SpeedyLabsAI/Primus-FinAgent

Primus-FinAgent Primus-FinAgent is a 4B-parameter agentic model for financial analysis over SEC 10-K filings. Given a question about a company's filing, it discovers the relevant tables, inspects their schemas, writes SQL, and computes the answer — using tools, not memorized facts.

What it does It runs a multi-turn ReAct loop (up to 20 turns) with native function calling over four tools:

Tool Purpose get_table_names list available tables for a company get_table_info inspect a table's columns, dtypes, sample values sql_query run a filtered SQL query (no SELECT *) calculator evaluate a numeric expression It concludes with a FINAL ANSWER: block in the requested unit (ratio, %, $, etc.). The hallmark behavior: it discovers schemas instead of guessing, reads tool errors and self-corrects, and cites the figures it used.

Intended use Agentic question-answering over structured financial tables (10-K / SEC-style data). Research and reproduction of small-specialist-vs-generalist tool-use findings. Not intended for: financial advice, unverified production decisions, or use outside an agent scaffold with the four tools below.

Training Base: Qwen/Qwen3-4B-Instruct-2507 Algorithm: GRPO (Group Relative Policy Optimization), full-parameter, FSDP2 + optimizer offload Reward: binary correctness from an LLM judge (gpt-5-nano) + a small bonus for inspecting the correct tables Rollouts: vLLM, multi-turn tool-calling ReAct over an in-memory SQLite of ~6,900 SEC tables (207 companies) Hardware: 8× A100-80GB, single node Data: single-table FinQA QA pairs