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nvidia
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Model Details
Model Provider
nvidia
Model Tree
Qwen/Qwen3-VL-8B-Thinking
Input Modalities
Output Modalities
Supported Functionality
EGM-Qwen3-VL-8B-SFT is the supervised fine-tuning (SFT) checkpoint from the first stage of the EGM (Efficient Visual Grounding Language Models) training pipeline. It is built on top of 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.
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 model.
bash
pip install -U huggingface_hubhuggingface-cli download nvidia/EGM-8B-SFT --local-dir ./models/EGM-8B-SFT
Then follow the installation instructions in the EGM repository, 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 verlbash scripts/grounding_qwen.sh
See the EGM repository for full RL training instructions.
| 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 |
| Model | Description |
|---|---|
| nvidia/EGM-8B | Final RL-trained model (best performance) |
| nvidia/EGM-4B-SFT | SFT checkpoint for the 4B variant |
| nvidia/EGM-4B | Final RL-trained 4B model |
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}}
This repository benefits from Qwen3-VL, InternVL, verl and verl-internvl.
| Max Position Embeddings | 262,144 |
| Vocabulary Size | 151,936 |