Ostixe360
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MoMv3-M-A-mixed-precision

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 ---
 library_name: transformers
-tags: []
 ---
 # Model Card for Model ID
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 ---
 library_name: transformers
+tags:
+- moe
+- moah
+- mod
+license: apache-2.0
+datasets:
+- Locutusque/UltraTextbooks
+language:
+- en
 ---
 # Model Card for Model ID
 ## Model Details
 ### Model Description
 <!-- Provide a longer summary of what this model is. -->
+MoM: Mixture of Mixture
+This Model is a first test to combine [Jamba](https://huggingface.co/ai21labs/Jamba-v0.1) architecture with mixture of attention head and mixture of depth.
+Mamba and attention layers are in bf16 precision and the rest is in 1.58bits precision
+107M over a total of 1025M parameters are in bf16 precision ~ 10% 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 for **MLP**
+- **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](https://github.com/ostix360/optimized-LLM/tree/d266bc404346b71ea237c0744be0f8928f6b3217)
 ## Training Details
+  - **wandb**: [training detail](https://wandb.ai/ostix360/Mixture%20of%20mixture%20(mod,%20moah%20moe)/runs/wtoujazq)
+### Training Data
+We use the first 100k data 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