Search public pages, research tools, and SAVRN solutions.
EasyWAM-Unified-Wan22 is an open-weight model for robotics from OpenMOSS, released under Creative Commons Attribution-NonCommercial 4.0. Its published files total 10.6 GB.
This repository hosts released EasyWAM-Unified checkpoints built on Wan2.2-TI2V-5B.
This repository hosts released EasyWAM-Unified checkpoints built on Wan2.2-TI2V-5B. EasyWAM-Unified uses a single Video DiT to jointly represent and denoise video and action tokens, coupling both modalities bidirectionally through the same self-attention layers. The checkpoints are trained with the EasyWAM codebase. The LoRA checkpoint uses rank 128 (alpha 128) on the backbone Video DiT target modules. Other trainable EasyWAM modules are stored in the same checkpoint, so it must be loaded with the matching lora task recipe. Task success rate (%) under the EasyWAM LIBERO evaluation protocol. All results use stateposition: sequence; higher is better. Models using the same Wan2.2-TI2V-5B…
Excerpt from the card by OpenMOSS, licensed cc-by-nc-4.0.
5 files, 10.6 GB in total. The weights are 2 files totalling 10.6 GB in pt.
| File | Type | Size | SHA-256 |
|---|---|---|---|
| libero_unified_wan22.pt | Weights | 10.0 GB | a9c6e9e554d0 |
| libero_unified_wan22_lora_128.pt | Weights | 646.2 MB | f898370a0ad2 |
| libero_dataset_stats.json | Configuration | 41.0 KB | — |
| README.md | Documentation | 5.1 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
Released by OpenMOSS through its official repository on Hugging Face. Read the license.
| Precision | Weights in memory |
|---|---|
| As published | 10.6 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Not without separate permission. EasyWAM-Unified-Wan22 is released under Creative Commons Attribution-NonCommercial 4.0. CC BY-NC 4.0 permits sharing and adapting with credit for non-commercial purposes only. Commercial use needs separate permission from the rights holder.
LIBERO 4in1（liberospatial / liberoobject / liberogoal / libero10）共 53.19 GB 的 Wan2.2-VAE 编码 latent 缓存： 训练 LIBERO policy（action head / VLA）时直接读取 latent 缓存，避免重复 VAE 编码。 - 窗口模式: windowed（--windowed） - 输出:.pt 文件，每 episode 一个 - dataset（推荐）: https://huggingface.co/datasets/MangoGoes/libero4in1wan2.2vaelatentdataset - model（本仓库）: https://huggingface.co/MangoGoes/libero4in1wan2.2vaelatentcosmosstyle
Wan2.2-TI2V-5B video DiT + 48-joint action head, trainingmode=joint. The base is suhyeok's finalized B-method recipe: a teacher-forced (sigma=0.25) self-EMA teacher plus an iBOT prototype loss at L18 L18, gamma=0.01, two-view. On top of it the 3 PAST cond latent frames are pooled into one motion frame before a chosen block. These are NOT the surrogate ctxpool runs. The surrogate line (older base, pd8 x GA1) lives in hmkang/wamctxpoolxattn and hmkang/wamctxpoolavg. Do not compare across the two sets. Geometry: 4-latin (numframesin=25, numframesout=41, fdf 2) = 4 cond + 2 future latent slots, 96 tokens per latent frame, 576 tokens per row. Effective batch 16 clips x GA 2 x 2 views = 64 rows…
weighted/imatrix quants of https://huggingface.co/DeepCybo/PhysBrain1.5-8B For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/PhysBrain1.5-8B-GGUF This is a vision model - mmproj files (if any) will be in the static repository. If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here are Artefact2's thoughts on the matter…
static quants of https://huggingface.co/MirilAI/Miril-DroneVLM-2B-2 For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant…
Vivy-R-Robotics-3B (R-ROBOTICS-3B) is the embodied robotics and desktop/hardware actuation model of the Vivy.v6 R-Series ecosystem. It combines a complete 3.09-billion-parameter (3.09B) 36-layer language-to-mission compiler with a sub-millisecond ( 37.81% -> 75.62% -> 94.53% -> 95.82% -> 96.72%). - R-ROBOTICS-3B drives LAW3ROBOTICSHIERARCHICALASTCLOSURE and LAW7CROSSPAGE50PTABLECONTINUATION, enforcing deterministic structural hierarchy and kinematic state continuity across 50+ page documents.
Own lineage: PPO from scratch (nb1, commit 46c48b1) continued as nb2 and nb4 (commit fac88cc), checkpoint 850 (1.328 s); evolution strategies on the weights against the Arena sprint scorer (0.952 s); then the network frozen and wrapped in a step counter carried in the headroll action that adds joint-target and forward-command offsets from about 0.5 m to the line, found by CMA-ES on the event's official seeds: 0.940 s. No other entry's weights used. Simulation only. A perpetual policy for the microduck (61-D observation, 14 actions, 50 Hz). Runs until told otherwise — a gait, loaded into a policy slot. The observation normalizer is baked into policy.onnx; feed raw observations. manifest.json…