Galactica vs LlamaIndex

Five open-weight checkpoints from 125M to 120B parameters give researchers a science-trained language model built from 106 billion curated tokens across 48 million papers, textbooks, encyclopedias, and knowledge bases. Galactica stores, combines, and reasons across modalities including LaTeX, Python code, SMILES formulas, and amino acid sequences inside one decoder-only Transformer architecture. Meta's Papers with Code team open sourced the weights for researchers who want to study how language models organize scientific knowledge.

General-purpose LLMs train on broad web crawls where social chatter can dominate the token budget. Galactica used only curated open-access science sources, which the paper argues lets it train for multiple epochs without overfitting. Meta removed the public web demo three days after launch in November 2022 when critics showed confident but fabricated citations and equations, so today you download checkpoints from Hugging Face rather than chat through a hosted interface.

Machine learning researchers can reproduce benchmark numbers from the arXiv paper, including 77.6% on PubMedQA and 52.9% on MedMCQA dev. Computational biologists and chemists can test protein annotation and molecule tasks through Galactica's specialized tokens for amino sequences and SMILES strings. Graduate students exploring scientific QA, citation prediction, or math word problems can experiment with sizes up to 120 billion parameters without training from scratch.

Developers building LLM apps use LlamaIndex to parse messy documents before retrieval or agent steps. LlamaParse turns PDFs, scans, tables, charts, and handwritten notes into structured markdown and JSON, then adds schema-based extraction, classification, splitting, and indexing on top. Open-source LlamaIndex and Workflows libraries cover the same RAG building blocks for teams that want to self-host pieces of the stack.

Where generic OCR tools stop at plain text, LlamaParse routes pages through task-specific agents with auto-correction loops, so messy layouts survive as clean markdown or JSON without custom templates. Auto Mode picks a parse tier per page and can cut credit spend by up to 80%, which matters when you are processing invoices, claims, or technical manuals at volume rather than one-off uploads.

Teams in finance, insurance, manufacturing, and healthcare use LlamaIndex to feed LLMs and document agents with citation-backed fields instead of brittle copy-paste. Developers get Python and TypeScript SDKs, a REST API, and optional VPC deployment when SaaS data residency is not enough.