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alpaca-lora reviews and mentions
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LoRA: Low-Rank Adaptation of Large Language Models
For those wondering why this is interesting: This technique is being used to reproduce[0] (though unclear exactly with what fidelity) the Alpaca results from Stanford[1] with a few hours of training on consumer-grade hardware.
I believe that there will be a cottage industry of providing application-specific fine-tuned models like this, that can run in e.g. AWS very inexpensively. The barrier today seems to be that the base model (here, LLaMa) is encumbered and can't be used commercially. Someone will soon, I'm confident, release e.g. an MIT-licensed equivalent and we'll all be off to the races.
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Show HN: Web demo of 13B Alpaca-LLaMA trained on improved Stanford dataset
- Fine-tuning dataset was based on https://github.com/tloen/alpaca-lora/raw/81eb72f707b0505a03b... with minor improvements; I'm going to publish my version soon
- Alpaca-LoRA: Low-Rank LLaMA Instruct-Tuning
- I wrote a Discord bot to host your own ChatGPT-style chatbot with ALPACA finetuned LLaMA weights on consumer GPUs. 13b fits in a 3080, 30b fits in a 3090. No censorship, ask anything you want!
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Show HN: ChatLLaMA – A ChatGPT style chatbot for Facebook's LLaMA
did you use the cleaned and improved alpaca dataset from https://github.com/tloen/alpaca-lora/issues/28 ?
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[P] fastLLaMa, A python wrapper to run llama.cpp
Amazing work! Any plans to get this working with alpaca-lora? Would love to see your fast implementation have the improved outputs from Alpaca.
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Show HN: Finetune LLaMA-7B on commodity GPUs using your own text
I've been playing around with https://github.com/zphang/minimal-llama/ and https://github.com/tloen/alpaca-lora/blob/main/finetune.py, and wanted to create a simple UI where you can just paste text, tweak the parameters, and finetune the model quickly using a modern GPU.
To prepare the data, simply separate your text with two blank lines.
There's an inference tab, so you can test how the tuned model behaves.
This is my first foray into the world of LLM finetuning, Python, Torch, Transformers, LoRA, PEFT, and Gradio.
Enjoy!
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How to do Llama 30B 4bit finetuning?
alpaca-lora applied this successfully to fine-tuning LLaMa, and then exported / combined with the original model, later quantizing back to 4-bit so that it could be loaded by alpaca.cpp.
As for the Lora the 7b trained on a 4090 16gb in 5hrs https://github.com/tloen/alpaca-lora, and almost ran out of memory. I’ve read the 30b has been trained on a single a100 with 40gb
- It can be done! (Devs attention required)
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tloen/alpaca-lora is an open source project licensed under Apache License 2.0 which is an OSI approved license.