mistral_darulm_20_05_24_part1-2_32000_unigram_part1_lr1e4_bs256 – AI Model by RefalMachine | AlphaNeural AI

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RefalMachine/
mistral_darulm_20_05_24_part1-2_32000_unigram_part1_lr1e4_bs256

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text-generation

generated_from_trainer

RefalMachine/mistral_darulm_20_05_24_part1-2_32000_unigram_mean_init_03_07_24

autotrain_compatible

text-generation-inference

endpoints_compatible

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mistral_darulm_20_05_24_part1-2_32000_unigram_part1_lr1e4_bs256

This model is a fine-tuned version of RefalMachine/mistral_darulm_20_05_24_part1-2_32000_unigram_mean_init_03_07_24 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 2.0924
Accuracy: 0.5528

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: 0.0001
train_batch_size: 4
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 32
total_train_batch_size: 128
total_eval_batch_size: 128
optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 100
num_epochs: 1.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.4483	0.09	2000	2.2346	0.5327
2.3981	0.18	4000	2.1935	0.5381
2.3458	0.26	6000	2.1723	0.5408
2.3539	0.35	8000	2.1518	0.5437
2.3637	0.44	10000	2.1349	0.5463
2.3438	0.53	12000	2.1194	0.5487
2.283	0.61	14000	2.1059	0.5507
2.3057	0.7	16000	2.0977	0.5520
2.2765	0.79	18000	2.0937	0.5526
2.2652	0.88	20000	2.0925	0.5528
2.2647	0.96	22000	2.0924	0.5529

Framework versions

Transformers 4.37.2
Pytorch 2.3.0a0+6ddf5cf85e.nv24.04
Datasets 2.18.0
Tokenizers 0.15.2