HashemAlsaket's Stars
langchain-ai/langchain
🦜🔗 Build context-aware reasoning applications
pytorch/pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
ggerganov/llama.cpp
LLM inference in C/C++
OpenBB-finance/OpenBB
Investment Research for Everyone, Everywhere.
karpathy/llm.c
LLM training in simple, raw C/CUDA
qdrant/qdrant
Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
unslothai/unsloth
Finetune Llama 3.2, Mistral, Phi, Qwen 2.5 & Gemma LLMs 2-5x faster with 80% less memory
NixOS/nix
Nix, the purely functional package manager
vanna-ai/vanna
🤖 Chat with your SQL database 📊. Accurate Text-to-SQL Generation via LLMs using RAG 🔄.
THU-MIG/yolov10
YOLOv10: Real-Time End-to-End Object Detection [NeurIPS 2024]
abetlen/llama-cpp-python
Python bindings for llama.cpp
pytorch/torchchat
Run PyTorch LLMs locally on servers, desktop and mobile
hegelai/prompttools
Open-source tools for prompt testing and experimentation, with support for both LLMs (e.g. OpenAI, LLaMA) and vector databases (e.g. Chroma, Weaviate, LanceDB).
VadimBoev/FlappyBird
Less than 100 Kilobytes. Works for Android 5.1 and above
CatalaLang/catala
Programming language for literate programming law specification
bioint/MetisFL
The first open Federated Learning framework implemented in C++ and Python.
Leeroo-AI/mergoo
A library for easily merging multiple LLM experts, and efficiently train the merged LLM.
automateyournetwork/packet_buddy
pcap analysis provided by chatGPT4 Turbo
Sission/Coupled-VAE-Improved-Robustness-and-Accuracy-of-a-Variational-Autoencoder
We present a coupled Variational Auto-Encoder (VAE) method that improves the accuracy and robustness of the probabilistic inferences on represented data. The new method models the dependency between input feature vectors (images) and weighs the outliers with a higher penalty by generalizing the original loss function to the coupled entropy function, using the principles of nonlinear statistical coupling. We evaluate the performance of the coupled VAE model using the MNIST dataset. Compared with the traditional VAE algorithm, the output images generated by the coupled VAE method are clearer and less blurry. The visualization of the input images embedded in 2D latent variable space provides a deeper insight into the structure of new model with coupled loss function: the latent variable has a smaller deviation and the output values are generated by a more compact latent space. We analyze the histograms of probabilities for the input images using the generalized mean metrics, in which increased geometric mean illustrates that the average likelihood of input data is improved. Increases in the -2/3 mean, which is sensitive to outliers, indicates improved robustness. The decisiveness, measured by the arithmetic mean of the likelihoods, is unchanged and -2/3 mean shows that the new model has better robustness.
HashemAlsaket/AutoBinClassifier
randomizedcoder/goTrackRTP
Jason-Adam/Jason-Adam