simple-llm-finetuner
AlpacaDataCleaned
simple-llm-finetuner | AlpacaDataCleaned | |
---|---|---|
12 | 14 | |
1,977 | 1,394 | |
- | - | |
10.0 | 7.6 | |
5 months ago | about 1 year ago | |
Jupyter Notebook | Python | |
MIT License | Apache License 2.0 |
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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
AlpacaDataCleaned
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While training LoRA I get 'Failed to read file... JSON parse error'
I tried using the default alpaca_data_cleaned.json training dataset as mentioned here: https://github.com/gururise/AlpacaDataCleaned/blob/main/alpaca_data_cleaned.json. Does anyone know why I could be getting this error? The file must be in correct format since it is the default file they have shown in their example.
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Why run LLMs locally?
This cleaned alpaca dataset gives a good idea of how data is formatted for the standard alpaca json format. Personally, I'd handle making your own datasets by using gpt4 to format the data into a dataset. You can do it by hand or use a llama model, but I've personally just found using chatgpt to be the most efficient way to get the highest possible output. I'm trying to go for quality over quantity.
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New llama LoRA trained on WizardLM dataset
I created a dataset merge based on the following very high quality datasets:
- [P] Finetuning a commercially viable open source LLM (Flan-UL2) using Alpaca, Dolly15K and LoRA
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Stability AI Launches the First of Its StableLM Suite of Language Models
That dataset is licensed under CC BY NC 4.0, which is not open. It also has a bunch of garbage in it; see https://github.com/gururise/AlpacaDataCleaned
- Alpacino-13B
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GPT4-X-Alpaca 30B 4-bit, by MetaIX based on LoRA by chansung
The alpaca cleaned dataset has integrated the Microsoft GPT-4 dataset and cleaned many of the issues.
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Alpaca, LLaMa, Vicuna [D]
13b Alpaca Cleaned (trained on the cleaned dataset) is very impressive and works well as an instruct model w/o any censorship.
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Is there a good place to post datasets for the community?
There's already a community maintained Alpaca with cleaned data. https://github.com/gururise/AlpacaDataCleaned And a huge amount of work has already been done.
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Dirty data sets and LLaMA/ALPACA...
this might be what you're looking for: https://github.com/gururise/AlpacaDataCleaned
What are some alternatives?
alpaca-lora - Instruct-tune LLaMA on consumer hardware
StableLM - StableLM: Stability AI Language Models
paper-qa - LLM Chain for answering questions from documents with citations
safetensors - Simple, safe way to store and distribute tensors
peft - 🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
koboldcpp - A simple one-file way to run various GGML and GGUF models with KoboldAI's UI
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
simpleAI - An easy way to host your own AI API and expose alternative models, while being compatible with "open" AI clients.
minimal-llama
GPT-4-LLM - Instruction Tuning with GPT-4
OpenChatKit
txtinstruct - 📚 Datasets and models for instruction-tuning