h2o-llmstudio
MiniGPT-4
| h2o-llmstudio | MiniGPT-4 | |
|---|---|---|
| 13 | 37 | |
| 3,602 | 24,925 | |
| 3.3% | 0.9% | |
| 9.3 | 9.1 | |
| 8 days ago | 19 days ago | |
| Python | Python | |
| Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
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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?
MiniGPT-4
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"Building Machines That Learn and Think Like People", 7 Years Later
I just think the tech has been out for so long it's not as big of a deal. Mini-Gpt4 has been out for 6 months! Of course the descriptions aren't exactly gpt-4 grade, but with mistral 7b being used as the language model instead of llama 7b, the reasoning ability will improve noticeably.
[1] https://github.com/Vision-CAIR/MiniGPT-4
- Minigpt4 Inference on CPU
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Multimodal LLM for infographics images
Isn't there only two open multimodal LLMs, LLaVA and mini-gpt4?
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Ai trained on photos
For LLM visual instruction, you can use LLaVA, LaVIN, or MiniGPT-4.
- CLIP and DeepDanbooru Alternatives For Prompt Generation [Relevant Self-Promotion]
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Looking for a pre trained food recognition model
Please read the rules before posting. If you want a model for visual instruction, use LLaVA, LaVIN, or MiniGPT-4.
- Minigpt-4 (Vicuna 13B + images)
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Upload a photo of your meal and get roasted by ChatGPT
So we use MiniGPT-4 for image parsing, and yep it does return a pretty detailed (albeit not always accurate) description of the photo. You can actually play around with it on Huggingface here.
We use MiniGPT-4 first to interpret the image and then pass the results onto GPT-4. Hopefully, once GPT-4 makes its multi-modal functionality available, we can do it all in one request.
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Give some love to multi modal models trained on censored llama based models
But I would like to bring up that there are some multi models(llava, miniGPT-4) that are built based on censored llama based models like vicuna. I tried several multi modal models like llava, minigpt4 and blip2. Llava has very good captioning and question answering abilities and it is also much faster than the others(basically real time), though it has some hallucination issue.
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/
LLaVA - [NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond.
killport - A command-line tool to easily kill processes running on a specified port.
FastChat - An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.
HealthGPT - Query your Apple Health data with natural language 💬 🩺
AutoGPT - AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.
bark - 🔊 Text-Prompted Generative Audio Model
stable-diffusion-webui-wd14-tagger - Labeling extension for Automatic1111's Web UI
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.
BooruDatasetTagManager
ue5-llama-lora - A proof-of-concept project that showcases the potential for using small, locally trainable LLMs to create next-generation documentation tools.