pytorch_sparse VS Sparsebit

Compare pytorch_sparse vs Sparsebit and see what are their differences.

pytorch_sparse

PyTorch Extension Library of Optimized Autograd Sparse Matrix Operations (by rusty1s)
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pytorch_sparse Sparsebit
1 1
957 320
- 1.3%
5.3 5.9
13 days ago 4 months ago
Python Python
MIT License Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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pytorch_sparse

Posts with mentions or reviews of pytorch_sparse. We have used some of these posts to build our list of alternatives and similar projects.

Sparsebit

Posts with mentions or reviews of Sparsebit. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-03-19.

What are some alternatives?

When comparing pytorch_sparse and Sparsebit you can also consider the following projects:

MyGrad - Drop-in autodiff for NumPy.

LLaMA-8bit-LoRA - Repository for Chat LLaMA - training a LoRA for the LLaMA (1 or 2) models on HuggingFace with 8-bit or 4-bit quantization. Research only.

sparsegpt-for-LLaMA - Code for the paper "SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot" with LLaMA implementation.

tabmat - Efficient matrix representations for working with tabular data

FQ-ViT - [IJCAI 2022] FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer

text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.

alpaca-lora - Instruct-tune LLaMA on consumer hardware

aimet - AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.

trl - Train transformer language models with reinforcement learning.

llama.cpp - LLM inference in C/C++

nncf - Neural Network Compression Framework for enhanced OpenVINOâ„¢ inference

neural-compressor - SOTA low-bit LLM quantization (INT8/FP8/INT4/FP4/NF4) & sparsity; leading model compression techniques on TensorFlow, PyTorch, and ONNX Runtime