ParCIS/Chimera
Chimera: bidirectional pipeline parallelism for efficiently training large-scale models.
This project helps machine learning researchers and engineers train very large neural networks more efficiently. It takes your prepared dataset (like Wikipedia text for BERT models) and uses specialized techniques to distribute the training workload across multiple GPUs. The output is a more quickly trained, large-scale neural network model, ready for deployment or further research. This is for professionals working with state-of-the-art deep learning models.
No commits in the last 6 months.
Use this if you are a machine learning researcher or engineer struggling with the time and computational resources required to train extremely large neural networks.
Not ideal if you are working with smaller models, do not have access to a multi-GPU cluster managed by SLURM, or are not already comfortable with advanced distributed training concepts.
Stars
70
Forks
9
Language
Python
License
GPL-3.0
Category
Last pushed
Mar 20, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/transformers/ParCIS/Chimera"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
openvinotoolkit/nncf
Neural Network Compression Framework for enhanced OpenVINO™ inference
huggingface/optimum
🚀 Accelerate inference and training of 🤗 Transformers, Diffusers, TIMM and Sentence Transformers...
NVIDIA/Megatron-LM
Ongoing research training transformer models at scale
huggingface/optimum-intel
🤗 Optimum Intel: Accelerate inference with Intel optimization tools
eole-nlp/eole
Open language modeling toolkit based on PyTorch