SmolLM-1.7B-sft-160k – AI Model by rasyosef | AlphaNeural AI

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rasyosef/
SmolLM-1.7B-sft-160k

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HuggingFaceTB/SmolLM-1.7B

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SmolLM-1.7B-sft-160k

This model is a fine-tuned version of HuggingFaceTB/SmolLM-1.7B on the generator dataset. It achieves the following results on the evaluation set:

Loss: 1.1601
Model Preparation Time: 0.0348

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: 4e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 50
num_epochs: 1
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time
1.1547	0.0518	200	1.1745	0.0348
1.1416	0.1037	400	1.1734	0.0348
1.1334	0.1555	600	1.1720	0.0348
1.1415	0.2073	800	1.1710	0.0348
1.1399	0.2591	1000	1.1697	0.0348
1.1448	0.3110	1200	1.1685	0.0348
1.1429	0.3628	1400	1.1673	0.0348
1.1368	0.4146	1600	1.1665	0.0348
1.1309	0.4664	1800	1.1656	0.0348
1.1429	0.5183	2000	1.1646	0.0348
1.1474	0.5701	2200	1.1638	0.0348
1.1311	0.6219	2400	1.1633	0.0348
1.126	0.6737	2600	1.1625	0.0348
1.1356	0.7256	2800	1.1618	0.0348
1.1329	0.7774	3000	1.1613	0.0348
1.129	0.8292	3200	1.1610	0.0348
1.1347	0.8811	3400	1.1605	0.0348
1.1305	0.9329	3600	1.1602	0.0348
1.1278	0.9847	3800	1.1601	0.0348

Framework versions

PEFT 0.12.0
Transformers 4.44.0
Pytorch 2.4.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1