GPT-4-LLM
Instruction Tuning with GPT-4 (by Instruction-Tuning-with-GPT-4)
alpaca-lora
Instruct-tune LLaMA on consumer hardware (by tloen)
GPT-4-LLM | alpaca-lora | |
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5 | 107 | |
3,998 | 18,238 | |
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5.4 | 3.6 | |
11 months ago | 3 months ago | |
HTML | Jupyter Notebook | |
Apache License 2.0 | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
GPT-4-LLM
Posts with mentions or reviews of GPT-4-LLM.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-08-22.
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Fine-tuning LLMs with LoRA: A Gentle Introduction
I'm using the Instruction Tuning with GPT-4 dataset, which is hosted on Huggingface.
- (31F). Lost 1.8% body fat and gained 1.3 lbs muscle mass in 2 weeks!
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What’s the current best model that will run well locally on a 3090?
No, GPT4 x Alpaca, GPT4 Alpaca, and GPT4All use different datasets. GPT4 x Alpaca uses GPTeacher, GPT4 Alpaca uses Microsoft Research's GPT-4-LLM, and GPT4All uses their own. GPT4All is commonly considered to be the worst out of all of them in the general community.
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GPT4-X-Alpaca 30B 4-bit, by MetaIX based on LoRA by chansung
For anyone wondering how this compares with the 13B GPT4 x Alpaca, the dataset used is different. The 13B GPT4xAlpaca uses the GPTeacher dataset, while this uses the Microsoft Research dataset from Instruction Tuning with GPT-4. It should be a direct upgrade to Stanford's Alpaca, and I'll add it to the wiki as GPT4 Alpaca without an x to differentiate it.
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GPT-4 Takes the Lead in Instruction-Tuning of Large Language Models: Advancing Generalization Capabilities for Real-World Tasks
Github: https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM
alpaca-lora
Posts with mentions or reviews of alpaca-lora.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-09-11.
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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