alpaca-lora
lora
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alpaca-lora | lora | |
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107 | 83 | |
18,167 | 6,597 | |
- | - | |
3.6 | 0.0 | |
2 months ago | about 1 month ago | |
Jupyter Notebook | Jupyter Notebook | |
Apache License 2.0 | Apache License 2.0 |
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alpaca-lora
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How to deal with loss for SFT for CausalLM
Here is a example: https://github.com/tloen/alpaca-lora/blob/main/finetune.py
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How to Finetune Llama 2: A Beginner's Guide
In this blog post, I want to make it as simple as possible to fine-tune the LLaMA 2 - 7B model, using as little code as possible. We will be using the Alpaca Lora Training script, which automates the process of fine-tuning the model and for GPU we will be using Beam.
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Fine-tuning LLMs with LoRA: A Gentle Introduction
Implement the code in Llama LoRA repo in a script we can run locally
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Newbie here - trying to install a Alpaca Lora and hitting an error
Hi all - relatively new to GitHub / programming in general, and I wanted to try to set up Alpaca Lora locally. Following the guide here: https://github.com/tloen/alpaca-lora
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A simple repo for fine-tuning LLMs with both GPTQ and bitsandbytes quantization. Also supports ExLlama for inference for the best speed.
Follow up the popular work of u/tloen alpaca-lora, I wrapped the setup of alpaca_lora_4bit to add support for GPTQ training in form of installable pip packages. You can perform training and inference with multiple quantizations method to compare the results.
- FLaNK Stack Weekly for 20 June 2023
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Converting to GGML?
If instead you want to apply a LoRa to a pytorch model, a lot of people use this script to apply to LoRa to the 16 bit model and then quantize it with a GPTQ program afterwards https://github.com/tloen/alpaca-lora/blob/main/export_hf_checkpoint.py
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Simple LLM Watermarking - Open Lllama 3b LORA
There are a few papers on watermarking LLM output, but from what I have seen they all use complex methods of detection to allow the watermark to go unseen by the end user, only to be detected by algorithm. I believe that a more overt system of watermarking might also be beneficial. One simple method that I have tried is character substitution. For this model, I LORA finetuned openlm-research/open_llama_3b on the alpaca_data_cleaned_archive.json dataset from https://github.com/tloen/alpaca-lora/ modified by replacing all instances of the "." character in the outputs with a "ι" The results are pretty good, with the correct the correct substitutions being generated by the model in most cases. It doesn't always work, but this was only a LORA training and for two epochs of 400 steps each, and 100% substitution isn't really required.
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text-generation-webui's "Train Only After" option
I am kind of new to finetuning LLM's and am not able to understand what this option exactly refers to. I guess it has the same meaning as the "train_on_inputs" parameter of alpacalora though.
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Learning sources on working with local LLMs
Read the paper and also: https://github.com/tloen/alpaca-lora
lora
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You can now train a 70B language model at home
Diffusion unet has an "extended" version nowadays that applies to the resnet part as well as the cross-attention: https://github.com/cloneofsimo/lora
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How it feels right now
Absolutely. But that doesn't matter because you only have to train it at scale, once. There are papers released already that show it's possible to update weights in small sections. You won't have to wait for the next monolithic LLM to drop to get up to date information. It will start to learn in bits and pieces.
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LoRA tuning in julia
No, it's a deep learning thing
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What does Lora mean?
Low Rank Adaptation of Large Language Models.
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[D] An ELI5 explanation for LoRA - Low-Rank Adaptation.
Recently, I have seen the LoRA technique (Low-Rank Adaptation of Large Language Models) as a popular method for fine-tuning LLMs and other models.
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Combining LoRA, Retro, and Large Language Models for Efficient Knowledge Retrieval and Retention
Enter LoRA, a method proposed for adapting pre-trained models to specific tasks[2]. By freezing pre-trained model weights and injecting trainable rank decomposition matrices into the transformer architecture, LoRA can reduce the number of trainable parameters and the GPU memory requirement, making the adaptation of LLMs for downstream tasks more feasible.
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100K Context Windows
Open-source LLM projects have largely solved this using Low-Rank Adaptation of Large Language Models (LoRA): https://arxiv.org/abs/2106.09685
Apparently an RTX 4090 running overnight is sufficient to produce a fine-tuned model that can spit out new Harry Potter stories, or whatever...
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President Biden meets with AI CEOs at the White House amid ethical criticism
Alpaca was trained for $600 ($100 for the smaller model) and offers outputs competitive with ChatGTP. https://arxiv.org/abs/2106.09685
- LoRA: Low-Rank Adaptation of Large Language Models
- LORA: Low-Rank Adaptation of Large Language Models
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.
stable-diffusion-webui - Stable Diffusion web UI
qlora - QLoRA: Efficient Finetuning of Quantized LLMs
LyCORIS - Lora beYond Conventional methods, Other Rank adaptation Implementations for Stable diffusion.
llama.cpp - LLM inference in C/C++
sd_dreambooth_extension
gpt4all - gpt4all: run open-source LLMs anywhere
kohya-trainer - Adapted from https://note.com/kohya_ss/n/nbf7ce8d80f29 for easier cloning
llama - Inference code for Llama models
ControlNet - Let us control diffusion models!
ggml - Tensor library for machine learning
sd-webui-additional-networks