| #!/usr/bin/env python3 | |
| """ | |
| Detects whether the terminal is using a light or dark theme and automatically | |
| adjusts 256-color palette indices so that colors render consistently regardless | |
| of the active theme. This is especially useful when a terminal (e.g. Ghostty) | |
| generates its 256-color palette to match the current theme — or when it doesn't, | |
| and we need to compensate by flipping the indices ourselves. | |
| """ | |
| import os |
| """ | |
| The most atomic way to train and run inference for a GPT in pure, dependency-free Python. | |
| This file is the complete algorithm. | |
| Everything else is just efficiency. | |
| @karpathy | |
| """ | |
| import os # os.path.exists | |
| import math # math.log, math.exp |
Terminals should generate the 256-color palette from the user's base16 theme.
If you've spent much time in the terminal, you've probably set a custom base16 theme. They work well. You define a handful of colors in one place and all your programs use them.
The drawback is that 16 colors is limiting. Complex and color-heavy programs struggle with such a small palette.
| --- | |
| name: frontend-design | |
| description: Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, or applications. Generates creative, polished code that avoids generic AI aesthetics. | |
| license: Complete terms in LICENSE.txt | |
| --- | |
| This skill guides creation of distinctive, production-grade frontend interfaces that avoid generic "AI slop" aesthetics. Implement real working code with exceptional attention to aesthetic details and creative choices. | |
| The user provides frontend requirements: a component, page, application, or interface to build. They may include context about the purpose, audience, or technical constraints. |
| import os | |
| import json | |
| import subprocess | |
| from anthropic import Anthropic | |
| # Tool definitions | |
| TOOLS = [ | |
| { | |
| "name": "list_files", | |
| "description": "List files and directories at a given path", |
| # train_grpo.py | |
| # | |
| # See https://github.com/willccbb/verifiers for ongoing developments | |
| # | |
| """ | |
| citation: | |
| @misc{brown2025grpodemo, | |
| title={Granular Format Rewards for Eliciting Mathematical Reasoning Capabilities in Small Language Models}, | |
| author={Brown, William}, |
| You are an assistant that engages in extremely thorough, self-questioning reasoning. Your approach mirrors human stream-of-consciousness thinking, characterized by continuous exploration, self-doubt, and iterative analysis. | |
| ## Core Principles | |
| 1. EXPLORATION OVER CONCLUSION | |
| - Never rush to conclusions | |
| - Keep exploring until a solution emerges naturally from the evidence | |
| - If uncertain, continue reasoning indefinitely | |
| - Question every assumption and inference |
This community was a great part of my life for the past two years, so as 2024 comes to a close, I wanted to feed my nostalgia a bit. Let me take you back to the most notable things happened here this year.
This isn't a log of model releases or research, rather things that were discussed and upvoted by the people here. So notable things missing is also an indication of what was going on of sorts. I hope that it'll also show the amount of progress and development that happend in just a single year and make you even more excited for what's to come in 2025.
The year started with the excitement about Phi-2 (443 upvotes, by u/steph_pop). Phi-2 feels like ancient history these days, it's also fascinating that we end the 2024 with the Phi-4. Just one week after, people discovered that apparently it [was trained on the software engineer's diary](https://reddit.com/r/LocalLLaMA/comments/1
Good question! I am collecting human data on how quantization affects outputs. See here for more information: ggml-org/llama.cpp#5962
In the meantime, use the largest that fully fits in your GPU. If you can comfortably fit Q4_K_S, try using a model with more parameters.
See the wiki upstream: https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix
