LongLoRA
xTuring
| LongLoRA | xTuring | |
|---|---|---|
| 4 | 31 | |
| 2,473 | 2,525 | |
| 3.6% | 0.9% | |
| 9.1 | 8.4 | |
| 3 months ago | about 2 months ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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.
LongLoRA
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Ask HN: AI/ML papers to catch up with current state of AI?
LongAlpaca / One of many ways to extend context, and a useful dataset / https://arxiv.org/abs/2309.12307
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Aurelian: 70B 32K story-writing (and more) [Alpha]
Finally, LongLORA is a method to reduce the number of computations over a large context, and also specifically train the embed and norm layers fully, that is, no quantization or LORA for those. They are small layers and easy to train without too much VRAM cost, but the LongLORA authors noticed they have a big impact on long context performance. I am not using their computation reduction methods, but I am using their suggestion to train embed/norm layers fully.
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Why train on Yi 4K instead of 200K?
That used to be true, but things like LongLORA and LongQLoRA demonstrate that you can increase the context length of a foundation model.
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Using Overfitting to Debug My LLM [P]
For reference, I am using the LongLoRA SFT implementation for fine-tuning a CodeLLaMA model on a code generation instruction. I have also attached my evaluation code below:
xTuring
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I'm developing an open-source AI tool called xTuring, enabling anyone to construct a Language Model with just 5 lines of code. I'd love to hear your thoughts!
Explore the project on GitHub here.
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LLaMA 2 fine-tuning made easier and faster
If you're curious, I encourage you to: - Dive deeper with the LLaMA 2 tutorial here. - Explore the project on GitHub here. - Connect with our community on Discord here.
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RAG vs. Fine-Tuning
If you want best performance, you need to do both RAG and fine-tuning very well. There are plenty of resources on doing fine-tuning thought. I'm one of the contributors to https://github.com/stochasticai/xturing project focused on fine-tuning LLMs. You can find help in the discord channel listed on the GitHub.
- Build, customize and control your own personal LLMs via xTuring OSS
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Finetuning LLaMA 2 (the base models) ?
What tools do you use and achieved great results ? … For me i have tried xturing and SFTTrainer and they got me a semi okay results.
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Finetuning using Google Colab (Free Tier)
Code: https://github.com/stochasticai/xTuring/blob/main/examples/llama/llama_lora_int8.py Colab: https://colab.research.google.com/drive/1SQUXq1AMZPSLD4mk3A3swUIc6Y2dclme?usp=sharing
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I would like to try my hand at finetuning some models. What is the best way to start? I have some questions that I'd appreciate your help on.
We are a group of researchers out of Harvard working on open-source library called xTuring, focused on fine-tuning LLMs: https://github.com/stochasticai/xturing.
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Fine tuning on my tweets
Fine tuning I was thinking about using this (low GPU memory footprint): https://github.com/stochasticai/xturing/blob/main/examples/int4_finetuning/README.md
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Colab for finetuning llama models in 4-bit?
I can't speak for QLORA, as I haven't had a chance to get an implementation working, but I've had success with StochasticAI's Xturing. It's by far the most streamlined method of finetuning I've come across, and they offer int8 and int4 fintuning (but only for llama-7B).
- Just wanna say this.
What are some alternatives?
relora - Official code for ReLoRA from the paper Stack More Layers Differently: High-Rank Training Through Low-Rank Updates
quivr - Your GenAI Second Brain 🧠 A personal productivity assistant (RAG) ⚡️🤖 Chat with your docs (PDF, CSV, ...) & apps using Langchain, GPT 3.5 / 4 turbo, Private, Anthropic, VertexAI, Ollama, LLMs, Groq that you can share with users ! Local & Private alternative to OpenAI GPTs & ChatGPT powered by retrieval-augmented generation.
Zicklein - Finetuning instruct-LLaMA on german datasets.
axolotl - Go ahead and axolotl questions
torch-adapters - Small Library of PyTorch Adaptation modules
FinGPT - FinGPT: Open-Source Financial Large Language Models! Revolutionize 🔥 We release the trained model on HuggingFace.
discus - A data-centric AI package for ML/AI. Get the best high-quality data for the best results. Discord: https://discord.gg/t6ADqBKrdZ
awesome-totally-open-chatgpt - A list of totally open alternatives to ChatGPT
punica - Serving multiple LoRA finetuned LLM as one
Meshtasticator - Discrete-event and interactive simulator for Meshtastic.
RingAttention - Transformers with Arbitrarily Large Context