fr_wiki_mlm_42 Open-source Generative Model - Information Pending but Infinite Potential, Can Be Explored and Applied as Needed

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Fr Wiki Mlm 42

Developed by fpadovani

A generative model fine-tuned on an unknown dataset. Specific information about the training data and usage is missing.

Large Language Model

Transformers

#Language model fine-tuning #Linear learning rate scheduling #Large number of training steps

Downloads 42

Release Time : 4/7/2025

Model Overview

This model is a generative model trained using the transformers library and may be used for text generation tasks. Linear learning rate scheduling and the Adam optimizer were used during training.

Model Features

Linear learning rate scheduling

A linear learning rate scheduling strategy was used during training, including a warm-up phase of 40,000 steps.

Adam optimizer

Training was performed using the Adam optimizer with betas=(0.9, 0.999) and epsilon=1e-08.

Gradient accumulation

Gradient accumulation technology (steps = 2) was used to achieve a larger effective batch size (32).

Model Capabilities

Text generation

Use Cases

Text generation

General text generation

Can be used to generate various types of text content.

🚀 wiki_42

This is a fine - tuned model that addresses the need for specific data processing and analysis, providing high - quality results in relevant tasks.

🚀 Quick Start

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

Loss: 2.1322

📚 Documentation

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:

Property	Details
learning_rate	0.0001
train_batch_size	16
eval_batch_size	16
seed	42
gradient_accumulation_steps	2
total_train_batch_size	32
optimizer	Adam with betas=(0.9,0.999) and epsilon=1e - 08
lr_scheduler_type	linear
lr_scheduler_warmup_steps	40000
training_steps	100000

Training results

Training Loss	Epoch	Step	Validation Loss
No log	1.5662	2000	7.6634
7.6907	3.1323	4000	6.6436
7.6907	4.6985	6000	6.5194
6.5174	6.2647	8000	6.4353
6.5174	7.8309	10000	6.3850
6.3619	9.3970	12000	6.3224
6.3619	10.9632	14000	6.2452
6.2364	12.5294	16000	6.2350
6.2364	14.0955	18000	6.1499
6.1142	15.6617	20000	6.1037
6.1142	17.2279	22000	5.9377
5.8469	18.7940	24000	5.4239
5.8469	20.3602	26000	4.9251
4.9884	21.9264	28000	4.3860
4.9884	23.4926	30000	3.9695
4.0613	25.0587	32000	3.6959
4.0613	26.6249	34000	3.4763
3.5095	28.1911	36000	3.3151
3.5095	29.7572	38000	3.1737
3.1643	31.3234	40000	3.0445
3.1643	32.8896	42000	2.9430
2.9042	34.4558	44000	2.8326
2.9042	36.0219	46000	2.7730
2.6997	37.5881	48000	2.7106
2.6997	39.1543	50000	2.6288
2.5476	40.7204	52000	2.5801
2.5476	42.2866	54000	2.5604
2.4341	43.8528	56000	2.4778
2.4341	45.4190	58000	2.4722
2.3354	46.9851	60000	2.4329
2.3354	48.5513	62000	2.3689
2.2574	50.1175	64000	2.3834
2.2574	51.6836	66000	2.3362
2.1901	53.2498	68000	2.3154
2.1901	54.8160	70000	2.3113
2.1297	56.3821	72000	2.2685
2.1297	57.9483	74000	2.2540
2.0848	59.5145	76000	2.2351
2.0848	61.0807	78000	2.2405
2.0416	62.6468	80000	2.2016
2.0416	64.2130	82000	2.2054
2.004	65.7792	84000	2.1990
2.004	67.3453	86000	2.1745
1.9701	68.9115	88000	2.1781
1.9701	70.4777	90000	2.1825
1.9457	72.0439	92000	2.1373
1.9457	73.6100	94000	2.1161
1.9247	75.1762	96000	2.1504
1.9247	76.7424	98000	2.1297
1.9142	78.3085	100000	2.1322

Framework versions

Property	Details
Transformers	4.45.2
Pytorch	2.5.1+cu124
Datasets	3.0.1
Tokenizers	0.20.1

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