Update model card (trimmed to match the Chinese version)

README.md

@@ -9,16 +9,11 @@ tags:
   - pose-estimation
 ---
 
-# **AssemLM 2.0 Checkpoint**
-
-**Language:** English | [中文](README_zh.md)
+# **AssemLM 2.0 Model**
 
 **Related links:** [AssemLM 1.0 paper (arXiv)](https://arxiv.org/abs/2604.08983) · [AssemLM official website](https://assemlmhome.github.io/) · [Model repository](https://huggingface.co/TeleEmbodied/AssemLM2.0) · [Dataset repository](https://huggingface.co/datasets/TeleEmbodied/AssemLM2.0_Datasets)
 
-This directory contains the AssemLM 2.0 continuous-pose 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.
+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
 
@@ -27,10 +22,6 @@ point clouds, two rendered manual images, and an object-category instruction.
 | `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 |
 
-Local working copies may additionally contain `README_zh.md` (Chinese version of
-this card) and backup files such as `config copy.yaml`; these are not part of the
-repository.
-
 Checkpoint details:
 
 | Item | Value |
@@ -41,23 +32,12 @@ Checkpoint details:
 | Stored format | PyTorch state dictionary |
 | SHA-256 | `ae2030284436e500ff090ccebb9c6135493c7ddc6f522d3ce03125d3f72aa9fc` |
 
-The Hugging Face repository is private and currently contains:
-
-```text
-.gitattributes
-config.yaml
-epoch_0215_steps_2111515/pytorch_model/mp_rank_00_model_states.pt
-```
-
 ## Model architecture
 
-AssemLM 2.0 combines a Qwen3-VL vision-language backbone with a
-rotation-equivariant point-cloud encoder and a continuous pose-regression head.
-
 | Component | Configuration |
 |---|---|
 | Framework | `AssemLM2` |
-| Base VLM | Qwen3-VL-2B-Instruct, supplied through `ASSEMLM_VLM_PATH` |
+| Base VLM | Qwen3-VL-2B-Instruct |
 | Attention implementation | `eager` |
 | Point encoder | `vn_dgcnn_patch` |
 | Point feature dimension | 512 |
@@ -66,129 +46,14 @@ rotation-equivariant point-cloud encoder and a continuous pose-regression head.
 | 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 | enabled, grid size 64, `joint_isotropic` |
-| Point attention | bidirectional over all point tokens |
+| 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 |
 
-## Data contract
-
-Training and evaluation discover every `assemlm_*.hdf5` file below
-`ASSEMLM_DATA_ROOT`.
-
-```yaml
-data_features: src_pc,tgt_pc,imgs,lang,asset,category,pre_pose
-import_rotation: true
-centering_mode: mean
-```
-
-The loader applies rotation augmentation and mean centering. Translation
-augmentation is not used by this release.
-
-Each HDF5 sample provides:
-
-| Field | Description |
-|---|---|
-| `partA-pc` | Moving-part point cloud |
-| `base_partB-pc` | Fixed/base-part point cloud |
-| `image_base_<manual>` | Base-part manual image |
-| `image_assemble_<manual>` | Assembled-state manual image |
-| `category` | Object category |
-
-The `<manual>` suffix is `lineart` for Two by Two and `freestyle` for the other
-release datasets.
-
-## Evaluation metrics
-
-The AssemLM 2.0 evaluator reports:
-
-| Metric | Meaning |
-|---|---|
-| `Avg GD` | Mean geodesic rotation error |
-| `RMSE(T)` | Translation RMSE |
-| `CD` | One half of the symmetric Chamfer distance |
-| `PA(0.01)` | Part accuracy for `CD < 0.01` |
-| `PA(0.008)` | Part accuracy for `CD < 0.008` |
-| `PA(0.005)` | Part accuracy for `CD < 0.005` |
-| `CD(R)` | Translation-corrected Chamfer distance, also divided by two |
-
-## Loading and evaluation
-
-Set the required paths:
-
-```bash
-export ASSEMLM_VLM_PATH=/path/to/Qwen3-VL-2B-Instruct
-export CONFIG_PATH=/path/to/AssemLM2/config.yaml
-export CHECKPOINT_PATH=/path/to/AssemLM2/epoch_0215_steps_2111515/pytorch_model/mp_rank_00_model_states.pt
-```
-
-Run the release evaluation launcher:
-
-```bash
-cd /path/to/AssemLM
-bash scripts/eval_assemlm_v2_seed0_9.sh
-```
-
-The configuration uses:
-
-```yaml
-base_vlm: ${oc.env:ASSEMLM_VLM_PATH}
-```
-
-Therefore `ASSEMLM_VLM_PATH` must be available before loading `config.yaml`.
-
-To start the GUI with automatic dataset and model loading:
-
-```bash
-assemlm-gui \
-  --host 0.0.0.0 \
-  --port 7860 \
-  --dataset /path/to/assemlm_partnet.hdf5 \
-  --checkpoint "${CHECKPOINT_PATH}" \
-  --config "${CONFIG_PATH}"
-```
-
-To construct the model directly:
-
-```python
-from omegaconf import OmegaConf
-import torch
-
-from assemlm.model.framework import build_framework
-
-cfg = OmegaConf.load("config.yaml")
-model = build_framework(cfg)
-state_dict = torch.load(
-    "epoch_0215_steps_2111515/pytorch_model/mp_rank_00_model_states.pt",
-    map_location="cpu",
-)
-if isinstance(state_dict, dict) and "state_dict" in state_dict:
-    state_dict = state_dict["state_dict"]
-model.load_state_dict(state_dict, strict=True)
-model.eval()
-```
-
-## Intended use
-
-This checkpoint is intended for:
-
-- AssemLM 2.0 evaluation and reproducibility studies;
-- continuous 6D pose prediction for two-part robotic assembly;
-- multimodal spatial reasoning from point clouds, rendered manuals, and text.
-
-The matching training/evaluation data is published in the companion dataset
-repository [`TeleEmbodied/AssemLM2.0_Datasets`](https://huggingface.co/datasets/TeleEmbodied/AssemLM2.0_Datasets)
-(five full HDF5 files plus the `preview/` samples and the lightweight `sub500/`
-subsets used by the GUI).
-
 ## 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); see the
-**Licensing** section of the dataset card for the per-subset details. This
-release does not supersede any upstream license, and users remain responsible
-for complying with the terms of the original datasets.
+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