Image-Text-to-Text
Transformers
Safetensors
English
qwen3_vl
qwen3-vl
geometry
multimodal
reinforcement-learning
grpo
conversational
Instructions to use pH202411/MetaReason-RL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pH202411/MetaReason-RL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="pH202411/MetaReason-RL") 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, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("pH202411/MetaReason-RL") model = AutoModelForMultimodalLM.from_pretrained("pH202411/MetaReason-RL", device_map="auto") 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 Settings
- vLLM
How to use pH202411/MetaReason-RL with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "pH202411/MetaReason-RL" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "pH202411/MetaReason-RL", "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/pH202411/MetaReason-RL
- SGLang
How to use pH202411/MetaReason-RL 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 "pH202411/MetaReason-RL" \ --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": "pH202411/MetaReason-RL", "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 "pH202411/MetaReason-RL" \ --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": "pH202411/MetaReason-RL", "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 pH202411/MetaReason-RL with Docker Model Runner:
docker model run hf.co/pH202411/MetaReason-RL
MetaReason-RL
MetaReason-RL is the geometry-reasoning checkpoint from MetaReason. It uses the Qwen3-VL architecture and contains four BF16 safetensors shards.
Load and run
pip install "transformers>=4.57.1" accelerate safetensors pillow
import torch
from transformers import AutoProcessor, Qwen3VLForConditionalGeneration
model_id = "pH202411/MetaReason-RL"
processor = AutoProcessor.from_pretrained(model_id)
model = Qwen3VLForConditionalGeneration.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
)
messages = [{
"role": "user",
"content": [
{"type": "image", "image": "geometry_problem.png"},
{"type": "text", "text": "Solve this geometry problem and give the final answer."},
],
}]
inputs = processor.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
with torch.inference_mode():
output = model.generate(**inputs, max_new_tokens=4096)
output = output[:, inputs.input_ids.shape[1]:]
print(processor.batch_decode(output, skip_special_tokens=True)[0])
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Model tree for pH202411/MetaReason-RL
Base model
Qwen/Qwen3-VL-8B-Instruct
docker model run hf.co/pH202411/MetaReason-RL