Tiny Tool Use by Bagel Labs

Achieve tool use with open-source LLMs, made simple

44 followers

Achieve tool use with open-source LLMs, made simple

44 followers

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Tiny Tool Use is a minimal, open-source library for LLMs to make reliable, auditable tool calls. Supports SFT, DPO, and synthetic data — all driven by simple JSON config. Fast setup, strong evals, and ready for real-world prototyping.

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Launch tags:Open Source•Developer Tools•Artificial Intelligence

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👋 Hey Product Hunt! At Bagel Labs, we believe the future of advanced AI systems depends on their ability to reason with external tools, APIs, and data sources. But when we tried building with existing “tool-use” stacks, we ran into the same issues every time: brittle code, no reproducibility, and zero shared benchmarks. So we built Tiny Tool Use — a minimal, MIT-licensed open-source library that turns adapting LLMs for robust, auditable tool calls into a config-only workflow. No fragile scaffolding. No hidden state. Just one JSON file and a single CLI. 🚀 Why we built it Adapt open source LLMs in minutes, not days — from config to full fine-tune Make tool calls auditable via explicit schemas and benchmarked outputs Scale synthetic data generation via Teacher Mode — models generate, remix, and train on their own traces. Tool use is what makes models like o3 so great. 🛠️ What makes it different • Configuration-only pipelines — models, tools, datasets, hyper-params all live in JSON • One-flag training mode swap — "method": "sft" | "dpo" | "teacher_mode" • Synthetic data at scale — blend real + generated traces via "real_fraction" • Full evaluation suite — TensorBoard, Berkeley Function Calling Leaderboard (BFCL), and detailed metrics • Production-ready — LoRA adapters, GPU-agnostic trainer, model merge utilities 🔍 Evaluations that matter Every training run includes: • Tool selection accuracy • Format correctness • Execution success • Response quality And every run is logged with full provenance: config, seed, Git commit. 🏗️ What we’re proud of <400 lines of core logic — readable, hackable, extensible Full LoRA support from 0.6B to 70B+ Community contributions already landing (new tool schemas, eval reports) Seamless end-to-end: train, merge, benchmark, share Whether you're a researcher, builder, or just LLM-curious — we built Tiny Tool Use so anyone can take models from static text prediction to dynamic tool reasoning. We’re excited to launch today and can’t wait to see what you build with it. Check out the repo here! 👉 GitHub: https://github.com/bagel-org/bag...

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1yr ago

Femme Stock

@kyrannio Congratulations Kiri! So happy to get the early access.

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1yr ago

Coverr

Maker

@zeng Thanks, Zeng! We are so thrilled to have you!

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1yr ago

Smoopit

@kyrannio This is exactly what the ecosystem needed! The config-only approach is brilliant, it removes so much boilerplate complexity. Quick question, how does the Teacher Mode synthetic data generation compare to traditional fine-tuning in terms of model performance? Are you seeing better generalization?

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1yr ago