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LoRA Alternatives
Similar projects and alternatives to LoRA
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text-generation-webui
A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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Open-Assistant
OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.
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petals
🌸 Run LLMs at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading
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lora
Using Low-rank adaptation to quickly fine-tune diffusion models. (by cloneofsimo)
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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LocalAI
:robot: The free, Open Source OpenAI alternative. Self-hosted, community-driven and local-first. Drop-in replacement for OpenAI running on consumer-grade hardware. No GPU required. Runs gguf, transformers, diffusers and many more models architectures. It allows to generate Text, Audio, Video, Images. Also with voice cloning capabilities.
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refact
WebUI for Fine-Tuning and Self-hosting of Open-Source Large Language Models for Coding
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LLaVA
[NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond.
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LLaMA-Adapter
Discontinued Fine-tuning LLaMA to follow Instructions within 1 Hour and 1.2M Parameters [Moved to: https://github.com/OpenGVLab/LLaMA-Adapter] (by ZrrSkywalker)
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LMFlow
An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. Large Models for All.
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LyCORIS
Lora beYond Conventional methods, Other Rank adaptation Implementations for Stable diffusion.
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ComfyUI
The most powerful and modular stable diffusion GUI, api and backend with a graph/nodes interface.
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StyleTTS2
StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models
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SaaSHub
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NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better LoRA alternative or higher similarity.
LoRA reviews and mentions
Posts with mentions or reviews of LoRA.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-12-08.
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DECT NR+: A technical dive into non-cellular 5G
This seems to be an order of magnitude better than LoRa (https://lora-alliance.org/ not https://arxiv.org/abs/2106.09685). LoRa doesn't have all the features this one does like OFDM, TDM, FDM, and HARQ. I didn't know there's spectrum dedicated for DECT use.
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Training LLMs Taking Too Much Time? Technique you need to know to train it faster
So to solve this, we tried researching into some optimization techniques and we found LoRA, Which stands for Low-Rank Adaptation of Large Language Models.
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OpenAI employee: GPT-4.5 rumor was a hallucination
> Anyone have any ideas / knowledge on how they deploy little incremental fixes to exploited jailbreaks, etc?
LoRa[1] would be my guess.
For detailed explanation I recommend the paper. But the short explanation is that it is a trick which lets you train a smaller, lower dimensional model which when you add to the original model it gets you the result you want.
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Can a LoRa be used on models other than Stable Diffusion?
LoRA was initially developed for large language models, https://arxiv.org/abs/2106.09685 (2021). It was later that people discovered that it worked REALLY well for diffusion models.
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StyleTTS2 – open-source Eleven Labs quality Text To Speech
Curious if we'll see a Civitai-style LoRA[1] marketplace for text-to-speech models.
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Andreessen Horowitz Invests in Civitai, Which Profits from Nonconsensual AI Porn
From https://arxiv.org/abs/2106.09685:
> LoRA: Low-Rank Adaptation of Large Language Models
> An important paradigm of natural language processing consists of large-scale pre-training on general domain data and adaptation to particular tasks or domains. As we pre-train larger models, full fine-tuning, which retrains all model parameters, becomes less feasible. Using GPT-3 175B as an example -- deploying independent instances of fine-tuned models, each with 175B parameters, is prohibitively expensive. We propose Low-Rank Adaptation, or LoRA, which freezes the pre-trained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture, greatly reducing the number of trainable parameters for downstream tasks. Compared to GPT-3 175B fine-tuned with Adam, LoRA can reduce the number of trainable parameters by 10,000 times and the GPU memory requirement by 3 times. LoRA performs on-par or better than fine-tuning in model quality on RoBERTa, DeBERTa, GPT-2, and GPT-3, despite having fewer trainable parameters, a higher training throughput, and, unlike adapters, no additional inference latency.
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Is supervised learning dead for computer vision?
Yes, your understanding is correct. However, instead of adding a head on top of the network, most fine-tuning is currently done with LoRA (https://github.com/microsoft/LoRA). This introduces low-rank matrices between different layers of your models, those are then trained using your training data while the rest of the models' weights are frozen.
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Run LLMs at home, BitTorrent‑style
Somewhat yes. See "LoRA": https://arxiv.org/abs/2106.09685
They're not composable in the sense that you can take these adaptation layers and arbitrarily combine them, but training different models while sharing a common base of weights is a solved problem.
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New LoRa RF distance record: 1336 km / 830 mi
With all the naive AI zealotry on HN can you really fault me?
They're referring to this:
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Open-source Fine-Tuning on Codebase with Refact
It's possible to fine-tune all parameters (called "full fine-tune"), but recently PEFT methods became popular. PEFT stands for Parameter-Efficient Fine-Tuning. There are several methods available, the most popular so far is LoRA (2106.09685) that can train less than 1% of the original weights. LoRA has one important parameter -- tensor size, called lora_r. It defines how much information LoRA can add to the network. If your codebase is small, the fine-tuning process will see the same data over and over again, many times in a loop. We found that for a smaller codebase small LoRA tensors work best because it won't overfit as much -- the tensors just don't have the capacity to fit the limited training set exactly. As the codebase gets bigger, tensors should become bigger as well. We also unfreeze token embeddings at a certain codebase size. To pick all the parameters automatically, we have developed a heuristic that calculates a score based on the source files it sees. This score is then used to determine the appropriate LoRA size, number of finetuning steps, and other parameters. We have tested this heuristic on several beta test clients, small codebases of several files, and large codebases like the Linux kernel (consisting of about 50,000 useful source files). If the heuristic doesn't work for you for whatever reason, you can set all the parameters yourself.
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A note from our sponsor - InfluxDB
www.influxdata.com | 18 Apr 2024
Stats
Basic LoRA repo stats
34
8,890
5.4
about 1 month ago
microsoft/LoRA is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of LoRA is Python.
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