Testing Qwen3.8-27B on a Hard Pivot Table (No Column-Shift Errors)
Testing Qwen3.8-27B on a hard document extraction case: an insurance pivot table with row/column headers and aggregated values instead of a flat list. Same table from two prior tests, same generic "*"...
View ArticleTable Only Extraction Mode on Large Financial Statement
In this video I test Sparrow table only extraction mode on a large financial statement table. I use a 6 month property management sample data, with 51 rows and 7 columns, including section headers,...
View ArticleBigger Isn't Always Better: Gemma 31B vs Ministral 14B on a Pivot Table
More parameters doesn't automatically mean better extraction. Same insurance pivot table as the last video, same generic "*" query, no schema, but this time run through Sparrow's Advanced mode, backed...
View ArticleCan an LLM Read a Pivot Table? Sparrow + Ministral 14B
Pivot tables are a genuinely hard case for LLM-based extraction — row/column headers, merged cells, and aggregated values instead of a flat list. In this video I test whether Ministral 14B, running in...
View ArticleSparrow Standard Mode: Document Extraction with Ministral 14B
A look at Sparrow's Standard tier, powered by Ministral 3 14B via MLX-VLM.In this video I run a document through Sparrow's UI (sparrow.katanaml.io/process) using the Standard model — Ministral 14B. It...
View ArticleMistral OCR 4 + Sparrow: Document to JSON
Follow-up to the Mistral OCR + Sparrow integration video. Mistral released OCR 4 — the latest model with improved accuracy, native bounding box extraction, and structural block labels. One model string...
View ArticleBuilding an AI Agent That Searches the Web and Makes Investment Decisions
In this video I build a local agentic AI pipeline that analyzes a bond portfolio and makes sell/hold decisions based on risk analysis and live web search data.The agent runs four steps: load portfolio...
View ArticleMistral OCR + Sparrow: Document to JSON
Integrated Mistral OCR as a new cloud inference backend into Sparrow, an open-source document extraction platform. This gives Sparrow a full cloud option alongside its existing local backends (MLX,...
View ArticleSparrow 0.6.0: New Production-Ready UI for Local Document AI
Sparrow just got a complete UI overhaul — rebuilt from the ground up with Next.js and shadcn for a production-grade experience.What's new in this release:- Faster document upload and extraction...
View ArticleGemma 4 12B vs Ministral 14B: Who Wins at Structured Table Extraction?
Head-to-head test: Gemma 4 12B vs Ministral 14B on structured table extraction.In this video, I run a head-to-head test: Gemma 4 12B (8-bit and bf16) vs Ministral 14B (8-bit), extracting data from a...
View ArticleBuilding Agentic AI Pipelines for Document Analysis
In this video, I show how to build a local agentic AI pipeline using Sparrow to extract and analyze data from financial documents. The agent runs two steps: - Extract structured data from a bonds...
View ArticleInstruction-Based Data Analysis with Sparrow and Local LLM
In this video, I show how to use Sparrow instruction processing pipeline to analyze a bond portfolio JSON extracted from a financial document — all running locally, no external APIs.I run three...
View ArticleSmart Document Extraction with Business Rules — Gemma vs Qwen vs Ministral
In this video I show how Sparrow hints work — a powerful feature that goes beyond simple field extraction. Using a bank bonds portfolio document, I demonstrate how to define business rules directly in...
View ArticleLarge Table Extraction to JSON with dots.ocr — No Vision LLM Hallucinations
Sparrow now supports a dedicated table mode for extracting large, complex tables into structured JSON — without Vision LLM hallucinations. Vision LLMs struggle with dense tabular data: they hallucinate...
View ArticleMoE vs Dense Models for Structured Data Extraction — Who Wins?
MoE or Dense — which model architecture wins for structured data extraction from documents? It depends on document complexity. In this video, I test MoE vs Dense models on real extraction tasks and...
View ArticleGemma 4 for Structured Data Extraction: Can It Beat Qwen 3.5?
In this video, I put Gemma 4 to the test on a real-world task — extracting structured data from bank statements — and benchmark it head-to-head against Mistral's Ministral and Qwen 3.5.I run both the...
View ArticleRunning Multiple Models on One GPU with vLLM and GPU Memory Utilization
In this video I show how to run multiple vLLM model instances on the same GPU (Nvidia) in parallel by adjusting the --gpu-memory-utilization flag.You'll see: - How to launch separate vLLM servers for...
View ArticleHow to Cache vLLM Model in FastAPI for Faster Inference
I show you how to keep your vLLM model loaded in FastAPI cache for much faster inference — without reloading it on every request.
View ArticleQwen 3.5 Test for JSON Structured Data Extraction
Quick test of the new Qwen 3.5 models on JSON structured data extraction from images. Testing and comparing results for 9B FP16, 27B Q8, and A3B 35B Q8. The 35B Q8 model wins in terms of both speed and...
View ArticleFast Large Table Extraction: Sparrow + dots.ocr to JSON
Sparrow provides table processing mode. It is optimized to handle large tables, it comes with separate template script (new templates can be easily added) to process dots.ocr markdown output into...
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