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library_name: transformers
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---
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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###
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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[More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Procedure
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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### Model Architecture and Objective
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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#### Software
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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---
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language:
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- en
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license: apache-2.0
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library_name: transformers
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tags:
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- moe
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- moah
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- mod
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- mh-moe
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datasets:
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- Locutusque/UltraTextbooks
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---
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# Model Card for Model ID
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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MoM: Mixture of Mixture
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This Model is a first test to combine [Jamba](https://huggingface.co/ai21labs/Jamba-v0.1) architecture with bf16 bits linear layers, mixture of attention head and **multi head** mixture of depth.
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The goal is to developpe and test if this kind of architectures have not too much quality loss for a fast inference.
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- **Model type:** Mixture of attention head mixture of depth and mixture of expert bf16 linear layers
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- **License:** Apache licence 2.0
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### Model Sources [optional]
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- **Repository:** https://github.com/ostix360/optimized-LLM
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## How to Get Started with the Model
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This model has a generation problem because of a softmax application in the mod process
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If you want to test this model please look at this repo at this [commit](https://github.com/ostix360/optimized-LLM/)
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## Training Details
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- **wandb**: [training detail](https://wandb.ai/ostix360/Mixture%20of%20mixture%20(mod,%20moah%20moe)/runs/ygwwa30r)
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### Training Data
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We use the first ~0.5B tokens of Locutusque/UltraTextbooks to train this model
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### Training Procedure
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We use adam-8 bits with default betas and epsilon values
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#### Preprocessing [optional]
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The data fit the model max length i.e. 512 tokens
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#### Training Hyperparameters
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Please look at the wandb metadata to see the hyperparameters or the train.py file in the repo
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## Technical Specifications
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### Compute Infrastructure
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#### Hardware
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- one 4070 ti GPU
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#### Software
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- pytorch, transformers etc
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