markprompt
openai-cookbook
| markprompt | openai-cookbook | |
|---|---|---|
| 9 | 215 | |
| 2,188 | 56,195 | |
| - | 1.2% | |
| 10.0 | 9.5 | |
| 5 months ago | about 9 hours ago | |
| TypeScript | MDX | |
| Apache License 2.0 | MIT License |
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.
markprompt
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Announcing the Markprompt + Contenda integration: Confbrew
Register for a key on Markprompt
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Open-Source GPT-4 Platform for Markdown
I filed an issue a few hours ago. https://github.com/motifland/markprompt/issues/5 (for asciidoc)
- Markprompt - GPT-4 prompt for your Markdown docs
- Open Source GPT-4 platform for Markdown
- Markprompt: Open-Source GPT-4 for Markdown, Markdoc and MDX
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?
semantic-kernel - Integrate cutting-edge LLM technology quickly and easily into your apps
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
vscode-markdoc - VSCode extension for Markdoc syntax highlighting and language support
gpt4-pdf-chatbot-langchain - GPT4 & LangChain Chatbot for large PDF docs
txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
chatgpt-retrieval-plugin - The ChatGPT Retrieval Plugin lets you easily find personal or work documents by asking questions in natural language.
julia-sanfrancisco - A clone of the game "Where in the World is Carmen Sandiego?" largely re-created with generative AI.
askai - Command Line Interface for OpenAi ChatGPT
vectordb-benchmark
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]
knowledge_gpt - Accurate answers and instant citations for your documents.