gptqlora
prompt-engineering
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| gptqlora | prompt-engineering | |
|---|---|---|
| 2 | 18 | |
| 94 | 7,932 | |
| - | 2.8% | |
| 7.6 | 5.1 | |
| 11 months ago | 6 months ago | |
| Python | ||
| MIT License | MIT License |
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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
prompt-engineering
- Ask HN: Any good collection of writing prompts for GPT 3.5/4?
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Show HN: LLM Agent Paper List
An agent is a style of prompt that lets LLMs act as reasoning engines. It's also known as the ReAct pattern (which engineers are avoiding using for namespace collision reasions).
You can read a good intro example here: https://github.com/brexhq/prompt-engineering#react
- FLaNK Stack Weekly for 20 June 2023
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What are your long-term career goals?
Well, if developers get replaced by AI, then who are the managers going to manage :). I personally don't think AI is just going to replace us. The way we work will continue to change as new AI tools come out. I'm taking time to tinker with new tools and seeing how others do as well (e.g., I found Brex's tips and tricks for working with LLMs very insightful: https://github.com/brexhq/prompt-engineering).
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A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT
I recognize there's plenty of catnip here when it comes to calling this "engineering" or not, however, whatever you want to call it (prompt fiddling?), the techniques are crucial if you want to achieve reasonably consistent output from current-state LLMs. As models improve concerns about context window limitations will be reduced and it will be easier to discern user intent.
These are good straight-to-the-point guides:
- Prompt Engineering by BrexHQ: https://github.com/brexhq/prompt-engineering
- OpenAI guidance: https://help.openai.com/en/articles/6654000-best-practices-f...
- https://devblogs.microsoft.com/dotnet/gpt-prompt-engineering...
- (great examples): https://www.deeplearning.ai/short-courses/chatgpt-prompt-eng...
tl;dr:
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(2/2) May 2023
Brex's Prompt Engineering Guide (https://github.com/brexhq/prompt-engineering)
- GitHub - brexhq/prompt-engineering: Tips and tricks for working with Large Language Models like OpenAI's GPT-4.
- Brex’s Prompt Engineering Guide
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%
promptfoo - Test your prompts, models, and RAGs. Catch regressions and improve prompt quality. LLM evals for OpenAI, Azure, Anthropic, Gemini, Mistral, Llama, Bedrock, Ollama, and other local & private models with CI/CD integration.
GirlfriendGPT - Girlfriend GPT is a Python project to build your own AI girlfriend using ChatGPT4.0
Prompt-Engineering-Guide - 🐙 Guides, papers, lecture, notebooks and resources for prompt engineering
chathub - All-in-one chatbot client
FinGPT - FinGPT: Open-Source Financial Large Language Models! Revolutionize 🔥 We release the trained model on HuggingFace.
chain-of-thought-hub - Benchmarking large language models' complex reasoning ability with chain-of-thought prompting
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
gorilla - Gorilla: An API store for LLMs
canal - 阿里巴巴 MySQL binlog 增量订阅&消费组件