Instructions to use AlexWortega/tinyvla with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use AlexWortega/tinyvla with LeRobot:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Commit History
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VLA closed-loop driver (fixed head camera + auto-gain): falls <1s all behaviors — BC covariate shift on unstable system
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closed-loop MicroDuck harness: obs.rs 61D builder validated by ONNX teacher (stands 8s @10Hz replan)
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CRITICAL fix: torch.compile'd expert saved as expert._orig_mod.* -> from_pretrained silently dropped it (random flow head at eval). Save the uncompiled module
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lekiwi zero-shot eval harness (hub streaming, 2x2 demo/descriptor grid)
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eval_microduck: zero-shot eval+render harness (v4 mode: demos from same behavior, duck descriptor)
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train_fast: non-persistent hub workers (recycle each epoch to reset leaks)
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wds mixture: unitree+nav converters (gnm/vamos), wds source branch in train_fast, AMP autoselect (V100 fp16), embedding expand 16->32, push/fetch/validate tooling, DATA.md
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train_fast: v4 all-channels via hub stream (support passthrough, morph_text tokenization, guarded shard morph block)
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fix DDP hang: spawn dataloader workers (fork-after-CUDA/NCCL starved torchrun heartbeat), picklable Collate, fewer workers per rank
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fix: yaml 1.1 parses '2250.0e12' as string -> float() cast in MFU calc (crashed first log step)
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train_fast: torchrun DDP (per-rank streaming, rank0 logging/saves, global throughput)
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tinyvla_b200: B200 streaming training on nvidia/physical-ai (745 samples/s e2e, 7x ViT fast path, hub episode streaming, full instructions)
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