# microsoft/KBLaM

Official Implementation of "KBLaM: Knowledge Base augmented Language Model"

Repository: https://github.com/microsoft/KBLaM
Canonical: https://ross.abutalabs.com/products/kblam
Homepage: https://arxiv.org/abs/2410.10450
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2026-07-02T07:09:23+00:00

## Health v2 (maintenance only)
Score: 64/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 90, release rhythm 35, longevity 54
- inputs: {"age_days": 762, "days_push": 62, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1451, forks 124 (observed 2026-08-28T04:04:46.062099+00:00)

## What it is
Official implementation of KBLaM, a method for augmenting pre-trained LLMs with external knowledge by encoding a knowledge base into continuous key-value vectors integrated via a rectangular attention mechanism. It eliminates external retrieval modules and scales linearly with knowledge base size, supporting Llama and Phi-3 models.

## Use cases
- augment an LLM with a knowledge base without a retrieval module
- integrate 10K+ knowledge triples into an 8B model on a single GPU
- replicate the KBLaM paper experiments
- generate synthetic QA datasets for knowledge-augmented training
- dynamically update LLM knowledge without fine-tuning

## When to choose
- you want retrieval-free knowledge augmentation with linear scaling in KB size
- you need to reproduce or extend the KBLaM research
- you want to inject a large knowledge base into a small-context LLM

## When to avoid
- you need a production-ready RAG pipeline with mature tooling
- your model architecture is not among the supported Llama/Phi-3 variants
- you lack GPU resources for training

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, llm-training, rag, nlp
- domain: large-language-models, machine-learning, artificial-intelligence
- platform: python
- tags: knowledge-base, llm-augmentation, research-code, iclr-2025, microsoft, retrieval-augmented-generation, gpu

## Member repositories
- microsoft/KBLaM (main) score 64

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:46.062099+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 2026-08-30T04:35:51.929191+00:00, confidence not recorded.
  - readme: https://github.com/microsoft/KBLaM (fetched 2026-08-28T04:04:46.062099+00:00, sha 2ab96e054250)
  - homepage: https://arxiv.org/abs/2410.10450 (fetched 2026-08-29T11:45:20.295363+00:00, sha de3d11e39305)
  - site_page: https://info.arxiv.org/about/donate.html (fetched 2026-08-29T11:45:20.298864+00:00, sha cca9c3a11c56)
  - site_page: https://info.arxiv.org/about/ourmembers.html (fetched 2026-08-29T11:45:20.303292+00:00, sha 47cbc55ff1de)
  - site_page: https://info.arxiv.org/about (fetched 2026-08-29T11:45:20.306097+00:00, sha a1f16f915a9a)
  - site_page: https://info.arxiv.org/labs/index.html (fetched 2026-08-29T11:45:20.301139+00:00, sha b14a8d05a0ec)
- Data as of 2026-08-30T08:39:29.467469+00:00.