deqing_-_llama-3.2-350M-fourier-awq – AI Model by RichardErkhov | AlphaNeural AI

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deqing_-_llama-3.2-350M-fourier-awq

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llama-3.2-350M-fourier - AWQ

Model creator: https://huggingface.co/deqing/
Original model: https://huggingface.co/deqing/llama-3.2-350M-fourier/

Original model description:

library_name: transformers base_model: llama_small_config.json tags:

generated_from_trainer model-index:
name: llama-3.2-350M-fourier results: []

llama-3.2-350M-fourier

This model is a fine-tuned version of llama_small_config.json on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 3.7984

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: 0.0005
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 256
optimizer: Use adamw_torch 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: 1000
num_epochs: 1
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
6.2583	0.0754	1000	5.1105
4.7487	0.1508	2000	4.5762
4.4098	0.2262	3000	4.3661
4.2531	0.3016	4000	4.2368
4.1448	0.3770	5000	4.1446
4.0569	0.4524	6000	4.0733
3.9844	0.5278	7000	4.0040
3.9199	0.6032	8000	3.9439
3.8622	0.6786	9000	3.8956
3.8158	0.7541	10000	3.8521
3.7792	0.8295	11000	3.8213
3.7546	0.9049	12000	3.8034
3.7425	0.9803	13000	3.7984

Framework versions

Transformers 4.46.2
Pytorch 2.1.2+cu121
Datasets 3.1.0
Tokenizers 0.20.1