Projects
Everything here is public. Models on Hugging Face, code on GitHub. Download counts are read from the API when this page is built, not typed in by hand.
A 7B that ties a 1.6T model at text-to-SQL
Post-training finetuning-text-to-sql
Qwen2.5-Coder-7B, post-trained and benchmarked on BIRD dev (1,534 questions). Accuracy means result accuracy: every generated query is executed against the real database and the result sets are compared. No fuzzy string matching, no exact-match SQL scoring, because both of those lie.
| Model | Params | Accuracy |
|---|---|---|
| GLM 5.2 | 744B | 63.0% |
| DeepSeek V4-Pro | 1.6T | 58.7% |
| This work, 7B + Best-of-N | 7B | 58.5% |
A 7B ties a 1.6T model at roughly 0.4% of the parameters, and runs locally. GLM 5.2 still wins outright. This is a parameter-efficiency result, not a superiority one. The two things that moved the needle were distilling the teacher's reasoning rather than its SQL (+5.2pp, and it generalized to unseen databases) and execution-based majority voting at inference (+6.4pp, no retraining).
Open models & datasets
Local 5.2k downloads all time.
| Artifact | Type | All time | 30 days |
|---|---|---|---|
| qwen2.5-coder-7b-bird-cot | Model · reasoning-distilled text-to-SQL | 3.8k | 173 |
| …-GGUF | Model · quantized for local inference | 631 | 122 |
| bird-cot-sft | Dataset · CoT traces from Qwen3-Coder-480B | 134 | 26 |
| spider-dpo-1040 | Dataset · 1,040 execution-verified preference pairs | 652 | 235 |
| LocalSQL | Space · ask a question, get SQL, run it live |
All time is cumulative; 30 days is a moving window that decays after a spike. Shown separately because quoting one as the other is the easiest way to be wrong by an order of magnitude.
Detonating dependencies before your agent installs them
Agents package-quarantine
Supply-chain attacks like the LiteLLM and ctx PyPI hijacks
fire at install time, so static CVE scanners miss them entirely.
This intercepts pip and npm, runs the package in
a Docker honeypot seeded with canary credentials, and watches at the OS
boundary with strace, because postinstall
hooks spawn child processes that in-process monkey-patching never sees.
Research
Research Automatic Highlight Generation, IEEE ICAD 2026. paper repo. A ResNet3D classifier for goal detection in football footage at 82% accuracy and 88% recall; a sliding-window pipeline condenses a 90-minute match in under 15 minutes while capturing 95% of key events.
Also
Learning repos:
DPO from first principles
(six notebooks, y = 3x + 2 up to DPO on real preference
pairs) and
GRPO with the SQL executor as the reward function,
plus its RL environment.
MedSignal runs
clinical intelligence entirely on-device.
Drift Sentinel
catches schema changes that degrade ML models without breaking any
pipeline.
SplitMate
has split $23K+ across 182+ receipts for 22+ people.