stanford_alpaca
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stanford_alpaca | askai | |
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108 | 1,747 | |
28,761 | 86 | |
1.3% | - | |
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
- 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
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Integrating Strapi with ChatGPT and Next.js
In this tutorial, we will learn how to use Strapi, ChatGPT, and Next.js to build an app that displays recipes using AI.
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GPT-4 Turbo with Vision is a step backwards for coding
Maybe I am bit dim, but how one can choose GPT-4 Turbo? Is this available from https://chat.openai.com/ ?
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AI Developer Tool Limitations In 2024
With the rise of ChatGPT, Bard Gemini, GitHub Copilot, Devin, and other AI tools1, developers started to fear that AI tooling would replace them. Even though their capabilities are indeed impressive, I don't fear our jobs will go away in 2024.
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Data-driven customer acquisition: Machine Learning applied to Customer Lifetime Value
To illustrate the core concepts of ML and regression analysis, we’ll start with a simple model. ChatGPT (the free version) creates something that works with this prompt:
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From 12th Final Project to an ATM Management System: Leveraging ChatGPT 4 for PDF Analysis
Fast forward to my college years. I found myself at IIIT Delhi, a prestigious tier 1 computer science engineering college. Around the same time, ChatGPT emerged, shaking the world more vigorously than COVID-19. As fate would have it, I gained temporary access to ChatGPT 4 which runs on GPT 4, and curiosity piqued my interest.
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📊 Obsidian: Nutrition
It is worth mentioning that for my use case, I do not require a high level of precision, so I obtain the values with an AI. I describe the recipe and portions to ChatGPT, and it provides me with a very good estimate of the nutritional information of the meal.
- Exploring the Frontiers of AI: An In-Depth Look at ChatGPT-4
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
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.