Track 1 (ML): Cerebras modelzoo ML models

Corresponds to models already present in version R2.4.0 of the Cerebras modelzoo ML models software.

Good Match Criteria: You would be a good match for this track if your research already uses, or could potentially use, any of the following models, supported via PyTorch:

Model	Code Pointer
BERT	Code
BERT (fine-tuning) Classifier	Code
BERT (fine-tuning) Named Entity Recognition	Code
BERT (fine-tuning) Summarization	Code
BLOOM	Code
BTLM	Code
DiT	Code
DPO	Code
DPR	Code
ESM-2	Code
Falcon	Code
GPT-2	Code
GPT-3	Code
GPT-J	Code
GPT-NeoX	Code
GPT-J (fine-tuning) Summarization	Code
JAIS	Code
LLaMA, LLaMA-2 and LLaMA-3	Code
LLaVA	Code
Mistral	Code
Mixtral of Experts	Code
Multimodal Simple	Code
RoBERTa	Code
SantaCoder	Code
StarCoder	Code
Transformer	Code
T5	Code

Based on the Cerebras modelzoo R2.4.0 GitHub page.

Track Specific Questions

If your project falls under this category, make sure to address the following questions in your application document:

1. Please, indicate which model(s) from the modelzoo you intend to use. Do you anticipate being interested in adjusting the model architecture?
2. Please, describe the dataset you are intending to use.
3. How big is the dataset of interest (total dataset size, number of samples, and sample size in MB)?
4. Please, elaborate on the readiness of the dataset of interest. Is it fully available at this time? If not, how soon would it be fully available?
5. Please specify the shapes of the input and output tensors for your model/s.
6. If possible, please specify the name of the dimensions for your input and output tensors from the previous question. E.g. (batch, input channels, height, width)
7. Please specify the loss function that you would like to use.
8. Please, list the libraries complementary to standard PyTorch and/or TensorFlow distributions that you would need to train your model(s).
9. Please, list the key libraries that you would need for data preprocessing.