Llama3.2-1B-QLoRA-Explainer – AI Model by Dinesh2001 | AlphaNeural AI

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Dinesh2001/
Llama3.2-1B-QLoRA-Explainer

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text-generation

meta-llama/Llama-3.2-1B-Instruct

endpoints_compatible

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Llama3.2-1B-QLoRA-Explainer

This model is a fine-tuned version of meta-llama/Llama-3.2-1B-Instruct on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0579

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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 50
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
0.0652	0.3556	200	0.0650
0.0626	0.7111	400	0.0615
0.06	1.0658	600	0.0596
0.0596	1.4213	800	0.0591
0.0588	1.7769	1000	0.0587
0.0582	2.1316	1200	0.0584
0.0581	2.4871	1400	0.0583
0.0576	2.8427	1600	0.0579

Framework versions

PEFT 0.17.0
Transformers 4.55.2
Pytorch 2.8.0+cu128
Datasets 4.0.0
Tokenizers 0.21.4