LLaMA-LoRA-Tuner
simple-llm-finetuner
| LLaMA-LoRA-Tuner | simple-llm-finetuner | |
|---|---|---|
| 6 | 12 | |
| 426 | 1,977 | |
| - | - | |
| 7.9 | 10.0 | |
| 12 months ago | 5 months ago | |
| Python | Jupyter Notebook | |
| - | MIT License |
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LLaMA-LoRA-Tuner
- [P] Uptraining a pretrained model using company data?
- (HELP) Token Issue on Generation
- Help with Random Characters and Words on Output
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Fine-tuning LLaMA for research without Meta license
I would like to fine-tune LLaMA using this tuner for a research paper, but I am wondering if it is legal to do so. If it isn't, does anyone have suggestions for alternatives which are similarly user-friendly as the one above, since I am not a good programmer? Any advice would be greatly appreciated, thank you!
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Why run LLMs locally?
The bad news is that, as far as I know, it does require a GPU. The good news is that I've gotten training done with a 7b model on both google colab and kaggle with free accounts. Both have 'just' enough vram to make it work as long as you use load the model in 8bit. Like --load-in-8bit on the command line with oobabooga. The Lora Tuner frontend even has a colab notebook set up to simplify things even more. Though the frontend keeps the LoRA Rank and LoRA Alpha values capped pretty low. Thankfully that's just set in the GUI though. I think it was one of the files in its UI directory. Pretty easy to just hand edit it to allow for higher values if desired.
- How can I train my custom dataset on top of Vicuna?
simple-llm-finetuner
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Ask HN: Resource to learn how to train and use ML Models
Just the appropriate reddit groups and follow folks on twitter, plus use a search engine.
1. Learn to run a model, checkout llama.cpp Tons of free models on huggingface.com
2. Learn to finetune a model - https://github.com/lxe/simple-llm-finetuner
3. Learn to train one. PyTorch, TensorFlow, HuggingFace libraries, etc.
Good luck.
- How can I train my custom dataset on top of Vicuna?
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[D] The best way to train an LLM on company data
So as far as set up goes, you just need to: “”” Git clone https://github.com/lxe/simple-llama-finetuner Cd simple-llama-finetuner Pip install -r requirements.txt Python app.py ## if you’re on a remote machine (Paperspace is my go to) then you may need to edit the last line of this script to set ‘share=True’ in the launch args “””
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Show HN: Document Q&A with GPT: web, .pdf, .docx, etc.
oobabooga's textgen webui has a tab for fine tuning now. You only need a single consumer GPU to fine tune up to 33B parameter models at a rate of about 200 epochs per hour, per GPU.
There are also one-click finetuning projects which run on free Google Colab GPUs like https://github.com/lxe/simple-llama-finetuner
It's easy and not complex at all.
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How do I fine tune 4 bit or 8 bit models?
for a single 4090, easiest way to get started and simple to use: https://github.com/lxe/simple-llama-finetuner
- Are there publicly available datasets other than Alpaca that we can use to fine-tune LLaMA?
- Show HN: Finetune LLaMA-7B on commodity GPUs using your own text
- [Project] Finetune LLaMA-7B on commodity GPUs (and Colab) using your own text
What are some alternatives?
CodeCapybara - Open-source Self-Instruction Tuning Code LLM
alpaca-lora - Instruct-tune LLaMA on consumer hardware
AlpacaDataCleaned - Alpaca dataset from Stanford, cleaned and curated
paper-qa - LLM Chain for answering questions from documents with citations
CodeCapypara - [Moved to: https://github.com/FSoft-AI4Code/CodeCapybara]
peft - 🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
BELLE - BELLE: Be Everyone's Large Language model Engine（开源中文对话大模型）
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
lora - Train Large Language Models (LLM) using LoRA
minimal-llama
koboldcpp - A simple one-file way to run various GGML and GGUF models with KoboldAI's UI
OpenChatKit