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Chutes

chutesai

Models in Library2
Datasets in Library1
Models on Hugging Face27
Followers16

Compare the published evidence

This profile connects the 3 model and dataset records shown below to their upstream cards and sources. The library counts describe SAVRN's coverage, rather than this publisher's entire output.

Published tasks: Text generation.

Reported licenses: mit, other. Open a record to review its specific license and conditions.

Compare model specifications, source dates, and hardware estimates on the individual records. Publisher identity verification is separate from a model's quality, deployment readiness, or suitability for your work.

Models

Model · Text generation

parallax-8b-kappa

Chutes

These are research checkpoints from kappa, a training run of Parallax that is still in progress. Parallax is a mixture-of-experts language model trained on consumer GPUs spread over several countries. The hosts have no direct connections to each other and synchronize over the public internet. - Base model. It is pretrained on a mixture of web, document, math, code, knowledge-focused and book text. It is not instruction-tuned, chat-tuned or safety-tuned, and it will continue text rather than follow instructions. - Mid-training. Each export is a snapshot of a run that has not finished. Later exports are expected to differ, and quality between exports is not monotonic. - Research artifact. It…

Open weights mit

Model · Text generation

parallax-8b-lambda

Chutes

These are research checkpoints from lambda, a training run of Parallax that is still in progress. Parallax is a mixture-of-experts language model trained on consumer GPUs spread over several countries. The hosts have no direct connections to each other and synchronize over the public internet. - Base model. It is pretrained on a mixture of web, document, math, code, knowledge-focused and book text. It is not instruction-tuned, chat-tuned or safety-tuned, and it will continue text rather than follow instructions. - Mid-training. Each export is a snapshot of a run that has not finished. Later exports are expected to differ, and quality between exports is not monotonic. - Research artifact. It…

Open weights mit

Datasets

Canonical archive of the parallax 1T-token production curriculum, phase-split, EXACTLY as prod runs it. NOT a text dataset: artifacts are flat llama3 uint32 token bins (EOS 128001 between docs), val bins (doc-level 1/100), plus per-pool mixmanifest.json / poolconfig.json provenance and SHA256SUMS. Files >45GB are stored as.part-NNN chunks (cat name.part- > name to reassemble; per-part sha256 in CHUNKS.sha256). (4a) -> 5e6-8e6 or YaRN (4b). Phase-4 replay packed with document-boundary masking ON. Realized per-source shares, exact token counts, floors, sampling seeds, decontamination (13-gram, stride 4) and every assembly decision/deviation: see MANIFEST.json and phase34mixmanifest.json.…

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