constrained-decoding

Query language for blending SQL and local language models across structured + unstructured data, with type constraints.

Reproduction Package for the paper "Type-Constrained Code Generation with Language Models" [PLDI 2025]

Constrained Decoding of Diffusion LLMs with Context-Free Grammars.

For our ICRA 2025 paper 🏆 "SELP: Generating Safe and Efficient Task Plans for Robot Agents with Large Language Models" by Yi Wu, Zikang Xiong, Yiran Hu, Shreyash Iyengar, Nan Jiang, Aniket Bera, Lin Tan, and Suresh Jagannathan. (🏆 Best Paper Award Finalist!)

📐 Just-in-time logic enforcement for network data generation with LLMs

A guide to structured generation using constrained decoding

Code for paper "Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text Generation" EMNLP 2021 and "Constrained Abstractive Summarization: Preserving Factual Consistency with Constrained Generation" arXiv 2020

Constraint-driven code generation that transforms AI from probabilistic guessing into controlled search through valid program spaces.

[Pytorch] Efficient tokenization for recommendations and generative retrieval. Inspired by STATIC decoding from "Vectorizing the Trie"

Speculative grammar backtracking algorithm for LLM decoding conforming to some lark context-free grammar (CFG)

Single-binary GGUF model runtime in C — CPU/CUDA/Metal, OpenAI-compatible. Serves, scores, and trains LoRA directly through the quantized weights it deploys, with byte-reproducible adapters. Tool calls survive the token limit; sparse MoE and schema-constrained decoding included.

PEG grammars as LLM output constraints. TypeScript-native parser generator with parametric rules.

🧠 Visualize token-by-token sampling with chat templates, nucleus filtering, constrained decoding, and attribution heatmaps in one local app ⚡

Context-Free Grammar-guided Generation of FHIR Resources Using Large Language Models (MIE 2026)

Deterministic, integer-only SLM inference runtime — no floating point on the inference path, so the same model and prompt produce bit-identical output across CPUs, GPUs, compilers, and vendors.

🤖 AI-powered function calling engine using a LLM to convert natural language prompts into validated JSON function calls.

A function calling tool that translates natural language prompts into structured function calls using a small LLM and constrained decoding

VibeDrift - Run any LLM on your own hardware. Bypass the VRAM wall with CPU/RAM inference, MOE expert offloading, and 4-bit quantization. No Cloud, no Subscription.

Convert a regular expression into a GBNF grammar for constrained decoding with llama.cpp and local LLMs. Zero dependencies.

Reproduction & re-implementation of the AloLab paper (arXiv:2605.02363): closing the structured-output reliability gap in small LLMs via iterative black-box prompt optimization. GSM8K, 4 models, ablations, McNemar significance.