LLaMA-8bit-LoRA
Repository for Chat LLaMA - training a LoRA for the LLaMA (1 or 2) models on HuggingFace with 8-bit or 4-bit quantization. Research only. (by serp-ai)
alpaca-lora
Instruct-tune LLaMA on consumer hardware (by tloen)
| LLaMA-8bit-LoRA | alpaca-lora | |
|---|---|---|
| 3 | 107 | |
| 145 | 18,197 | |
| 0.7% | - | |
| 5.1 | 3.6 | |
| 8 months ago | 2 months ago | |
| Python | Jupyter Notebook | |
| - | 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.
LLaMA-8bit-LoRA
Posts with mentions or reviews of LLaMA-8bit-LoRA.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-04-06.
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Any news on training LoRAs in 4-bit mode?
https://github.com/serp-ai/LLaMA-8bit-LoRA/blob/main/docs/merging_the_weights.md < merge models
- [R] π€π Unlock the Power of Personal AI: Introducing ChatLLaMA, Your Custom Personal Assistant! ππ¬
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 "αΎΎ" 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
What are some alternatives?
When comparing LLaMA-8bit-LoRA and alpaca-lora you can also consider the following projects:
text-generation-webui-testing - A fork of textgen that still supports V1 GPTQ, 4-bit lora and other GPTQ models besides llama.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
sparsegpt-for-LLaMA - Code for the paper "SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot" with LLaMA implementation.
qlora - QLoRA: Efficient Finetuning of Quantized LLMs
trl - Train transformer language models with reinforcement learning.
llama.cpp - LLM inference in C/C++
Sparsebit - A model compression and acceleration toolbox based on pytorch.
gpt4all - gpt4all: run open-source LLMs anywhere
alpaca_lora_4bit
llama - Inference code for Llama models
ggml - Tensor library for machine learning
RWKV-LM - RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.
LLaMA-8bit-LoRA vs text-generation-webui-testing
alpaca-lora vs text-generation-webui
LLaMA-8bit-LoRA vs sparsegpt-for-LLaMA
alpaca-lora vs qlora
LLaMA-8bit-LoRA vs trl
alpaca-lora vs llama.cpp
LLaMA-8bit-LoRA vs Sparsebit
alpaca-lora vs gpt4all
LLaMA-8bit-LoRA vs alpaca_lora_4bit
alpaca-lora vs llama
alpaca-lora vs ggml
alpaca-lora vs RWKV-LM