Model Card for Model ID
Model Details
Model Description
MoM: Mixture of Mixture
This Model is a first test to combine Jamba architecture with mixture of attention head and mixture of depth.
Attention layers only are in bf16 precision and the rest is in 1.58bits precision
17M over a total of 1025M parameters are in bf16 precision ~ 1.7% of the parameters are in bf16
The goal is to developpe and test if this kind of architectures have not too much quality loss for a fast inference.
- Model type: Mixture of attention head mixture of depth and mixture of expert with 1.58bits linear layer excpeted for attention
- License: Apache licence 2.0
Model Sources [optional]
- Repository: https://github.com/ostix360/optimized-LLM
How to Get Started with the Model
If you want to test this model please look at this repo at this commit
Training Details
- wandb: training detail
Training Data
We use the first ~0.5B tokens of Locutusque/UltraTextbooks to train this model
Training Procedure
We use adam-8 bits with default betas and epsilon values
Preprocessing [optional]
The data fit the model max length i.e. 512 tokens
Training Hyperparameters
Please look at the wandb meta data or the train.py in the repo to see the hyperparameters
Technical Specifications [optional]
Compute Infrastructure
Hardware
- one 4070 ti GPU
Software
- pytorch, transformers etc
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