rellm
lmql
| rellm | lmql | |
|---|---|---|
| 7 | 30 | |
| 491 | 3,360 | |
| - | 4.0% | |
| 5.0 | 9.5 | |
| 9 months ago | 6 days ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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rellm
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Run and create custom ChatGPT-like bots with OpenChat
- https://github.com/r2d4/rellm
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Forcing GPT-4 or GPT-3.5-turbo to adhere to a specific output format
MS guidance as mentioned and ReLLM
- GitHub - r2d4/rellm: Exact structure out of any language model completion.
- AI Showdown: Wizard Vicuna vs. Stable Vicuna, GPT-4 as the judge (test in comments)
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ReLLM: Exact Structure for Large Language Model Completions
There's probably a better API that wraps generate, but there's a bit more work than the logit mask.
You have to go one token at a time, otherwise the masking becomes combinatoric rather than linear (two tokens at a time -- need to generate all two token pairs, etc.).
But otherwise, that's what the code does! https://github.com/r2d4/rellm/blob/main/rellm/rellm.py#L21
- r2d4/rellm: Exact structure out of any language model completion.
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?
OpenChat - LLMs custom-chatbots console ⚡
guidance - A guidance language for controlling large language models.
gpt-jargon - Jargon is a natural language programming language specified and executed by LLMs like GPT-4.
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
convostack - Plug and play embeddable AI chatbot widget and backend deployment framework
simpleaichat - Python package for easily interfacing with chat apps, with robust features and minimal code complexity.
NeMo-Guardrails - NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.
llama-api-server - A OpenAI API compatible REST server for llama.
guardrails - Adding guardrails to large 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.