This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B on the llama-duo/synth_coding_dataset_dedup dataset. It achieves the following results on the evaluation set:

Loss: 1.5174

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: 0.002
train_batch_size: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 2
total_train_batch_size: 128
total_eval_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss
0.4861	1.0	135	1.2495
0.458	2.0	270	1.2390
0.4423	3.0	405	1.2549
0.4244	4.0	540	1.2665
0.4051	5.0	675	1.2714
0.3815	6.0	810	1.2959
0.3546	7.0	945	1.3560
0.3233	8.0	1080	1.4125
0.2969	9.0	1215	1.4809
0.2818	10.0	1350	1.5174

Framework versions

PEFT 0.12.0
Transformers 4.44.0
Pytorch 2.2.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1

llama-duo/llama3-8b-coding-gpt4o-100k

llama3-8b-coding-gpt4o-100k

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

llama-duo/
llama3-8b-coding-gpt4o-100k