---
license: apache-2.0
language:
- en
base_model:
- internlm/Spatial-SSRL-Qwen3VL-4B
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- text-generation-inference
- fp8
- vllm
---

![1](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/aWSJpDL6keXNMCKxkaPBt.png)

# **Spatial-SSRL-Qwen3VL-4B-FP8**

> **Spatial-SSRL-Qwen3VL-4B-FP8** is an FP8-compressed variant built on top of **internlm/Spatial-SSRL-Qwen3VL-4B**. This edition applies **BF16 · FP8 (F8_E4M3)** precision formats to reduce memory footprint and increase inference throughput, while preserving the spatial reasoning and multimodal understanding strengths of the original 4B architecture.
>
> The base **Spatial-SSRL-Qwen3VL-4B** model is a spatially enhanced vision-language model built upon **Qwen3-VL-4B-Instruct**. It integrates **Spatial-SSRL**, a lightweight self-supervised reinforcement learning paradigm designed to scale RLVR efficiently. The model demonstrates strong spatial intelligence while maintaining the original general visual capabilities of its base architecture.

> [!important]
> FP8 (8-bit floating point) weight and activation quantization using hardware acceleration on GPUs – [FP8 W8A8](https://docs.vllm.ai/en/stable/features/quantization/fp8/).
> Quantization W8A8 FP8-dynamic recipe – [examples](https://github.com/vllm-project/llm-compressor/tree/main/examples/quantization_w8a8_fp8).

## Key Highlights

* **BF16 · FP8 (F8_E4M3) Compression**: Transformer Engine based FP8 quantization reduces VRAM usage while maintaining spatial reasoning fidelity.
* **Spatial-SSRL Optimization**: Lightweight self-supervised reinforcement learning enhances spatial grounding and reasoning.
* **4B Vision-Language Backbone**: Efficient multimodal performance with balanced compute and memory requirements.
* **Strong Spatial Intelligence**: Improved object localization, spatial relationships, depth reasoning, and geometric understanding.
* **Preserved General Vision Capabilities**: Retains captioning, VQA, and multimodal dialogue strengths of Qwen3-VL-4B-Instruct.
* **RLVR Compatible Framework**: Designed for scalable reinforcement learning with verifiable rewards.
* **Optimized Deployment**: FP8 compression enables smoother inference on Hopper and other compatible GPU architectures.

## About Spatial-SSRL

**Spatial-SSRL** is a lightweight, tool-free framework naturally compatible with the RLVR training paradigm and easy to extend to a wide range of pretext tasks.

The framework currently formulates **five spatially oriented tasks**, requiring only standard RGB or RGB-D images. It is modular and extensible, enabling the research community to introduce new spatial pretext objectives that further strengthen large vision-language models.

We welcome contributions to expand Spatial-SSRL with effective spatial reasoning tasks that enhance LVLM capabilities.

## Quick Start with Transformers

```python
from transformers import Qwen3VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch

# Load the Spatial SSRL Qwen3VL 4B FP8 model
model = Qwen3VLForConditionalGeneration.from_pretrained(
    "internlm/Spatial-SSRL-Qwen3VL-4B-FP8",
    torch_dtype="auto",
    device_map="auto"
)

processor = AutoProcessor.from_pretrained(
    "internlm/Spatial-SSRL-Qwen3VL-4B-FP8"
)

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "sample_image.png",
            },
            {
                "type": "text",
                "text": "Describe spatial relationships between objects and explain their relative positions."
            },
        ],
    }
]

text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)

image_inputs, video_inputs = process_vision_info(messages)

inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
).to("cuda")

generated_ids = model.generate(**inputs, max_new_tokens=1024)

generated_ids_trimmed = [
    out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]

output_text = processor.batch_decode(
    generated_ids_trimmed,
    skip_special_tokens=True,
    clean_up_tokenization_spaces=False
)

print(output_text)
```

## Intended Use

* **Spatial Reasoning Research**: Evaluating geometric and relational understanding in LVLMs.
* **Robotics & Embodied AI**: Scene grounding and object relationship modeling.
* **Autonomous Systems**: Depth and relative positioning analysis.
* **3D Scene Understanding**: RGB and RGB-D based spatial inference.
* **Multimodal Assistants**: Visual question answering with spatial awareness.

## Limitations & Risks

> **Note**: This model focuses on spatial reasoning and multimodal understanding.

* **Extreme Occlusions**: Performance may degrade when objects are heavily occluded.
* **Unusual Camera Geometry**: Severe distortion or non-standard projections may reduce spatial accuracy.
* **Hardware Requirements**: FP8 acceleration requires compatible GPU support for optimal throughput.
* **Responsible Use**: Ensure compliance with safety and deployment regulations in robotics or autonomous systems contexts.