torch-grammar
lmql
| torch-grammar | lmql | |
|---|---|---|
| 3 | 30 | |
| 63 | 3,360 | |
| - | 4.0% | |
| 5.2 | 9.5 | |
| 11 days ago | 8 days ago | |
| Python | Python | |
| - | Apache License 2.0 |
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torch-grammar
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Show HN: LLMs can generate valid JSON 100% of the time
Yes! This is closer to the approach I took in my port of llama.cpp's grammar support to PyTorch: https://github.com/Shopify/torch-grammar/blob/main/torch_gra... ... it generates an tensor mapping each PDA stack to a map of which tokens are acceptable from that state. It seems like a much better way to do it.
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Llama: Add Grammar-Based Sampling
I implemented this for PyTorch too at https://github.com/Shopify/torch-grammar
lmql
- Show HN: Fructose, LLM calls as strongly typed functions
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Prompting LLMs to constrain output
have been experimenting with guidance and lmql. a bit too early to give any well formed opinions but really do like the idea of constraining llm output.
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[D] Prompt Engineering Seems Like Guesswork - How To Evaluate LLM Application Properly?
the only time i've ever felt like it was anything other than guesswork was using LMQL . not coincidentally, LMQL works with LLMs as autocomplete engines rather than q&a ones.
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Guidance for selecting a function-calling library?
lqml
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Show HN: Magentic – Use LLMs as simple Python functions
This is also similar in spirit to LMQL
https://github.com/eth-sri/lmql
- Show HN: LLMs can generate valid JSON 100% of the time
- LangChain Agent Simulation – Multi-Player Dungeons and Dragons
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The Problem with LangChain
LLM calls are just function calls, so most functional composition is already afforded by any general-purpose language out there. If you need fancy stuff, use something like Python‘s functools.
Working on https://github.com/eth-sri/lmql (shameless plug, sorry), we have always found that compositional abstractions on top of LMQL are mostly there already, once you internalize prompts being functions.
- Is there a UI that can limit LLM tokens to a preset list?
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Local LLMs: After Novelty Wanes
LMQL is another.
What are some alternatives?
outlines - Structured Text Generation
guidance - A guidance language for controlling large language models.
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
Constrained-Text-Generation-Studio - Code repo for "Most Language Models can be Poets too: An AI Writing Assistant and Constrained Text Generation Studio" at the (CAI2) workshop, jointly held at (COLING 2022)
simpleaichat - Python package for easily interfacing with chat apps, with robust features and minimal code complexity.
Constrained-Text-Genera
NeMo-Guardrails - NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.
json-schema-spec - The JSON Schema specification
guardrails - Adding guardrails to large language models.
relm - ReLM is a Regular Expression engine for Language Models
basaran - Basaran is an open-source alternative to the OpenAI text completion API. It provides a compatible streaming API for your Hugging Face Transformers-based text generation models.