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Open-weight model · Robotics
by Dongyun Kim Dongkkka/Task_000759_FLOWER_B200_bs8_step5000
Task_000759_FLOWER_B200_bs8_step5000 is an open-weight model for robotics from Dongyun Kim. Its published files total 15.2 GB.
FLOWER VLA fine-tuned on the ROBOTIS SG2 Task 000759 LeRobot v3 dataset. The SG2 action/proprioception heads and the six DiT layers absent from the public 12-layer checkpoint are initialized for this fine-tuning setup.
FLOWER VLA fine-tuned on the ROBOTIS SG2 Task 000759 LeRobot v3 dataset. The SG2 action/proprioception heads and the six DiT layers absent from the public 12-layer checkpoint are initialized for this fine-tuning setup. The compatible Florence-2 and FLOWER weights are retained. This repository contains a full PyTorch Lightning checkpoint, including model, EMA, optimizer, scheduler, and global-step state. It is intended for offline evaluation and controlled inference integration. It does not authorize robot actuation. Offline MAE will be added after the uploaded checkpoint is downloaded and evaluated with the fixed validation split (5%, seed 242).
Excerpt from the card by Dongyun Kim.
8 files, 15.2 GB in total. The weights are 1 file totalling 15.2 GB in ckpt.
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.ckpt | Weights | 15.2 GB | b110dba7fc76 |
| cyclo_input_metadata.json | Configuration | 1.3 KB | — |
| dataset_stats.json | Configuration | 11.0 KB | — |
| model_config.yaml | Configuration | 677 B | — |
| stats.json | Configuration | 1.9 KB | — |
| training_config.yaml | Configuration | 3.6 KB | — |
| README.md | Documentation | 1.3 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
Released by Dongyun Kim through its official repository on Hugging Face.
| Precision | Weights in memory |
|---|---|
| As published | 15.2 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
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…