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pretty_name: AssemLM 2.0 Checkpoint
license: other
tags:
- robotic-assembly
- point-cloud
- 3d-vision
- multimodal
- pose-estimation
---
# **AssemLM 2.0 Model**
**Related links:** [AssemLM 1.0 paper (arXiv)](https://arxiv.org/abs/2604.08983) 路 [AssemLM official website](https://assemlmhome.github.io/) 路 [Model repository](https://huggingface.co/TeleEmbodied/AssemLM2.0) 路 [Dataset repository](https://huggingface.co/datasets/TeleEmbodied/AssemLM2.0_Datasets)
This directory contains the AssemLM 2.0 checkpoint (`epoch_0215_steps_2111515`) and the matching training/evaluation configuration. The model predicts a 9D pose for the moving part of a two-part assembly from two point clouds, two rendered manual images, and an object-category instruction.
## Files
| File | Description |
|---|---|
| `config.yaml` | Active AssemLM 2.0 model and training configuration |
| `epoch_0215_steps_2111515/pytorch_model/mp_rank_00_model_states.pt` | Full model checkpoint of the release |
Checkpoint details:
| Item | Value |
|---|---|
| Checkpoint directory | `epoch_0215_steps_2111515` |
| Recorded epoch / step | `215` / `2,111,515` |
| Checkpoint size | `4,482,346,012` bytes (about `4.17 GiB`) |
| Stored format | PyTorch state dictionary |
| SHA-256 | `ae2030284436e500ff090ccebb9c6135493c7ddc6f522d3ce03125d3f72aa9fc` |
## Model architecture
| Component | Configuration |
|---|---|
| Framework | `AssemLM2` |
| Base VLM | Qwen3-VL-2B-Instruct |
| Attention implementation | `eager` |
| Point encoder | `vn_dgcnn_patch` |
| Point feature dimension | 512 |
| Patch construction | 128 patches, patch size 4, FPS-KNN |
| Pooling / normalization | mean pooling, RMSNorm |
| Patch bridge | 4 transformer layers, hidden size 1024, 8 heads |
| Bridge layout | `four_branch_equiv_invariant` |
| Point projector | hidden sizes `[1024, 2048]`, output size 2048 |
| Point M-RoPE | grid size 64, `joint_isotropic` |
| Point attention | custom causal attention |
| Pose output | 9D: 3D translation + 6D rotation |
| Pose head | hidden size 4096, 2 residual blocks |
## Licensing
This checkpoint is released for research use. It was trained on the AssemLM 2.0 data collection, which aggregates several upstream 3D assembly datasets with different terms (IKEA-Manual, PartNet, PartNeXt, BiAssemble, Two by Two). This release does not supersede any upstream license, and users remain responsible for complying with the terms of the original datasets.
## Citation
If this project is useful to you, please consider citing:
```bibtex
@article{jing2026assemlm,
title={AssemLM: A Spatial Reasoning Multimodal Large Language Model for Robotic Assembly},
author={Jing, Zhi and Qiao, Jinbin and Lu, Ouyang and Ao, Jicong and Qiu, Shuang and Xu, Huazhe and Jiang, Yu-Gang and Bai, Chenjia},
journal={arXiv preprint arXiv:2604.08983},
year={2026}
}
```
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