This repository hosts the **GenEvolve agent policy** — a Qwen3-VL-8B-Instruct
 For a user request, the agent samples a multi-turn trajectory of tool calls before emitting the final prompt-reference program. The downstream generator then renders the image.
-| Tool | Role | Output |
-|---|---|---|
-| `search(queries)` | External textual evidence — entities, dates, facts | Markdown digest |
-| `image_search(query)` | Visual references; each result is given a unique `IMG_###` id | Image list with local paths |
-| `query_knowledge(skill_name)` | Internal generation knowledge — `spatial_layout`, `text_rendering`, `quantity_counting`, `attribute_binding`, `anatomy_body_coherence`, `physical_material_consistency`, `creative_drawing`, `aesthetic_drawing` | Skill markdown |
 ---
 ## 🖼️ Visual Demos
@@ -178,7 +172,6 @@ The agent's final `<answer>` is a JSON object:
 ## ⚖️ Intended Use, Limits, Bias
 - **Intended use.** Research on tool-using image-generation agents, agentic prompt-program synthesis, and self-distillation from generated outcomes.
-- **Out of scope.** The model produces a *prompt + reference list*, not pixels. Final image quality and safety are inherited from the downstream generator you pair it with. Do not use the agent to fabricate likenesses, infringing logos, or misleading factual imagery — apply your own content-safety filter on the generator side.
 - **Search dependency.** The agent issues live web/image queries through user-provided tool wrappers. Quality of grounded facts depends on the search backend you plug in.
 - **Bias.** Tool outputs and reference images come from public web search, which carries demographic, cultural, and geographic biases that may be reflected in agent outputs.

 For a user request, the agent samples a multi-turn trajectory of tool calls before emitting the final prompt-reference program. The downstream generator then renders the image.
 ---
 ## 🖼️ Visual Demos
 ## ⚖️ Intended Use, Limits, Bias
 - **Intended use.** Research on tool-using image-generation agents, agentic prompt-program synthesis, and self-distillation from generated outcomes.
 - **Search dependency.** The agent issues live web/image queries through user-provided tool wrappers. Quality of grounded facts depends on the search backend you plug in.
 - **Bias.** Tool outputs and reference images come from public web search, which carries demographic, cultural, and geographic biases that may be reflected in agent outputs.