Nicolay Gerold
@nicolaygerold
Nicolay Gerold
eneral-purpose models
- 1.1B: TinyDolphin 2.8 1.1B. Takes about ~700MB RAM and tested on my Pi 4 with 2 gigs of RAM. Hallucinates a lot, but works for basic conversation.
- 2.7B: Dolphin 2.6 Phi-2. Takes over ~2GB RAM and tested on my 3GB 32-bit phone via llama.cpp on Termux.
- 7B: Nous Hermes Mistral 7B DPO. Takes about ~4-5GB RAM depending on
r/LocalLLaMA - Reddit
A “market” is a set of buyers, with sufficiently similar needs, constraints, and goals, that the same product can be sold to all of them. The “AI” market consist of companies like OpenAI and Cohere, and indeed that is a large and growing market, but unless you’re competing directly with them, you’re not “in the AI market.”
A chatbot is in the... See more
A chatbot is in the... See more
Jason Cohen • AI startups require new strategies: This time it's actually different
I think he kinda beats up his own definition. A chabot also isn’t a market. A chatbot can solve many different problems. It can be helping your own employees get up to speed quicker during onboarding, serve as discovery, help out confused customers on a website. A chatbot is also in the solution space. And a chatbot is just an AI packaged up to talk with some information source.
I agree with the general statement. AI is in the solution space and we have to find the right problem to solve with it. But there are so many problems out there that you can find one that is solvable with AI. And I am yet to see one process, product, employee that I cannot improve with AI.
Arginine can increase the levels of growth hormone under low glucose levels. It also increases the dilation of blood vessels and with that blood flow. L-Citrulline is a precourser of arginine and has a larger effect on arginine levels.
Andrew Huberman • How to Control Your Metabolism by Thyroid & Growth Hormone
OpenLLMetry is a set of extensions built on top of OpenTelemetry that gives you complete observability over your LLM application. Because it uses OpenTelemetry under the hood, it can be connected to your existing observability solutions - Datadog, Honeycomb, and others.
It's built and maintained by Traceloop under the Apache 2.0 license.
The repo... See more
It's built and maintained by Traceloop under the Apache 2.0 license.
The repo... See more
traceloop • GitHub - traceloop/openllmetry: Open-source observability for your LLM application, based on OpenTelemetry
Comments are helpful sometimes. But sometimes, they are just an indication of bad code.The proper use of comments is to compensate for our failure to express ourself in code [1].Whenever you have to add a comment in your code, ask yourself if it is really required or if you could instead put that into a new function and name the function so that it... See more
How to Write Clean Code in Python
- Always use BUFFERS when running an EXPLAIN . It gives some data that may be crucial for the investigation.
- Always, always try to get an Index Cond (called Index range scan in MySQL) instead of a Filter .
- Always, always, always assume PostgreSQL and MySQL will behave differently. Because they do.
Making a Postgres query 1,000 times faster
Vasopressin: suppresses urination made by the body. It causes water retention. It creates feelings of gitty love and can increases memory.
Andrew Huberman • The Science of Emotions & Relationships
Fine-Tuning for LLM Research by AI Hero
This repo contains the code that will be run inside the container. Alternatively, this code can also be run natively. The container is built and pushed to the repo using Github actions (see below). You can launch the fine tuning job using the examples in the https://github.com/ai-hero/llm-research-examples... See more
This repo contains the code that will be run inside the container. Alternatively, this code can also be run natively. The container is built and pushed to the repo using Github actions (see below). You can launch the fine tuning job using the examples in the https://github.com/ai-hero/llm-research-examples... See more
GitHub - ai-hero/llm-research-fine-tuning
1. Synthetic Data for Baseline Metrics¶
Synthetic data can be used to establish baseline precision and recall metrics for your reverse search. The simplest kind of synthetic data is to take existing text chunks, generate synthetic questions, and verify that when we query our synthetic questions, the sourced text chunk is retrieved correctly.
Benefi... See more
Synthetic data can be used to establish baseline precision and recall metrics for your reverse search. The simplest kind of synthetic data is to take existing text chunks, generate synthetic questions, and verify that when we query our synthetic questions, the sourced text chunk is retrieved correctly.
Benefi... See more