h2o-llmstudio
LLaMA-LoRA-Tuner
| h2o-llmstudio | LLaMA-LoRA-Tuner | |
|---|---|---|
| 13 | 6 | |
| 3,602 | 425 | |
| 3.3% | - | |
| 9.3 | 7.9 | |
| 8 days ago | 12 months ago | |
| Python | Python | |
| Apache License 2.0 | - |
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h2o-llmstudio
- Paid dev gig: develop a basic LLM PEFT finetuning utility
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building LLM model to answer question
Vector databases are probably a good place to start, though you've already tried LlamaIndex. You might want to try https://github.com/h2oai/h2o-llmstudio and https://github.com/h2oai/h2ogpt.
- [P] Uptraining a pretrained model using company data?
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Permissive LLaMA 7b chat/instruct model
Training framework: https://github.com/h2oai/h2o-llmstudio
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Is what I need possible currently?
Check out LLM Studio for fine tuning LLMs. Open source: https://github.com/h2oai/h2o-llmstudio
- FLaNK Stack Weekly for 30 April 2023
- FLaNK Stack Weekly for 24April2023
- GitHub - h2oai/h2o-llmstudio: H2O LLM Studio - a framework and no-code GUI for fine-tuning LLMs
- New Open Source Framework and No-Code GUI for Fine-Tuning LLMs: H2O LLM Studio
- Can an average person learn how to build a LLM model?
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?
What are some alternatives?
h2ogpt - Private chat with local GPT with document, images, video, etc. 100% private, Apache 2.0. Supports oLLaMa, Mixtral, llama.cpp, and more. Demo: https://gpt.h2o.ai/ https://codellama.h2o.ai/
CodeCapybara - Open-source Self-Instruction Tuning Code LLM
killport - A command-line tool to easily kill processes running on a specified port.
AlpacaDataCleaned - Alpaca dataset from Stanford, cleaned and curated
HealthGPT - Query your Apple Health data with natural language 💬 🩺
CodeCapypara - [Moved to: https://github.com/FSoft-AI4Code/CodeCapybara]
bark - 🔊 Text-Prompted Generative Audio Model
BELLE - BELLE: Be Everyone's Large Language model Engine（开源中文对话大模型）
pandas-ai - Chat with your database (SQL, CSV, pandas, polars, mongodb, noSQL, etc). PandasAI makes data analysis conversational using LLMs (GPT 3.5 / 4, Anthropic, VertexAI) and RAG.
lora - Train Large Language Models (LLM) using LoRA
ue5-llama-lora - A proof-of-concept project that showcases the potential for using small, locally trainable LLMs to create next-generation documentation tools.
koboldcpp - A simple one-file way to run various GGML and GGUF models with KoboldAI's UI