gpt-json
openai-cookbook
gpt-json | openai-cookbook | |
---|---|---|
7 | 216 | |
726 | 56,195 | |
- | 1.5% | |
6.8 | 9.5 | |
about 1 month ago | 3 days ago | |
Python | MDX | |
MIT License | MIT License |
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gpt-json
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Structured Output from LLMs (Without Reprompting!)
I did a POC project with it recently. The guidance on gpt-3.5-turbo and gpt-4 models isn't as functional as plain gpt-3. I found I had better results using https://github.com/piercefreeman/gpt-json and it doesn't require multiple calls to the API. Not as feature filled, but it may meet your needs
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This week's top indie A.I projects, launches and resources
Gpt-json: Structured and typehinted GPT responses in Python
- GitHub - piercefreeman/gpt-json: Structured and typehinted GPT responses in Python
- Show HN: GPT-JSON – Structured and typehinted GPT responses in Python
- GPT-JSON: Structured and typehinted GPT responses in Python
openai-cookbook
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Question-Answer System Architectures using LLMs
A pretrained LLM is a closed-book system: It can only access information that it was trained on. With domain fine-tuning, the system manifests additional material. An early prototype of this technique was shown in this OpenAi cookbook: For the target domain, text was embedded using an API, and then when using the LLM, embeddings were retrieved using semantic similarity search to formulate an answer. Although this approach evolved to retrieval-augmented generation, its still a technique to adapt a Gen2 (2020) or Gen3 (2022) LLM into a question-answering system.
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Ask HN: High quality Python scripts or small libraries to learn from
https://github.com/openai/openai-cookbook/blob/main/examples...
- Collection of notebooks showcasing some fun and effective ways of using Claude
- OpenAI Cookbook: Techniques to improve reliability
- OpenAI Cookbooks
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How to fine tune vit/convnet to focus on the layout of the input room image and ignore other things ?
It sounds like you are trying to tweak embeddings for similarity search. Rather than fine-tune the model's layers, you may want to try training a linear transformation the existing model's output embedding. Openai has a cookbook on how to do that. You will need some data though - but I think you can try it with ~20 pieces of synthetically generated data.
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Best base model 1B or 7B for full finetuning
tutorial from OpenAI https://github.com/openai/openai-cookbook/blob/main/examples/Question_answering_using_embeddings.ipynb
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Resources to learn ChatGPT and the OpenAI API
OpenAI Cookbook
- OpenAI Cookbook
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Another Major Outage Across ChatGPT and API
OpenAI community repo with lots of examples: https://github.com/openai/openai-cookbook
What are some alternatives?
zod-chatgpt
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
jsonformer - A Bulletproof Way to Generate Structured JSON from Language Models
gpt4-pdf-chatbot-langchain - GPT4 & LangChain Chatbot for large PDF docs
emdash - 📚🧙♂️ Wisdom indexer — use AI to organize text snippets so you can actually remember & learn from what you read
chatgpt-retrieval-plugin - The ChatGPT Retrieval Plugin lets you easily find personal or work documents by asking questions in natural language.
evadb - Database system for AI-powered apps
askai - Command Line Interface for OpenAi ChatGPT
struct-gpt - get structured output from LLM's
gpt_index - LlamaIndex (GPT Index) is a project that provides a central interface to connect your LLM's with external data. [Moved to: https://github.com/jerryjliu/llama_index]
open_llama - OpenLLaMA, a permissively licensed open source reproduction of Meta AI’s LLaMA 7B trained on the RedPajama dataset
txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows