stanford_alpaca
petals
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stanford_alpaca | petals | |
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108 | 98 | |
28,602 | 8,557 | |
1.4% | 2.7% | |
2.0 | 8.5 | |
17 days ago | 10 days ago | |
Python | Python | |
Apache License 2.0 | MIT License |
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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?
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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)
- 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.
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[D] High-quality, open-source implementations of LLMs
Alpaca [GitHub]
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please 0.1.0 released: let GPT-4 remember CLI args
Now if only this could be used offline, eg. with alpaca https://github.com/tatsu-lab/stanford_alpaca
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Is there a Chatgpt (or other LLMs) powered application in the field of cybersecurity/privacy for end users/b2c?
If you have a strong enough computer, there is Alpaca and llama.cpp which are both open-source. They also have the best privacy feature of all: to be able to be ran locally offline on your computer. I believe there are more foss LLMs out there too but idr.
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Does ChatGPT suck at programming for everyone or just for me?
Are you aware that you can run a pretrained LLM on just 8gb of ram with a single x86 cpu?
petals
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Mistral Large
So how long until we can do an open source Mistral Large?
We could make a start on Petals or some other open source distributed training network cluster possibly?
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Distributed Inference and Fine-Tuning of Large Language Models over the Internet
Can check out their project at https://github.com/bigscience-workshop/petals
- Make no mistake—AI is owned by Big Tech
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Run 70B LLM Inference on a Single 4GB GPU with This New Technique
There is already an implementation along the same line using the torrent architecture.
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Run LLMs in bittorrent style
Check it out at Petals.dev. Chatbot
- Mistral 7B,The complete Guide of the Best 7B model
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Run LLMs at home, BitTorrent‑style
I would be curious to have a ballpark estimate of the finetunning performance of a "private" petals cluster.
[0] https://github.com/bigscience-workshop/petals/wiki/Launch-yo...
The first question I had was "what are the economics?"
> Will Petals incentives be based on crypto, blockchain, etc.?
No, we are working on a centralized incentive system similar to the AI Horde kudos, even though Petals is a fully decentralized system in all other aspects. We do not plan to provide a service to exchange these points for money, so you should see these incentives as "game" points designed to be spent inside our system.
Petals is an ML-focused project designed for ML researchers and engineers, it does not have anything to do with finance. We decided to make the incentive system centralized because it is much easier to develop and maintain, so we can focus on developing features useful for ML researchers.
https://github.com/bigscience-workshop/petals/wiki/FAQ:-Freq...
Would love to share my 3080 Ti, but after running the commands in the getting started guide (https://github.com/bigscience-workshop/petals/wiki/Run-Petal...) it looks like there's a dependency versioning issue:
ImportError: cannot import name 'get_full_repo_name' from 'huggingface_hub' (~/.local/lib/python3.8/site-packages/huggingface_hub/__init__.py)
What are some alternatives?
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
alpaca-lora - Instruct-tune LLaMA on consumer hardware
ChatGLM-6B - ChatGLM-6B: An Open Bilingual Dialogue Language Model | 开源双语对话语言模型
Open-Assistant - OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.
llama - Inference code for Llama models
llama.cpp - LLM inference in C/C++
GLM-130B - GLM-130B: An Open Bilingual Pre-Trained Model (ICLR 2023)
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
Alpaca-Turbo - Web UI to run alpaca model locally
Auto-GPT - An experimental open-source attempt to make GPT-4 fully autonomous. [Moved to: https://github.com/Significant-Gravitas/Auto-GPT]