petals
stanford_alpaca
petals | stanford_alpaca | |
---|---|---|
98 | 108 | |
8,684 | 28,816 | |
1.5% | 0.7% | |
8.3 | 2.0 | |
5 days ago | about 2 months ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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.
petals
-
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?
[0] https://petals.dev/
-
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
- Would you donate computation and storage to help build an open source LLM?
-
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.
https://petals.dev/
-
Run LLMs in bittorrent style
Check it out at Petals.dev. Chatbot
- Is distributed computing dying, or just fading into the background?
-
Ask HN: Are there any projects currently exploring distributed AI training?
https://github.com/bigscience-workshop/petals
-
Mistral 7B,The complete Guide of the Best 7B model
https://github.com/bigscience-workshop/petals
Inference only: https://lite.koboldai.net/
- Run LLMs at home, BitTorrent‑style
stanford_alpaca
-
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?
-
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.
-
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)
-
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
-
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
-
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
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
llama - Inference code for Llama models
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.
GLM-130B - GLM-130B: An Open Bilingual Pre-Trained Model (ICLR 2023)
llama.cpp - LLM inference in C/C++
Auto-GPT - An experimental open-source attempt to make GPT-4 fully autonomous. [Moved to: https://github.com/Significant-Gravitas/Auto-GPT]
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
Alpaca-Turbo - Web UI to run alpaca model locally