Distilabel

Argilla's framework for synthetic data generation — pipelines of LLM steps that produce SFT/DPO training data; integrates with Hub.

Active · Verified 2026-10-27 · fresh as of 2026-10-27 · 15 cited facts, 1 with a third-party source · 1 recorded change · Suggest a correction

At a glance

Type: Synthetic-data pipelines for LLMs repo fix
Tier: T3
Section: Training infrastructure
Created: 2024 repo fix
Latest release: v1.5.3 (2025-01-28) registry fix
License: Apache-2.0 repo fix
GitHub: argilla-io/distilabel — ~3.4k stars, last commit 2026-10 repo fix
Pricing: searched not found fix
Funding: searched not found fix
Freshness: 2026-10-27 fix
Layer: model-infra repo fix
Licensing: open-source repo fix
Deployment locus: local repo fix
Platform vs product: platform repo fix
Developer activity: OpenRank 0.45 (12-mo avg 2.03) · 3 participants — 2026-09 third-party fix
Maintainer health: 4 issues in 90 days, 0% answered or closed · 0 releases in 12 months (latest 2025-01-28) · bus factor 2 — as of 2026-10-31 repo

Optimised for: searched not found

Anti-fit: searched not found

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Other training infrastructure in the catalog, ranked by inbound references.

MLflow T1
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AWS SageMaker T1
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Weights & Biases T1
Experiment-tracking and ML-ops platform (metrics, artifacts, sweeps, Weave tracing/evals). Acquired by CoreWeave (completed 2025-05-05, ~$1.7B); now part of CoreWeave Forge.
Comet ML T2
ML experiment tracking + LLM evaluation platform (Opik for LLM eval). Comet competes head-to-head with Weights & Biases.
Azure Machine Learning T1
Microsoft Azure's managed ML platform — pipelines, AutoML, Designer, prompt flow (LLM). Heavy integration with Azure OpenAI and Azure AI Studio.
LoRA T3
Microsoft Research's Low-Rank Adapters method — trains rank-r matrices added to attention weights; 10000x parameter reduction. Foundation of all adapter-style fine-tuning.

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2026-10-27
- Latest release first recorded as “v1.5.3 (2025-01-28)” source
- Deployment first recorded as “Self-hosted only — library” source
#271