Tried depth-upscaling Cosmo-1b by duplicating 6 layers, then LISA-training on a dataset reasonably similar to the original one in an attempt to 'self-repair'.
Not sure if it worked out exactly how I pictured but the nous eval's not overall much worse than the original at least.
(Took I think about 8 hours for, I want to say, ~80 million tokens on one RTX 3090?)
Thought about doing LORA first but I couldn't get peft_layers_to_transform working on axolotl and decided to go straight to LISA.
It's probably good(?) for (random selection of layers) to get experience trying to work around (random thick doubled layers) in some kind of brain-exercise sense anyway.
Capabilities assessment vs original and upscaled version:
This model is a fine-tuned version of
Lambent/cosmo-upscale
on the None dataset.
It achieves the following results on the evaluation set:
Loss: 1.0353
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: 0.0002
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 10
num_epochs: 1
Training results
Training Loss
Epoch
Step
Validation Loss
1.4298
0.0
1
1.4591
1.1229
0.25
1480
1.0594
1.0711
0.5
2960
1.0418
1.0511
0.75
4440
1.0353
Framework versions
Transformers 4.40.0.dev0
Pytorch 2.1.2+cu118
Datasets 2.18.0
Tokenizers 0.15.0
Runs of Lambent cosmo-upscale-lisa on huggingface.co
6
Total runs
0
24-hour runs
-1
3-day runs
-1
7-day runs
0
30-day runs
More Information About cosmo-upscale-lisa huggingface.co Model
cosmo-upscale-lisa huggingface.co
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