# apple/ml-clara

Repository: https://github.com/apple/ml-clara
Canonical: https://ross.abutalabs.com/products/ml-clara
Language: Python
License: NOASSERTION
License Family: other
Last push: 2025-12-19T17:17:56+00:00

## Health v2 (maintenance only)
Score: 43/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 58, release rhythm 35, longevity 21
- inputs: {"age_days": 294, "days_push": 257, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1153, forks 99 (observed 2026-08-28T04:03:47.502537+00:00)

## What it is
CLaRa is Apple's open-source end-to-end Retrieval-Augmented Generation model that compresses documents into continuous latent representations (32x-64x compression) unifying retrieval and generation in a single optimized model. The repository provides training code, evaluation data, and 7B model checkpoints (Base, Instruct, E2E) on Hugging Face.

## Use cases
- build a RAG system with compressed document representations
- reduce long-context costs in retrieval-augmented generation
- fine-tune a model that jointly optimizes retrieval and generation
- compress knowledge documents into latent embeddings for LLMs
- evaluate state-of-the-art RAG compression approaches
- run an end-to-end RAG model without a separate retriever

## When to choose
- you want cutting-edge research RAG with document compression
- long context lengths are a bottleneck in your RAG pipeline
- you want to reproduce or build on the CLaRa paper
- you need jointly trained retrieval and generation instead of separate components

## When to avoid
- you need a production-ready plug-and-play RAG framework with broad ecosystem support
- you require permissive licensing (Apple license, NOASSERTION)
- you need small models or CPU-only inference
- you want a mature tool with long-term community support

## Facets
- artifact type: library
- maturity: active
- function: rag, llm-inference, machine-learning, search-engine
- domain: large-language-models, machine-learning
- platform: python
- tags: document-compression, latent-reasoning, huggingface, research-model, apple, retrieval-augmented-generation, natural-language-processing, gpu, linux, macos

## Member repositories
- apple/ml-clara (main) score 43

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:47.502537+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:33:27.096118+00:00, confidence not recorded.
  - readme: https://github.com/apple/ml-clara (fetched 2026-08-28T04:03:47.502537+00:00, sha 687ea99e854f)
- Data as of 2026-08-30T08:39:29.467469+00:00.