Recon2Reason Reasoning 4B
Recon2Reason Reasoning 4B is a 4B-scale vision-language model specialized for spatial reasoning in indoor and embodied scenes. It is fine-tuned from Qwen3-VL-4B and improves reasoning about metric distance, relative position, object configuration, and spatial relations from visual inputs.
The checkpoint retains the standard Qwen3VLForConditionalGeneration architecture. No custom model code or trust_remote_code=True is required.
Highlights
- Standard Qwen3-VL Transformers interface
- 4,437,815,808 parameters
- BF16 weights in sharded Safetensors format
- Single-image and multi-image visual inputs
- Strong results on metric and qualitative spatial reasoning benchmarks
This repository contains the reasoning model only. The retrieval-augmented scene-reconstruction extension is released separately.
Model details
| Property | Value |
|---|---|
| Model name | Recon2Reason Reasoning 4B |
| Model type | Vision-language conditional generation model |
| Architecture | Qwen3VLForConditionalGeneration |
| Parameters | 4,437,815,808 |
| Weight dtype | BF16 |
| Weight format | Safetensors, 2 shards |
| Primary domain | Indoor spatial reasoning |
| Base model | Qwen3-VL-4B-Instruct |
| Tested Transformers version | 4.57.1 |
| License | Apache-2.0 |
Quick start
pip install "transformers==4.57.1" "torch>=2.6" accelerate safetensors pillow
import torch
from transformers import AutoProcessor, Qwen3VLForConditionalGeneration
model_id = "BAAI/Recon2Reason-Reasoning-4B"
processor = AutoProcessor.from_pretrained(model_id)
model = Qwen3VLForConditionalGeneration.from_pretrained(
model_id,
dtype="auto",
device_map="auto",
).eval()
messages = [
{
"role": "user",
"content": [
{"type": "image", "image": "path/to/scene.jpg"},
{
"type": "text",
"text": "Which object is closest to the chair? ",
},
],
}
]
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_ids = model.generate(
**inputs,
max_new_tokens=512,
do_sample=False,
)
generated_ids = output_ids[:, inputs["input_ids"].shape[1]:]
answer = processor.batch_decode(
generated_ids,
skip_special_tokens=True,
clean_up_tokenization_spaces=False,
)[0]
print(answer)
The model uses the standard Qwen3-VL chat format. For benchmark reproduction, use greedy decoding (do_sample=False) unless a benchmark specifies otherwise.
Evaluation
The checkpoint was evaluated in BF16 with PyTorch 2.8.0, Transformers 4.57.1, SDPA attention, and greedy decoding on NVIDIA RTX PRO 6000 Blackwell GPUs.
License
The model is released under the Apache License 2.0, subject to final confirmation that all training data and upstream artifacts permit this distribution. See LICENSE.
Acknowledgements
This work builds on Qwen3-VL. We thank the creators of the evaluation datasets and the open-source Transformers ecosystem.