stanford_alpaca
askai
stanford_alpaca | askai | |
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108 | 1,755 | |
28,856 | 86 | |
0.9% | - | |
2.0 | 10.0 | |
about 2 months ago | over 1 year ago | |
Python | TypeScript | |
Apache License 2.0 | - |
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stanford_alpaca
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How Open is Generative AI? Part 2
Alpaca is an instruction-oriented LLM derived from LLaMA, enhanced by Stanford researchers with a dataset of 52,000 examples of following instructions, sourced from OpenAI’s InstructGPT through the self-instruct method. The extensive self-instruct dataset, details of data generation, and the model refinement code were publicly disclosed. This model complies with the licensing requirements of its base model. Due to the utilization of InstructGPT for data generation, it also adheres to OpenAI’s usage terms, which prohibit the creation of models competing with OpenAI. This illustrates how dataset restrictions can indirectly affect the resulting fine-tuned model.
- Ask HN: AI/ML papers to catch up with current state of AI?
- OpenAI board in discussions with Sam Altman to return as CEO
- Are there any AI like ChatGPT without content restrictions?
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Fine-tuning LLMs with LoRA: A Gentle Introduction
In this article, we're going to experiment with LoRA and fine-tune Llama Alpaca using commercial hardware.
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Creating a new Finetuned model
Most papers I did read showed at least a thousand, even 10000 at several cases, so I assumed that to be the trend in the case of Low rank adapter(PEFT) training.(source: [2305.14314] QLoRA: Efficient Finetuning of Quantized LLMs (arxiv.org) , Stanford CRFM (Alpaca) and the minimum being openchat/openchat · Hugging Face ; There are a lot more examples)
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Shock tick up for wage growth to 7.3% in blow for Bank of England
I'm not talking about OpenAI ChatGPT I'm talking about things ALPACA, and where did they train these models? Off the existing models for a fraction of a fraction of a fraction of the cost: https://crfm.stanford.edu/2023/03/13/alpaca.html
- Bye bye Bing
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The idea maze for AI startups (2015)
I think there's a new approach for “How do you get the data?” that wasn't available when this article was written in 2015. The new text and image generative models can now be used to synthesize training datasets.
I was working on an typing autocorrect project and needed a corpus of "text messages". Most of the traditional NLP corpuses like those available through NLTK [0] aren't suitable. But it was easy to script ChatGPT to generate thousands of believable text messages by throwing random topics at it.
Similarly, you can synthesize a training dataset by giving GPT the outputs/labels and asking it to generate a variety of inputs. For sentiment analysis... "Give me 1000 negative movie reviews" and "Now give me 1000 positive movie reviews".
The Alpaca folks used GPT-3 to generate high-quality instruction-following datasets [1] based on a small set of human samples.
Etc.
[0] https://www.nltk.org/nltk_data/
[1] https://crfm.stanford.edu/2023/03/13/alpaca.html
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Repos and tutorials for a full finetune (not LoRA)
AFAIK, the original alpaca repo was a full finetune. https://github.com/tatsu-lab/stanford_alpaca
askai
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Website Optimization Using Strapi, Astro.js and OpenAI
We'll use several interesting technologies to achieve this: Strapi CMS to take care of the content management and backend, Astro which is a great new technology for quickly creating blazing fast frontend apps, and ChatGPT to provide the article summaries.
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OpenAI Bought Chatgpt.com
I was confused why https://chat.openai.com suddenly redirects to https://chatgpt.com which results in connection refused. Turns out chatgpt.com is in many blocklists (e.g. Pi-Hole) due to it being a potentially unsafe Domain before OpenAI acquiring it. So heads up if you use Pi-Hole / AdGuard etc.!
- The ChatGPT URL have changed
- Chat.openai.com now redirects (me) to chatgpt.com
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Unofficial ChatGPT API
This API allows you to interact with ChatGPT programmatically, and I've built some cool agents on top of it. Check out the code and let me know what you think! :
ChatGPT unofficial API :
This project is a Node.js application that interacts with the ChatGPT conversational AI model using Puppeteer, a Node.js library for automating web browsers.
Files chatgptv1.js: This file contains the main logic for the ChatGPT bot, including methods for initializing the browser, sending messages, receiving replies, and handling errors.
bart.js: This file contains a function that uses the Cloudflare API to summarize the conversation history when an error occurs, in order to resume the conversation.
twochatbotsconv.js: This file is simple use of the API , which creates two instances of the ChatGPT class, initiates a conversation between them, and saves the conversation history to a file.
.env: This file contains the API token for the Cloudflare API, which is used in the bart.js file.
Dependencies :
puppeteer: A Node.js library for automating web browsers. fs: The built-in file system module in Node.js. winston: A logging library for Node.js. crypto: The built-in cryptography module in Node.js. axios: A popular HTTP client library for Node.js. dotenv: A zero-dependency module that loads environment variables from a .env file.
Usage:
Install the dependencies by running npm install in your project directory. Create a .env file in the project directory and add your Cloudflare API token:
API_TOKEN=YourfreeCloudFlareAPIToken In your code, create a new instance of the ChatGPT class and use the sendMessage and getReply methods to interact with the ChatGPT model:
const ChatGPT = require('./chatgptv1');
const chatgpt = new ChatGPT(); await chatgpt.initializeBrowser();
await chatgpt.sendMessage('Hello, ChatGPT!'); const reply = await chatgpt.getReply(); console.log(reply);
await chatgpt.closeBrowser(); If an error occurs during the conversation, the handleError method will attempt to save the conversation history and resume the conversation using the summarized context.
Before Running :
run Google chrom in the debug mode using 9220 port , run : google-chrome-stable --remote-debugging-port=9222
Customization :
You can customize the behavior of the ChatGPT bot by passing options to the ChatGPT constructor:
chatbotUrl: The URL of the ChatGPT interface (default: 'https://chat.openai.com/'). headless: Whether to run the browser in headless mode (default: false). saveConversationCallback: A callback function that will be called with the conversation summary and the conversation file name when an error occurs.
License:
This project is licensed under the MIT License.
- It's a shame – chat.openai.com redirect to chatgpt.com is broken
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Building a Basic Forex Rate Assistant Using Agents for Amazon Bedrock
After wrestling with it for a bit and eventually giving up, I instead turned to ChatGPT to see if it is smart enough for the task. With my free plan, I asked ChatGPT 3.5 the following:
- Learn to ask for help
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How to build a custom GPT: Step-by-step tutorial
Go to chat.openai.com and log in
- Chat.openai.com no longer requires login
What are some alternatives?
alpaca-lora - Instruct-tune LLaMA on consumer hardware
ChatGPT - 🔮 ChatGPT Desktop Application (Mac, Windows and Linux)
ChatGLM-6B - ChatGLM-6B: An Open Bilingual Dialogue Language Model | 开源双语对话语言模型
gpt-4chan-model
Open-Assistant - OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.
openai-cookbook - Examples and guides for using the OpenAI API
llama.cpp - LLM inference in C/C++
ai-cli - Get answers for CLI commands from ChatGPT right from your terminal
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
KoboldAI-Client
Alpaca-Turbo - Web UI to run alpaca model locally
civitai - A repository of models, textual inversions, and more