dbrx
h2o-llmstudio
| dbrx | h2o-llmstudio | |
|---|---|---|
| 4 | 13 | |
| 2,407 | 3,602 | |
| 94.6% | 3.3% | |
| 5.9 | 9.3 | |
| 8 days ago | 7 days ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
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dbrx
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Hello OLMo: A Open LLM
One thing I wanted to add and call attention to is the importance of licensing in open models. This is often overlooked when we blindly accept the vague branding of models as “open”, but I am noticing that many open weight models are actually using encumbered proprietary licenses rather than standard open source licenses that are OSI approved (https://opensource.org/licenses). As an example, Databricks’s DBRX model has a proprietary license that forces adherence to their highly restrictive Acceptable Use Policy by referencing a live website hosting their AUP (https://github.com/databricks/dbrx/blob/main/LICENSE), which means as they change their AUP, you may be further restricted in the future. Meta’s Llama is similar (https://github.com/meta-llama/llama/blob/main/LICENSE ). I’m not sure who can depend on these models given this flaw.
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DBRX: A New Open LLM
Sorry, I forgot to link the repository and missed the edit window by the time I realized.
[1] https://github.com/databricks/dbrx
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?
What are some alternatives?
llama - Inference code for Llama models
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/
xTuring - Build, customize and control you own LLMs. From data pre-processing to fine-tuning, xTuring provides an easy way to personalize open-source LLMs. Join our discord community: https://discord.gg/TgHXuSJEk6
killport - A command-line tool to easily kill processes running on a specified port.
mixtral-offloading - Run Mixtral-8x7B models in Colab or consumer desktops
HealthGPT - Query your Apple Health data with natural language 💬 🩺
OLMo - Modeling, training, eval, and inference code for OLMo
bark - 🔊 Text-Prompted Generative Audio Model
makeMoE - From scratch implementation of a sparse mixture of experts language model inspired by Andrej Karpathy's makemore :)
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.
ue5-llama-lora - A proof-of-concept project that showcases the potential for using small, locally trainable LLMs to create next-generation documentation tools.
MiniGPT-4 - Open-sourced codes for MiniGPT-4 and MiniGPT-v2 (https://minigpt-4.github.io, https://minigpt-v2.github.io/)