h2o-llmstudio
lora
| h2o-llmstudio | lora | |
|---|---|---|
| 13 | 83 | |
| 3,614 | 6,642 | |
| 3.3% | - | |
| 9.3 | 0.0 | |
| about 9 hours ago | about 2 months ago | |
| Python | Jupyter Notebook | |
| Apache License 2.0 | 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?
lora
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You can now train a 70B language model at home
Diffusion unet has an "extended" version nowadays that applies to the resnet part as well as the cross-attention: https://github.com/cloneofsimo/lora
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How it feels right now
Absolutely. But that doesn't matter because you only have to train it at scale, once. There are papers released already that show it's possible to update weights in small sections. You won't have to wait for the next monolithic LLM to drop to get up to date information. It will start to learn in bits and pieces.
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LoRA tuning in julia
No, it's a deep learning thing
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What does Lora mean?
Low Rank Adaptation of Large Language Models.
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[D] An ELI5 explanation for LoRA - Low-Rank Adaptation.
Recently, I have seen the LoRA technique (Low-Rank Adaptation of Large Language Models) as a popular method for fine-tuning LLMs and other models.
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Combining LoRA, Retro, and Large Language Models for Efficient Knowledge Retrieval and Retention
Enter LoRA, a method proposed for adapting pre-trained models to specific tasks[2]. By freezing pre-trained model weights and injecting trainable rank decomposition matrices into the transformer architecture, LoRA can reduce the number of trainable parameters and the GPU memory requirement, making the adaptation of LLMs for downstream tasks more feasible.
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100K Context Windows
Open-source LLM projects have largely solved this using Low-Rank Adaptation of Large Language Models (LoRA): https://arxiv.org/abs/2106.09685
Apparently an RTX 4090 running overnight is sufficient to produce a fine-tuned model that can spit out new Harry Potter stories, or whatever...
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President Biden meets with AI CEOs at the White House amid ethical criticism
Alpaca was trained for $600 ($100 for the smaller model) and offers outputs competitive with ChatGTP. https://arxiv.org/abs/2106.09685
- LoRA: Low-Rank Adaptation of Large Language Models
- LORA: Low-Rank Adaptation of Large Language Models
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/
stable-diffusion-webui - Stable Diffusion web UI
killport - A command-line tool to easily kill processes running on a specified port.
LyCORIS - Lora beYond Conventional methods, Other Rank adaptation Implementations for Stable diffusion.
HealthGPT - Query your Apple Health data with natural language 💬 🩺
sd_dreambooth_extension
bark - 🔊 Text-Prompted Generative Audio Model
kohya-trainer - Adapted from https://note.com/kohya_ss/n/nbf7ce8d80f29 for easier cloning
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.
ControlNet - Let us control diffusion models!
ue5-llama-lora - A proof-of-concept project that showcases the potential for using small, locally trainable LLMs to create next-generation documentation tools.
sd-webui-additional-networks