VOLlama v0.1.0, an open-source, accessible chat client for OLlama
Unfortunately, many user interfaces for open source large language models are either inaccessible or annoying to use with screen readers, so I decided to make one for myself and others. Non screen reder users are welcome to use it as well.
I hope that ML UI libraries like Streamlit and Gradio will become more friendly with screen readers in the future, so making apps like this is not necessary! #LLM#AI#ML https://chigkim.github.io/VOLlama/
I really like the convention of using ✨ sparkle iconography as an “automagic” motif, e.g. to smart-adjust a photo or to automatically handle some setting. I hate that it has become the defacto iconography for generative AI. 🙁
#ML#AI#GenerativeAI#LLMs#FoundationModels#PoliticalEconomy: "A recent innovation in the field of machine learning has been the creation of very large pre-trained models, also referred to as ‘foundation models’, that draw on much larger and broader sets of data than typical deep learning systems and can be applied to a wide variety of tasks. Underpinning text-based systems such as OpenAI's ChatGPT and image generators such as Midjourney, these models have received extraordinary amounts of public attention, in part due to their reliance on prompting as the main technique to direct and apply them. This paper thus uses prompting as an entry point into the critical study of foundation models and their implications. The paper proceeds as follows: In the first section, we introduce foundation models in more detail, outline some of the main critiques, and present our general approach. We then discuss prompting as an algorithmic technique, show how it makes foundation models programmable, and explain how it enables different audiences to use these models as (computational) platforms. In the third section, we link the material properties of the technologies under scrutiny to questions of political economy, discussing, in turn, deep user interactions, reordered cost structures, and centralization and lock-in. We conclude by arguing that foundation models and prompting further strengthen Big Tech's dominance over the field of computing and, through their broad applicability, many other economic sectors, challenging our capacities for critical appraisal and regulatory response." https://journals.sagepub.com/doi/full/10.1177/20539517241247839
CakeML is a functional programming language and an ecosystem of proofs and tools built around the language. The ecosystem includes a proven-correct compiler that can bootstrap itself.
Tired of neutral responses from LLMs? Llama-3 seems great at following system prompts, so try this system prompt for an opinionated chatbot.
"You are a helpful, opinionated, decisive assistant. When asked a yes/no question, begin your respond with one word answer: yes or no. For open-ended or complex questions, adopt a firm stance. Justify your views with well-reasoned arguments, robust evidence, and succinct explanations, ensuring clarity and confidence in every response." #LLM#AI#ML
I've noticed they've started adding ML in anti-virus software, because it is significantly increasing the number of false positives on my Python-based app. 🙄
All of the red entries on Virus Total lately have all been ML detections. Of course, it's always been a problem with anti-virus mistakenly flagging Python apps as malware, but after I stopped using the common packaging tools, this went away. Now it's back.
Mark Zuckerberg on Llama 3: Apparently Meta stopped training Llama-3-70b before convergence and decided to move onto Llama-4. Meaning they could have kept training and made it smarter! Also llama3-70b multimodal as well as multilingual and bigger context window are coming. #LLM#AI#MLhttps://youtu.be/bc6uFV9CJGg
Start saving money for that M4 Ultra with 500GB! Maybe this could be the first open source that could surpass GPT-4! AIatMeta: "Llama 3 8B & 70B models are just the beginning of what we’re working to release for Llama 3. Our largest models currently in the works are 400B+ parameters and while they’re still in active development, we’re excited about how this work is trending." #LLM#AI#MLhttps://twitter.com/AIatMeta/status/1780997414071181370
Earlier today, Microsoft released new WizardLM-2 7b, 8x22b, 70b with great benchmark result, (of course, they say as good or almost same as GPT-4), but they removed weights on Huggingface, repo on Github, and their whitepaper. Someone on Reddit joked maybe they released GPT-4 by mistake! lol Quantized. weights from other people are still around on Huggingface! #ML#LLM#AI
Cool tip for running LLMs on Apple Silicon! By default, MacOS allows GPU to use up to 2/3 of RAM on machines with <=36GB and 3/4 on machines with >36GB. I used the command sudo sysctl iogpu.wired_limit_mb=57344 to override and allocate 56GB/64GB for GPU. This allowed me to load all layers of larger models for a faster speed! #MacOS#LLM#AI#ML
Ya está abierto el registro para nuestra reunión de abril: 🐲 LLMOps & ML para Drilling Performance y Python & Mazmorras, este mes en las oficinas de Repsol
Thanks to all the recent large LLMs, "Apple is considering support for up to half a terabyte of RAM" for the highest-end m4 Mac configurations. I'm sure the price won't be cheap, but I bet it will be cheaper than getting 500GB in vram from NVidia. lol #LLM#AI#MLhttps://9to5mac.com/2024/04/11/apple-first-m4-mac-release-ai/