Overview

mashriram/Qwen3-4B-Instruct-TableLLM-SFT is a 4 billion parameter instruction-tuned language model, built upon the Qwen3-4B-Instruct-2507 base model. Developed by mashriram, this model is uniquely specialized for tasks requiring table-based reasoning and understanding.

Key Capabilities

• Table Data Processing: Highly proficient in interpreting and extracting information from structured table data.
• Instruction Following: Designed to accurately follow instructions for table-related queries.
• Multilingual Support: Capable of handling table-based tasks in English, Spanish, Hindi, French, and German.
• Extended Context: Features a substantial context window of 40960 tokens, allowing for the processing of large tables or multiple related tables.

Good For

• Data Extraction from Tables: Automating the retrieval of specific information from tabular datasets.
• Table Question Answering: Answering complex questions where the answer is derived from table content.
• Structured Data Analysis: Assisting in the interpretation and summarization of data presented in tables.
• Multilingual Table Processing: Applications requiring table understanding across its supported languages.