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codellama-infilling-mojo – AI Model by annaluiza | AlphaNeural AI
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codellama-infilling-mojo
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peft
safetensors
trl
sft
generated_from_trainer
meta-llama/CodeLlama-7b-hf
adapter
llama2
us
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codellama-infilling-mojo
This model is a fine-tuned version of
meta-llama/CodeLlama-7b-hf
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 1.0794
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: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: constant
lr_scheduler_warmup_ratio: 0.03
num_epochs: 5
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
1.6816
1.0
50
1.2632
1.0669
2.0
100
1.0566
0.7663
3.0
150
1.0003
0.5906
4.0
200
1.0155
0.4724
5.0
250
1.0794
Framework versions
PEFT 0.11.2.dev0
Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.19.2
Tokenizers 0.19.1
