
Andrej Baranovskij
@andrejusb • 6,686 subscribers
Sparrow Creator: Open-Source AI Doc Extraction 🚀 | ML/Oracle Dev | @katana_ml | Try: https://t.co/V0h9FMJzKb | https://t.co/nRgXgLL0mO
Videos

Transforming Invoice Data into JSON: Local LLM with LlamaIndex & Pydantic 🚀 Complete video: Code: I explain how to get structured JSON output with LlamaIndex and dynamic Pydantic class. This helps to implement the use case of data extraction from invoice documents. The solution runs on the local machine, thanks to Ollama. I'm using a MacBook Air M1 with 8GB RAM. LlamaIndex 🦙 Pydantic ollama #Python #LLM #RAG
Andrej Baranovskij147,949 次观看 • 2 年前

Effective Table Data Extraction from PDF without LLM Sparrow Parse helps to read tabular data from PDFs, relying on various libraries, such as Unstructured or PyMuPDF4LLM. This allows us to avoid data hallucination errors often produced by LLMs when processing complex data structures. Learn more: ✅ ✅ Katana
Andrej Baranovskij27,886 次观看 • 2 年前

Document Querying with Qwen2-VL-7B and JSON Output Complete video: I demonstrate how to perform document queries using Qwen2-VL-7B. By simplifying field names, we streamline the prompts, making them more efficient and reusable across different documents. This approach is similar to running SQL queries on a database, but tailored for language models like Qwen2-VL-7B, with results returned in JSON format. Qwen #MultimodalLLM #OCR
Andrej Baranovskij15,976 次观看 • 1 年前

Running Qwen2 VL 72B 4bit 🇨🇳 quantized model on Mac Mini M4 Pro 64GB. It works very well and delivers good accuracy, suitable for practical use cases. Integrating MLX and MLX-VLM backend into Sparrow as main solution for local inference and data extraction. Kudos to Prince Canuma for porting Qwen2 VL to MLX.
Andrej Baranovskij13,742 次观看 • 1 年前
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