Instructions to use Jiahao-Wang/lingbot-va-robotwin-8task-v2-step10000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Jiahao-Wang/lingbot-va-robotwin-8task-v2-step10000 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Jiahao-Wang/lingbot-va-robotwin-8task-v2-step10000", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
LingBot-VA RoboTwin 8-Task V2 Step 10000
This is a LingBot-VA inference bundle post-trained on eight RoboTwin tasks.
Checkpoint
- Base model:
/work/hdd/bfxg/jwang87/lingbot-va-base - Post-training run:
robotwin_8task_v2_base - Checkpoint step:
10000 - Training job: Slurm
2425436 - WandB run:
robotwin_8task_v2_10k_bs8_bs1_accum2 - Effective batch size:
8(4 GPUs * per-GPU batch size 1 * gradient accumulation 2)
Only the transformer/ weights were updated during post-training. The tokenizer/,
text_encoder/, and vae/ directories are copied from the LingBot-VA base checkpoint
so the downloaded directory can be passed directly to LingBot-VA inference.
Training Tasks
click_bellstack_bowls_twohandover_micplace_can_basketpick_diverse_bottlesshake_bottle_horizontallyopen_microwavemove_stapler_pad
press_stapler was intentionally held out for generalization testing.
Usage
huggingface-cli download Jiahao-Wang/lingbot-va-robotwin-8task-v2-step10000 \
--repo-type model \
--local-dir <target_dir>
Then use:
python -m torch.distributed.run --nproc_per_node 1 wan_va/wan_va_server.py \
--config-name robotwin \
--pretrained-model-path <target_dir> \
--port 29056
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