Instructions to use easylearning/FutureWorlds with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use easylearning/FutureWorlds with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("easylearning/FutureWorlds", 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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Download code/THIRD_PARTY_NOTICES.md from easylearning/FutureWorlds: direct link, hf CLI and curl.
- Browser
- Download file 805 Bytes
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https://huggingface.co/easylearning/FutureWorlds/resolve/main/code/THIRD_PARTY_NOTICES.md
- Command line
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hf download hf://easylearning/FutureWorlds/code/THIRD_PARTY_NOTICES.md
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curl -L -o THIRD_PARTY_NOTICES.md https://huggingface.co/easylearning/FutureWorlds/resolve/main/code/THIRD_PARTY_NOTICES.md
805 Bytes
| # Third-party provenance | |
| This is a local research-code consolidation, not a public release. | |
| - `objective.py` preserves the original ByteDance/Hugging Face Apache-2.0 header from the audited RLVR-World/verl-derived implementation. | |
| - `reward_core.py` preserves its original upstream header. The audited upstream RLVR-World reference is commit `e1b8b6f40ca0696919ce9b8dbe965c4153bcf5f1`. | |
| - Tokenizer and backbone loading in the archived trainers rely on iVideoGPT/RLVR-World components, Hugging Face Transformers and PyTorch. Those model assets and datasets are not included here. | |
| - The local source manifest records file-level provenance. Before public distribution, consolidate the applicable full upstream licenses and choose the license for original additions; no blanket relicensing is implied here. | |