Instructions to use Wjjjh/Recursive-VAM-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Wjjjh/Recursive-VAM-models with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Wjjjh/Recursive-VAM-models", torch_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
Add model card and reproduction pointer
Browse files
README.md
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license: apache-2.0
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---
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---
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license: apache-2.0
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tags:
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- robotics
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- world-model
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- recursive-search
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- libero
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# Recursive-VAM Task-3 models
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Frozen model bundle for continuing the sparse-milestone and dynamic recursive
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depth experiments in
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[JunhaoWu7/Recursive-VAM](https://github.com/JunhaoWu7/Recursive-VAM).
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Contents:
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- `libero_goal_step5000_model/`: resolved transformer, text encoder, tokenizer,
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and Wan VAE used by the frozen same-root D1/D2/D3 experiment;
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- `action_semantic_goal_object_v3_seed42/`: frozen sparse cumulative milestone
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head and its training summary.
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The authoritative protocol, hashes, data paths, and interpretation rules are
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in `docs/TASK3_HANDOFF_20260727.md` and
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`experiments/task3_artifact_manifest.json` in the GitHub repository.
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This repository contains model artifacts only. Matching preencoded data is in
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the private dataset repository `Wjjjh/Recursive-VAM-data`.
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