gptqlora
lmql
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| gptqlora | lmql | |
|---|---|---|
| 2 | 30 | |
| 94 | 3,320 | |
| - | 7.1% | |
| 7.6 | 9.5 | |
| 11 months ago | about 1 month ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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gptqlora
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(2/2) May 2023
GPTQLoRA: Efficient Finetuning of Quantized LLMs with GPTQ (https://github.com/qwopqwop200/gptqlora/tree/main)
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GPTQLoRA: Efficient Finetuning of Quantized LLMs with GPTQ
The difference from QLoRA is that GPTQ is used instead of NF4 (Normal Float4) + DQ (Double Quantization) for model quantization. The advantage is that you can expect better performance because it provides better quantization than conventional bitsandbytes. The downside is that it is a one-shot quantization methodology, so it is more inconvenient than bitsandbytes, and unlike bitsandbytes, it is not universal. I'm still experimenting, but it seems to work. At least, I hope it can be more options for people using LoRA. https://github.com/qwopqwop200/gptqlora/tree/main
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?
tree-of-thoughts - Plug in and Play Implementation of Tree of Thoughts: Deliberate Problem Solving with Large Language Models that Elevates Model Reasoning by atleast 70%
guidance - A guidance language for controlling large language models.
GirlfriendGPT - Girlfriend GPT is a Python project to build your own AI girlfriend using ChatGPT4.0
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
chathub - All-in-one chatbot client
simpleaichat - Python package for easily interfacing with chat apps, with robust features and minimal code complexity.
chain-of-thought-hub - Benchmarking large language models' complex reasoning ability with chain-of-thought prompting
NeMo-Guardrails - NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.
guardrails - Adding guardrails to large language models.
gorilla - Gorilla: An API store for LLMs
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.