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Open-weight model · Robotics
by Harrysunshine Harrysunshine/so101-dm05-lora-sim-real-10task
so101-dm05-lora-sim-real-10task is an open-weight model for robotics from Harrysunshine, released under Gemma Terms of Use. Its published files total 682.2 MB.
在 Dexmal/DM05 上做 LoRA 微调得到的 SO-101 单臂策略： 仿真与真机混成一份，共 3698 集 / 1,280,507 帧 / 10 个任务，全部 30 fps、640×480： - LoRA（all-linear），可训参数 324M / 6.15B = 5.27% - 动作模式 RELATIVE，动作块长度 50 - 等效 batch 48（4 卡 × 每卡 6 × 梯度累积 2），5000 步，bf16，cosine 学习率 成败取环境自带的…
在 Dexmal/DM05 上做 LoRA 微调得到的 SO-101 单臂策略： 仿真与真机混成一份，共 3698 集 / 1,280,507 帧 / 10 个任务，全部 30 fps、640×480： - LoRA（all-linear），可训参数 324M / 6.15B = 5.27% - 动作模式 RELATIVE，动作块长度 50 - 等效 batch 48（4 卡 × 每卡 6 × 梯度累积 2），5000 步，bf16，cosine 学习率 成败取环境自带的 issuccess（物体在料箱内、夹爪松开、物体与机械臂都静止），每局上限 500 步： 需要 dexbotic 框架。适配器里记录的底座是 Dexmal/DM05， 完整的数据准备、训练与仿真评测流程见 dexbotic 的 docs/so101.md，教学示例见 适配器随底座 Dexmal/DM05 采用 Gemma 许可。
Excerpt from the card by Harrysunshine, licensed gemma.
9 files, 682.2 MB in total. The weights are 1 file totalling 648.8 MB in safetensors.
| File | Type | Size | SHA-256 |
|---|---|---|---|
| adapter_model.safetensors | Weights | 648.8 MB | 94ef2181b725 |
| adapter_config.json | Configuration | 5.6 KB | — |
| norm_stats.json | Configuration | 3.4 KB | — |
| processor_config.json | Configuration | 560 B | — |
| README.md | Documentation | 2.7 KB | — |
| chat_template.jinja | Other | 1.5 KB | — |
| .gitattributes | Repository | 1.6 KB | — |
| tokenizer.json | Tokenizer | 33.4 MB | daab2354f8a7 |
| tokenizer_config.json | Tokenizer | 795 B | — |
Released by Harrysunshine through its official repository on Hugging Face.
| Precision | Weights in memory |
|---|---|
| As published | 648.8 MB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Yes, with conditions. so101-dm05-lora-sim-real-10task is released under Gemma Terms of Use. Gemma models are released under Google's Gemma Terms of Use, which permit commercial use and redistribution subject to the Gemma Prohibited Use Policy, whose restrictions must be passed on to anyone the model is distributed to.
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…
EgoSteer-3B-RealMan is the generalist policy from EgoSteer: A Full-Stack System Towards Steerable Dexterous Manipulation from Egocentric Videos. It is a world-model-enhanced Vision-Language-Action (VLA) policy built on a Qwen3-VL backbone with a flow-matching action expert, post-trained from EgoSteer-3B-Base on real-world demonstrations collected on the RealMan robot. This is a robot-ready dual-camera (head + chest) checkpoint, corresponding to EgoSteer-9.6K-DG in the paper. For the base model to fine-tune on your own data, see EgoSteer-3B-Base. Our full-stack system integrates EgoSmith (data pipeline), Robot Stack (deployment), and EgoSteer (policy) to learn from 9.6k hours of large-scale…