Text Generation
Transformers
English
Russian
diffusion-language-model
dllm
decoding-strategy
levenshtein-editing
LLaDA
inference-time
research
Instructions to use Freedomform/wet-ink-v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Freedomform/wet-ink-v5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Freedomform/wet-ink-v5")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Freedomform/wet-ink-v5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Freedomform/wet-ink-v5 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Freedomform/wet-ink-v5" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Freedomform/wet-ink-v5", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Freedomform/wet-ink-v5
- SGLang
How to use Freedomform/wet-ink-v5 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Freedomform/wet-ink-v5" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Freedomform/wet-ink-v5", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Freedomform/wet-ink-v5" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Freedomform/wet-ink-v5", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Freedomform/wet-ink-v5 with Docker Model Runner:
docker model run hf.co/Freedomform/wet-ink-v5
detach PDF and Space links; paper website (EN/RU/UZ) is the hub
Browse files
README.md
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[](https://wetink.space-z.ai)
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[](https://github.com/Asilbekov/Explore)
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[-b31b1b)](https://huggingface.co/Freedomform/wet-ink-v5/blob/main/paper/wet-ink-paper.pdf)
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[](https://huggingface.co/spaces/Freedomform/wet-ink)
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[](https://huggingface.co/Freedomform/wet-ink-v5/blob/main/wet-ink-report-v5.pdf)
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[](https://github.com/Asilbekov/Explore/blob/main/LICENSE)
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<img src="assets/wet_ink_decoding.gif" alt="Wet-ink decoding animation: the frontier commits one token per step; a sliding window W behind it stays editable; tokens beyond the window are dry and frozen forever." width="100%"/>
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result: quality is *pair-identical* to the stock parallel decoder (exact McNemar p = 1.0)
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at a structural ×4.88 NFE cost — a negative result published as such.
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📖 The paper is also available as an interactive website
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**[wetink.space-z.ai](https://wetink.space-z.ai)**.
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**Read the paper as a website — [wetink.space-z.ai](https://wetink.space-z.ai)**:
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title, abstract, all sections, linked artifacts, citation export and a discussion board,
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in the style of a Hugging Face Papers page.
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A full paper preprint (arXiv-ready, 10 pages, LaTeX) is available in this repo:
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**[paper/wet-ink-paper.pdf](https://huggingface.co/Freedomform/wet-ink-v5/blob/main/paper/wet-ink-paper.pdf)**
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(LaTeX source: [paper/wet-ink-paper-arxiv-source.zip](https://huggingface.co/Freedomform/wet-ink-v5/blob/main/paper/wet-ink-paper-arxiv-source.zip)).
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## 📚 Provenance — every formula has a source
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Code, full data (v1→v5), tests and the technical report:
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**[github.com/Asilbekov/Explore](https://github.com/Asilbekov/Explore)** ·
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Paper website: **[wetink.space-z.ai](https://wetink.space-z.ai)**
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Interactive visualization: **[wet-ink Space](https://huggingface.co/spaces/Freedomform/wet-ink)**
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Questions, replications and counter-experiments are welcome — open an issue on GitHub.
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[](https://wetink.space-z.ai)
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[](https://github.com/Asilbekov/Explore)
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[](https://github.com/Asilbekov/Explore/blob/main/LICENSE)
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<img src="assets/wet_ink_decoding.gif" alt="Wet-ink decoding animation: the frontier commits one token per step; a sliding window W behind it stays editable; tokens beyond the window are dry and frozen forever." width="100%"/>
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result: quality is *pair-identical* to the stock parallel decoder (exact McNemar p = 1.0)
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at a structural ×4.88 NFE cost — a negative result published as such.
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📖 The paper is also available as an interactive website in three
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languages (EN / RU / UZ):
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**[wetink.space-z.ai](https://wetink.space-z.ai)**.
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---
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**Read the paper as a website — [wetink.space-z.ai](https://wetink.space-z.ai)**:
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title, abstract, all sections, linked artifacts, citation export and a discussion board,
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in three languages (English · Русский · Oʻzbek), in the style of a Hugging Face Papers page.
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## 📚 Provenance — every formula has a source
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Code, full data (v1→v5), tests and the technical report:
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**[github.com/Asilbekov/Explore](https://github.com/Asilbekov/Explore)** ·
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Paper website (EN / RU / UZ): **[wetink.space-z.ai](https://wetink.space-z.ai)**
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Questions, replications and counter-experiments are welcome — open an issue on GitHub.
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