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 2 days ago

Commit

c893ed7

verified ·

1 Parent(s): 562355d

Update public links in model card

Browse files

Files changed (1) hide show

README.md +9 -11

README.md CHANGED Viewed

@@ -16,7 +16,7 @@ tags:
 - multimodal
 - qwen3-vl
 datasets:
-- MeiGen-AI/GenEvolve-Data
 ---
 <div align="center">
@@ -30,11 +30,9 @@ datasets:
 <p>
   <a href="https://ephemeral182.github.io/GenEvolve/">
     <img alt="Project Page" src="https://img.shields.io/badge/🌐_Project-Page-1f6feb"></a>
-  <a href="https://arxiv.org/abs/XXXX.XXXXX">
-    <img alt="arXiv" src="https://img.shields.io/badge/📄_arXiv-XXXX.XXXXX-b31b1b"></a>
-  <a href="https://github.com/Ephemeral182/GenEvolve">
     <img alt="Code" src="https://img.shields.io/badge/💾_GitHub-Code-181717"></a>
-  <a href="https://huggingface.co/datasets/MeiGen-AI/GenEvolve-Data">
     <img alt="Dataset" src="https://img.shields.io/badge/🤗_Dataset-GenEvolve--Data-FFD21E"></a>
 </p>
@@ -111,12 +109,12 @@ For a user request, the agent samples a multi-turn trajectory of tool calls befo
 ## 🚀 Quick Start
-The deployed checkpoint is the **student policy** — it consumes a user prompt and returns a JSON `gen_prompt + reference_images` program through a `<think>/<tool_call>/<answer>` loop. The end-to-end runtime (vLLM serving + agent loop + tools + Qwen/Nano renderers) lives in the [GitHub repo](https://github.com/Ephemeral182/GenEvolve); the snippet below mirrors its installation and usage.
 ### 1. Install the main GenEvolve runtime
 ```bash
-git clone https://github.com/Ephemeral182/GenEvolve.git
 cd GenEvolve
 conda create -n genevolve python=3.11 -y && conda activate genevolve
@@ -144,7 +142,7 @@ MODEL_PATH=MeiGen-AI/GenEvolve PORT=8000 TP=1 DP=8 bash scripts/serve_vllm.sh
 ```bash
 export SERPER_API_KEY=<your_key>      # required for search / image_search
-export GOOGLE_API_KEY=<your_key>      # only for --backend nano-banana-pro
 # Nano Banana Pro renderer
 python examples/quickstart.py \
@@ -183,9 +181,9 @@ The agent's final `<answer>` is a JSON object:
 | Artifact | Link |
 |---|---|
 | Project page | https://ephemeral182.github.io/GenEvolve/ |
-| Paper (arXiv) | https://arxiv.org/abs/XXXX.XXXXX |
-| Code | https://github.com/Ephemeral182/GenEvolve |
-| Training data + benchmark | [MeiGen-AI/GenEvolve-Data](https://huggingface.co/datasets/MeiGen-AI/GenEvolve-Data) |
 | Base model | [Qwen/Qwen3-VL-8B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct) |
 ---

 - multimodal
 - qwen3-vl
 datasets:
+- MeiGen-AI/GenEvolve-Data-Bench
 ---
 <div align="center">
 <p>
   <a href="https://ephemeral182.github.io/GenEvolve/">
     <img alt="Project Page" src="https://img.shields.io/badge/🌐_Project-Page-1f6feb"></a>
+  <a href="https://github.com/MeiGen-AI/GenEvolve">
     <img alt="Code" src="https://img.shields.io/badge/💾_GitHub-Code-181717"></a>
+  <a href="https://huggingface.co/datasets/MeiGen-AI/GenEvolve-Data-Bench">
     <img alt="Dataset" src="https://img.shields.io/badge/🤗_Dataset-GenEvolve--Data-FFD21E"></a>
 </p>
 ## 🚀 Quick Start
+The deployed checkpoint is the **student policy** — it consumes a user prompt and returns a JSON `gen_prompt + reference_images` program through a `<think>/<tool_call>/<answer>` loop. The end-to-end runtime (vLLM serving + agent loop + tools + Qwen/Nano renderers) lives in the [GitHub repo](https://github.com/MeiGen-AI/GenEvolve); the snippet below mirrors its installation and usage.
 ### 1. Install the main GenEvolve runtime
 ```bash
+git clone https://github.com/MeiGen-AI/GenEvolve.git
 cd GenEvolve
 conda create -n genevolve python=3.11 -y && conda activate genevolve
 ```bash
 export SERPER_API_KEY=<your_key>      # required for search / image_search
+export GOOGLE_API_KEY=<your_key>      # or GEMINI_API_KEY; only for --backend nano-banana-pro
 # Nano Banana Pro renderer
 python examples/quickstart.py \
 | Artifact | Link |
 |---|---|
 | Project page | https://ephemeral182.github.io/GenEvolve/ |
+| Paper | Coming soon |
+| Code | https://github.com/MeiGen-AI/GenEvolve |
+| Training data + benchmark | [MeiGen-AI/GenEvolve-Data-Bench](https://huggingface.co/datasets/MeiGen-AI/GenEvolve-Data-Bench) |
 | Base model | [Qwen/Qwen3-VL-8B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct) |
 ---