ollada
The shelf for open-source diffusion language models.
Ollama is where people go to get open-source autoregressive models: clean names, one command, it just works. Diffusion language models (dLLMs) — LLaDA, Dream, SDAR, Stable-DiffCoder — have none of that. They're scattered across Hugging Face with incompatible generation code, undocumented defaults, and no verified "this actually runs" guarantee.
ollada is building the equivalent for diffusion:
pip install ollada
ollada pull diffcoder:8b
ollada run diffcoder:8b
ollada serve # local API on http://localhost:11435
- Curated library, short names —
dream:7b,sdar:4b,diffcoder:8b. A model is on the shelf only after a real-load verification run on real GPU hardware. - Verified recipes, not homework — the steps / block size / threshold / quantization that actually work are baked into each model's recipe, tested and published with the hardware they were measured on.
- A local API tools can point at — Ollama-shaped and OpenAI-shaped endpoints.
Status
0.0.1 — name-claim / planning release. The full plan (product definition, recipe format, initial 3-model shelf, verification gate, phased timeline) is in PLAN.md. No models are served yet; the runtime is in development.
Named the way Ollama was: Ollama ← LLaMA (the first big open autoregressive model). Ollada ← LLaDA (the first big open diffusion language model, Feb 2025).
Principles
- $0 project: no telemetry, no phone-home, no paid services.
- GPU-first and honest about it (diffusion on CPU is minutes per answer).
- Vendor benchmarks are never presented as our measurements.
License
MIT
1 maintainer
Metadata
1 maintainer
Release files for ollada 0.0.1
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