research/README.md

Research

Experimental code for fine-tuning and agentic benchmarks. Nothing here is wired into the Gradio Lesson Agent by default — use it to train models and score checkpoints against public benchmarks.

Path	Purpose
finetune.py	LoRA / QLoRA / full fine-tune on chat or instruction data
evals/	SLM agentic benchmark suite — BFCL, τ-bench, GAIA, SWE-bench (uv package slm-evals)
data/	Shared JSONL datasets for finetune and evals

Quick links

• USAGE.md — install groups, commands, and typical workflows
• docs/overview.md — how the pieces fit together
• evals/USAGE.md — benchmark CLI, configs, and results
• evals/docs/benchmarks.md — what each benchmark measures

Install (from repo root)

# All research tooling
uv sync --group finetune --group evals --group lm-eval

Individual groups:

Group	Command	Enables
finetune	uv sync --group finetune	research/finetune.py (LoRA, QLoRA, merge)
evals	uv sync --group evals	research/evals/ package (slm-benchmark)
lm-eval	uv sync --group lm-eval	slm-lm-eval CLI (GSM8K, ARC, HellaSwag, …)

Typical workflow

research/data/education-lesson-chat.jsonl
        │
        ▼
  research/finetune.py  ──►  models/finetuned/<preset>-lora/
        │
        └──► research/evals/  (BFCL, τ-bench, GAIA, SWE-bench, lm-eval)

See USAGE.md for copy-paste commands.