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gam-wilddet3d is an open-weight model for robotics from Gehao Zhang, released under other. Its published files total 4.8 GB.
The benchmark-independent weights of GAM (Grounded Action Model): the general WildDet3D detector (SAM3 backbone, text / click prompts, 2D + 3D box heads, depth head) and the universal span head that turns a task string into the object phrases to ground.
The benchmark-independent weights of GAM (Grounded Action Model): the general WildDet3D detector (SAM3 backbone, text / click prompts, 2D + 3D box heads, depth head) and the universal span head that turns a task string into the object phrases to ground. They are the defaults of the LeRobot / real-robot line of the released code (configs/lerobotbimanual.yaml): a LeRobot dataset with a task string can be trained on without manual labelling, but these weights are not tuned to any particular scene — on a new robot / camera / object set the detector (and the span head) may need fine-tuning for reliable grounding. Resolution matters. WildDet3D's native input is about 1k pixels (images are…
Excerpt from the card by Gehao Zhang, licensed other.
6 files, 4.8 GB in total. The weights are 2 files totalling 4.8 GB in pt.
| File | Type | Size | SHA-256 |
|---|---|---|---|
| span_head_universal.pt | Weights | 27.3 MB | 295372346788 |
| wilddet3d_general_v1.pt | Weights | 4.7 GB | f8b6a9e548f7 |
| config.json | Configuration | 997 B | — |
| README.md | Documentation | 4.7 KB | — |
| assets/gam_logo.svg | Other | 862 B | — |
| .gitattributes | Repository | 1.9 KB | — |
Released by Gehao Zhang through its official repository on Hugging Face. Read the license.
| Precision | Weights in memory |
|---|---|
| As published | 4.8 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
other, as its publisher declares it. Read the license text before commercial use.
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…
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…