Upload folder using huggingface_hub

README.md

@@ -0,0 +1,99 @@
+---
+license: apache-2.0
+language:
+  - en
+library_name: transformers
+base_model:
+  - Qwen/Qwen3-VL-8B-Thinking
+pipeline_tag: image-text-to-text
+tags:
+  - visual-grounding
+  - multimodal
+  - qwen3-vl
+  - supervised-fine-tuning
+---
+
+# EGM-Qwen3-VL-8B-SFT
+
+<p align="center">
+  <a href="https://nvlabs.github.io/EGM">[Project Page]</a> &nbsp;
+  <a href="https://github.com/NVlabs/EGM">[Code]</a> &nbsp;
+</p>
+
+<div align="center">
+  <img src="https://nvlabs.github.io/EGM/figure4.jpeg" width="90%"/>
+</div>
+
+## Model Summary
+
+**EGM-Qwen3-VL-8B-SFT** is the supervised fine-tuning (SFT) checkpoint from the first stage of the [EGM (Efficient Visual Grounding Language Models)](https://nvlabs.github.io/EGM) training pipeline. It is built on top of [Qwen3-VL-8B-Thinking](https://huggingface.co/Qwen/Qwen3-VL-8B-Thinking).
+
+This is an **intermediate checkpoint** intended for further reinforcement learning training. For the final model with best performance, see [nvidia/EGM-8B](https://huggingface.co/nvidia/EGM-8B).
+
+## Training Details
+
+### SFT Stage
+
+In the SFT stage, a proprietary VLM generates detailed chain-of-thought reasoning steps for visual grounding training data. The base Qwen3-VL-8B-Thinking model is then fine-tuned on this reasoning-augmented data to learn structured visual grounding with explicit reasoning.
+
+This SFT checkpoint serves as the initialization for the subsequent RL stage (GRPO), which yields the final [EGM-8B](https://huggingface.co/nvidia/EGM-8B) model.
+
+### How to Use for RL Training
+
+```bash
+pip install -U huggingface_hub
+huggingface-cli download nvidia/EGM-8B-SFT --local-dir ./models/EGM-8B-SFT
+```
+
+Then follow the installation instructions in the [EGM repository](https://github.com/NVlabs/EGM#installation), prepare the RL data and start training:
+
+```bash
+export BASE_DIR=$(pwd)
+export MODEL_PATH="${BASE_DIR}/models/EGM-8B-SFT"
+export OUTPUT_DIR="${BASE_DIR}/checkpoint/"
+export DATA_DIR="${BASE_DIR}/data/EGM_Datasets/processed_rl_data/"
+
+cd verl
+bash scripts/grounding_qwen.sh
+```
+
+See the [EGM repository](https://github.com/NVlabs/EGM#rl-training) for full RL training instructions.
+
+## Model Architecture
+
+| Component | Details |
+|---|---|
+| Architecture | Qwen3VLForConditionalGeneration |
+| Precision | bfloat16 |
+| Text Hidden Size | 4096 |
+| Text Layers | 36 |
+| Attention Heads | 32 (8 KV heads) |
+| Text Intermediate Size | 12,288 |
+| Vision Hidden Size | 1152 |
+| Vision Layers | 27 |
+| Patch Size | 16 x 16 |
+| Max Position Embeddings | 262,144 |
+| Vocabulary Size | 151,936 |
+
+## Related Models
+
+| Model | Description |
+|---|---|
+| [nvidia/EGM-8B](https://huggingface.co/nvidia/EGM-8B) | Final RL-trained model (best performance) |
+| [nvidia/EGM-4B-SFT](https://huggingface.co/nvidia/EGM-4B-SFT) | SFT checkpoint for the 4B variant |
+| [nvidia/EGM-4B](https://huggingface.co/nvidia/EGM-4B) | Final RL-trained 4B model |
+
+## Citation
+
+```bibtex
+@article{zhan2026EGM,
+    author = {Zhan, Guanqi and Li, Changye and Liu, Zhijian and Lu, Yao and Wu, Yi and Han, Song and Zhu, Ligeng},
+    title = {EGM: Efficient Visual Grounding Language Models},
+    booktitle = {arXiv},
+    year = {2026}
+}
+```
+
+## Acknowledgment
+
+This repository benefits from [Qwen3-VL](https://github.com/QwenLM/Qwen3-VL), [InternVL](https://github.com/OpenGVLab/InternVL), [verl](https://github.com/volcengine/verl) and [verl-internvl](https://github.com/Weiyun1025/verl-internvl).