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almo-OLMoE-1B-7B-0924-wglobalcopy-b3-layerbalancing-nopeft

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Model description

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Intended uses & limitations

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Training and evaluation data

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Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 8
total_train_batch_size: 128
total_eval_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 100
training_steps: 3000

Training results

Table with columns: Training Loss, Epoch, Step, Validation Loss, Ppl, Active (gib), Allocated (gib), Reserved (gib)
Training Loss	Epoch	Step	Validation Loss	Ppl	Active (gib)	Allocated (gib)	Reserved (gib)
No log	0	0	3.9875	53.9181	21.2	21.2	21.58
2.8675	0.0057	50	2.6445

Framework versions

Transformers 5.5.0
Pytorch 2.8.0+cu129
Datasets 4.5.0
Tokenizers 0.22.2

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Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 8
total_train_batch_size: 128
total_eval_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 100
training_steps: 3000

Training results

Table with columns: Training Loss, Epoch, Step, Validation Loss, Ppl, Active (gib), Allocated (gib), Reserved (gib)
Training Loss	Epoch	Step	Validation Loss	Ppl	Active (gib)	Allocated (gib)	Reserved (gib)
No log	0	0	3.9875	53.9181	21.2	21.2	21.58
2.8675	0.0057	50	2.6445

Framework versions

Transformers 5.5.0
Pytorch 2.8.0+cu129
Datasets 4.5.0
Tokenizers 0.22.2

almo-OLMoE-1B-7B-0924-wglobalcopy-b3-layerbalancing-nopeft

Get help setting up a custom Dedicated Endpoints.

README

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Explore FriendliAI today

README

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions