This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B-Instruct on the barc0/transduction_heavy_100k_jsonl and the barc0/transduction_heavy_suggestfunction_100k_jsonl datasets. It achieves the following results on the evaluation set:

Loss: 0.0319

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: 1e-05
train_batch_size: 8
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 2
total_train_batch_size: 128
total_eval_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
0.0446	1.0	1478	0.0433
0.0229	2.0	2956	0.0323
0.014	3.0	4434	0.0319

Framework versions

Transformers 4.45.0.dev0
Pytorch 2.4.0+cu121
Datasets 3.0.2
Tokenizers 0.19.1

RichardErkhov/barc0_-_Llama-3.1-ARC-Heavy-Transduction-8B-8bits

Original model description:

heavy-barc-llama3.1-8b-ins-fft-transduction_lr1e-5_epoch3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

RichardErkhov/
barc0_-_Llama-3.1-ARC-Heavy-Transduction-8B-8bits