telegram-chatgpt-bot
openai-cookbook
| telegram-chatgpt-bot | openai-cookbook | |
|---|---|---|
| 4 | 216 | |
| 32 | 56,794 | |
| - | 2.5% | |
| 6.4 | 9.4 | |
| about 1 year ago | 2 days ago | |
| TypeScript | MDX | |
| - | MIT License |
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telegram-chatgpt-bot
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There is a Node.js Chat GPT Telegram bot that is capable of understanding both text and voice messages.
There are at least a few of these out there: https://github.com/danneu/telegram-chatgpt-bot
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Personal Concierge Using OpenAI's ChatGPT via Telegram and Voice Messages
This is the new hello world.
https://github.com/danneu/telegram-chatgpt-bot
I tried building this for WhatsApp but Twilio is weirdly expensive.
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OpenAI Tokenizer
Of course, you can find BPE tokenizer impls in other languages for the same results as long as they use the "cl100k_base" data.
Typescript: https://github.com/danneu/telegram-chatgpt-bot/tree/master/t...
- Show HN: SlickGPT
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?
slickgpt - SlickGPT is a light-weight "use-your-own-API-key" web client for the OpenAI API written in Svelte. It offers GPT-4 integration, a userless share feature and other superpowers.
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
chatgpt-ui - ChatGPT UI with auth, OpenAI, Claude, Gemini support, written in Elixir + LiveView
gpt4-pdf-chatbot-langchain - GPT4 & LangChain Chatbot for large PDF docs
chatgpt-web - ChatGPT web interface using the OpenAI API
chatgpt-retrieval-plugin - The ChatGPT Retrieval Plugin lets you easily find personal or work documents by asking questions in natural language.
chat-gpt-telegram-bot - There is a Chat GPT Telegram bot that is capable of understanding both text and voice messages.
askai - Command Line Interface for OpenAi ChatGPT
plurality - A cornucopia of open source UIs built with ChatGPT API.
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]
buddy
txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows