Alternatives
Products that do what SmolDocling does
256M VLM for end-to-end document AI
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10
- 11
- 12
- 13
- 14
- 15
- 16
- 17
- 18OU
The traditional pipeline for unstructured data extraction typically follows these steps: 1. Image → OCR Model (e.g., Google Vision) → Layout Model (e.g. Surya) → LLM → Final Answer However, this can be streamlined using a Vision-Language Model (VLM): 2. Image → VLM → Final Answer Recently VLMs have improved a lot for OCR and document understanding tasks, specifically the Qwen-2.5-VL series. We can run the Qwen-2.5-VL-7B-AWQ model locally with just 16GB VRAM, and perform end-to-end information extraction (fields and table extraction) without any external models. Hallucination with VLMs One…
2025 · github.com
- 19
- 20IM
I made an open-source Python library for the Stable Diffusion Web UI. It's a direct alternative to Huggingface Diffusers except it has more features + runs the same scripts as A1111 so the results are replicable. Please give it a star on Github! https://github.com/saketh12/Auto1111SDK
2024 · github.com
- 21
- 22VB
In modern document question answering (QA) systems, Optical Character Recognition (OCR) serves an important role by converting PDF pages into text that can be processed by Large Language Models (LLMs). The resulting text can provide contextual input that enables LLMs to perform question answering over document content. Traditional OCR systems typically use a two-stage process that first detects the layout of a PDF — dividing it into text, tables, and images — and then recognizes and converts these elements into plain text. With the rise of vision-language models (VLMs) (such as Qwen-VL and…
Oct 2025 · github.com
- 23UA
Most of the document parsers fail on real world challenges like complex tables, handwritten documents, historical document scans, equations, multi-column layouts, complex reading order, etc. We built Unsiloed Parser to handle exactly these cases. Our latest parser v3.1 achieved #1 rank and scored 88.0 strict pass-rate on olmOCR-Bench. We ran the evaluation across 1,403 PDFs and 8,413 unit tests using the unmodified upstream Allen AI scorer (olmocr==0.4.27) and found Unsiloed beats 18 other OCR services, including GPT-5.5, Claude Opus 4.7, LlamaParse, Reducto, Azure Document Intelligence, AWS…
May 2026
- 24SE
I built a CLI tool in Go that extracts structured data (JSON, CSV, Parquet) from messy PDFs and HTML pages. The core idea: LLMs are great at understanding structure but wasteful for bulk data extraction. So smelt uses a two-pass architecture: 1. A fast Go capture layer parses the document and detects table-like regions 2. Those regions (not the whole document) get sent to Claude for schema inference — column names, types, nesting 3. The Go layer then does deterministic extraction using the inferred schema This means the LLM is never in the hot path of actual data processing. It figures out…
Mar 2026 · github.com
Ranked by how close each launch is in meaning, then by votes. Refine with a description →