[ICLR 2024] Fine-tuning LLaMA to follow Instructions within 1 Hour and 1.2M Parameters
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Learn where some of the network sysctl variables fit into the Linux/Kernel network flow. Translations: 🇷🇺
Train the smallest LM you can that fits in 16MB. Best model wins!
Generates x86, x64, or AMD64+x86 position-independent shellcode that loads .NET Assemblies, PE files, and other Windows payloads from memory and runs them with parameters
:control_knobs: Compact GUI for fine-tuning parameters and monitoring value changes
- tweaks
- gui
- creative-coding
- zero-dependency
A 100M-parameter multilingual TTS model for real-time CPU inference, voice cloning, and 48 kHz stereo generation
- chinese
- english
- multilingual
- realtime
A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
- automl
- hyperparam
- automated-machine-learning
- machine-learning
Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models
- deep-learning
- coursera
- coursera-assignment
- coursera-specialization
A curated list of automated machine learning papers, articles, tutorials, slides and projects
- automl
- automated-feature-engineering
- hyperparameter-optimization
- neural-architecture-search
High-speed download of LLaMA, Facebook's 65B parameter GPT model
Code and hyperparameters for the paper "Generative Adversarial Networks"
A ~9M parameter LLM that talks like a small fish.
Pretrained language model with 100B parameters
AI Infra / AI Orchestration / AI Control Plane
Fluro is a Flutter routing library that adds flexible routing options like wildcards, named parameters and clear route definitions.
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.
- deep-learning
- machine-learning
- ml-platform
- ml-infrastructure
Mining URLs from dark corners of Web Archives for bug hunting/fuzzing/further probing
- parameter-finder
- parameter
- bugbounty
- content-discovery
Parameterized ergonomic keyboard
Official repository for MOSS-Image, a 5B-parameter base DiT model with high-quality text rendering, layout generation, and efficient fine-tuning.
- controllable-generation
- foundation-models
- image-generation
- text-rendering
Automatic architecture search and hyperparameter optimization for PyTorch
- automl
- deep-learning
- tabular-data
- pytorch
CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs. It supports self-contained C-code generation and interfaces state-of-the-art codes such as SUNDIALS, IPOPT etc. It can be used from C++, Python, Matlab/Octave, Julia or Javascript
- optimization
- nonlinear
- derivatives
- code-generation
Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization
- hyperopt
- keras
- hyperparameter-optimization
Map incoming controller parameters to named scopes in your resources