FinGPT
prompt-engineering
| FinGPT | prompt-engineering | |
|---|---|---|
| 11 | 18 | |
| 11,586 | 8,016 | |
| 3.4% | 2.7% | |
| 9.5 | 5.1 | |
| 20 days ago | 7 months ago | |
| Jupyter Notebook | ||
| MIT License | MIT License |
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FinGPT
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GPT-4, without specialized training, beat a GPT-3.5 class model that cost $10B
There is also the open source FinGPT, that is claimed to beat GPT4 in some benchmarks at a fine tuning cost of $17.25.
https://github.com/AI4Finance-Foundation/FinGPT
- FLaNK Stack 05 Feb 2024
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Ask HN: How to Get into Quantitative Trading?
https://github.com/AI4Finance-Foundation/FinGPT
[1] Zipline is a Pythonic algorithmic trading library
- FinGPT
- FLaNK Stack Weekly for 20 June 2023
- GPT but for the Finance Industry
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FinGPT: Open-Source Financial Large Language Models
Code: https://github.com/AI4Finance-Foundation/FinGPT
- Is FinGPT Coming?
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?
modelscope - ModelScope: bring the notion of Model-as-a-Service to life.
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.
EasyCV - An all-in-one toolkit for computer vision
Prompt-Engineering-Guide - 🐙 Guides, papers, lecture, notebooks and resources for prompt engineering
keep - The open-source alert management and AIOps platform
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%
StanfordQuadruped
chathub - All-in-one chatbot client
alpaca_eval - An automatic evaluator for instruction-following language models. Human-validated, high-quality, cheap, and fast.
canal - 阿里巴巴 MySQL binlog 增量订阅&消费组件