magma-chat
lmql
| magma-chat | lmql | |
|---|---|---|
| 4 | 30 | |
| 206 | 3,360 | |
| 4.9% | 4.0% | |
| 8.4 | 9.5 | |
| 9 months ago | 5 days ago | |
| Ruby | Python | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
magma-chat
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Run and create custom ChatGPT-like bots with OpenChat
This feels similar to MagmaChat [1] which I open sourced about a month ago. Except mine is in Ruby on Rails.
[1] https://magmachat.ai
- MagmaChat v0.1.0 Released – Rails 7-based ChatGPT bot platform
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Google “We Have No Moat, and Neither Does OpenAI”
If you're technical just get yourself OpenAI API access which is super cheap and hook it up to your own self-hosted ChatGPT clone like https://github.com/magma-labs/magma-chat
The wait for GPT-4 is not as long as it used to be, and when you're using the API directly there's no censorship.
- Show HN: Open-Source ChatGPT Bot Platform Written in Ruby on Rails 7
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.
IF
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
empirical-philosophy - A collection of empirical experiments using large language models and other neural network architectures to test the usefulness of metaphysical constructs.
simpleaichat - Python package for easily interfacing with chat apps, with robust features and minimal code complexity.
convostack - Plug and play embeddable AI chatbot widget and backend deployment framework
NeMo-Guardrails - NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.
gpt-jargon - Jargon is a natural language programming language specified and executed by LLMs like GPT-4.
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.