Nicolay Gerold

@nicolaygerold

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2-5x faster 50% less memory local LLM finetuning

Manual autograd engine - hand derived backprop steps.

2x to 5x faster than QLoRA. 50% less memory usage.

All kernels written in OpenAI's Triton language.

0% loss in accuracy - no approximation methods - all exact.

No change of hardware necessary. Supports NVIDIA GPUs since 2018+. Minimum CUDA Compute

unslothai • GitHub - unslothai/unsloth: 5X faster 50% less memory LLM finetuning

LLM Training

Traditional AI - The most secure, understandable, and performant. However, Good implementations of traditional AI require that we define the rules behind the system, which makes it unfeasible for many of the use cases that the other 2 techniques thrive on.

Supervised Machine Learning- Middle of the road b/w AI and Deep Learning. Good when we have

Devansh • How to Pick between Traditional AI, Supervised Machine Learning, and Deep Learning [Thoughts]

LLMs

Where would I add generative AI? Generative AI has the ease of accessibility of traditional AI, where people think it is understandable, but it does not have that feature in itself. It also has the opaque and costly nature of DL. Many companies are at the moment rushing into developing things with generative AI without having any prior foundation in AI and any processes set up to manage it: data ops, devops, …

Traditional AI forces you to think about how something works, understand the system, and then define the rules for it. ML lets you use features and feature importance to shortcut some. Deep Learning allows you to brute force it. Generative AI allows you to brute force without any background in DL.

llamafile lets you distribute and run LLMs with a single file. (announcement blog post)

Our goal is to make open source large language models much more accessible to both developers and end users. We're doing that by combining llama.cpp with Cosmopolitan Libc into one framework that collapses all the complexity of LLMs down to a single-file... See more

Mozilla-Ocho • GitHub - Mozilla-Ocho/llamafile: Distribute and run LLMs with a single file.

AI on the Edge // Local First and LLM Inference

The No-Code Generative AI Platform

Deploy custom AI Assistants, Chatbots, and Workflow Automations to make your company 10x more efficient.

No-Code Platform for Enterprise AI

GenAI Startups

Most problems occur that people cannot regulate the stress (levels of cortisol and epinephrine in the brain) to stay calm and select the right action.

Andrew Huberman • Using Cortisol & Adrenaline to Boost Our Energy & Immune System

Snowflake is easy to use. As you mature and scale, Databricks becomes a strong competitor capable of more use cases. I consider both to have a reasonable moat against AI/LLMs, which is data governance with data lineage. I expect that moat to last at least a few years... even though most companies aren’t mature enough to have a strong data... See more

r/dataengineering - Reddit

Data

filesystem_spec

A specification for pythonic filesystems.

Install

pip install fsspec

would install the base fsspec. Various optionally supported features might require specification of custom extra require, e.g. pip install fsspec[ssh] will install dependencies for ssh backends support. Use pip install fsspec[full] for installation of all known... See more

fsspec • GitHub - fsspec/filesystem_spec: A specification that python filesystems should adhere to.

Data Storage

To do this, we employ a technique known as AI-assisted evaluation, alongside traditional metrics for measuring performance. This helps us pick the prompts that lead to better quality outputs, making the end product more appealing to users. AI-assisted evaluation uses best-in-class LLMs (like GPT-4) to automatically critique how well the AI's... See more

Developing Rapidly with Generative AI