FYI - #llama is NOT#opensource. The license is categorically not open source. Among other things, the llama 2 and 3 licenses explicitly violate Field of Endeavor.
I see all sorts of blogs and marketing materials claiming things are "open source" because they used llama somewhere. Please do not take these claims at face value.
"There has been a shift in the #AI space: some models, like #ChatGPT & #Gemini, have evolved into entire web platforms spanning multiple use cases & access points. Other large language models like #LLaMa or #OLMo, though technically speaking they share a basic architecture, don’t actually fill the same role. They are intended to live in the background as a service or component, not in the foreground as a name brand." https://techcrunch.com/2024/04/19/too-many-models/
With all the valid concern around #llm and #genai power and water usage, I thought I'd start a blog series on tiny LLMs. Let's see what they can do on real tasks on very power efficient hardware.
Meta released today Llama 3, the next generation of the Llama model. LLama 3 is a state-of-the-art open-source large language model. Here are some of the key features of the model: 🧵👇🏼
A major release to Ollama - version 0.1.32 is out. The new version includes:
✅ Improvement of the GPU utilization and memory management to increase performance and reduce error rate
✅ Increase performance on Mac by scheduling large models between GPU and CPU
✅ Introduce native AI support in Supabase edge functions
After months of work and $10 million, Databricks has unveiled DBRX - the world's most potent publicly available open-source large language model.
DBRX outperforms open models like Meta's Llama 2 across benchmarks, even nearing the abilities of OpenAI's closed GPT-4. Novel architectural tweaks like a "mixture of experts" boosted DBRX's training efficiency by 30-50%.
Please, use #AI to generate tons of #content that you otherwise couldn't.
But for the love of all that is holy, pay attention to what you are putting out. Read the output. If it doesn't say exactly what you would say, edit it! Make changes. Regenerate. Go through the process of making it good.
I truly don't think people hate AI content. They hate lazy content.
The Code Llama 34b model isn't half bad! Been toying around with it integrated into clion having it explain my own code to me and generate small functions and it's been so far around 90% successful, with most of the errors being minor, the bug detection does have a decent amount of false positives though. I also like that it's aware enough of api's to give doc links
Bonus points for it going off on a tangent once on why console applications are better than gui.
(1/3) Last Friday, I was planning to watch Masters of the Air ✈️, but my ADHD had different plans 🙃, and I ended up running a short POC and creating a tutorial for getting started with Ollama Python 🚀. The settings are available for both Docker 🐳 and locally.
TLDR: It is straightforward to run LLM models locally with the Ollama Python library. Models with up to ~7B parameters run smoothly with low compute resources.