gpt2-quote-fr-v1.1/README.md

library_name, license, base_model, tags, model-index, pipeline_tag

library_name	license	base_model	tags	model-index	pipeline_tag
transformers	apache-2.0	asi/gpt-fr-cased-small	generated_from_trainer	name results gpt2-quote-fr-v1.1	text-generation

GPT-2 French Quote Generator 🇫🇷

A fine-tuned GPT-2 model for generating philosophical and inspirational quotes in French.

Model Description

This model is based on asi/gpt-fr-cased-small and has been fine-tuned on a dataset of 975 carefully collected French quotes covering various philosophical themes.

Themes Covered

• ❤️ Love (Amour)
• 🌱 Life (Vie)
• 🌌 Existence
• 😊 Happiness (Bonheur)
• 🦉 Wisdom (Sagesse)
• ⏳ Time (Temps)
• 🕊️ Freedom (Liberté)
• 🙏 Spirituality (Spiritualité)

Usage

from transformers import pipeline

generator = pipeline(
    "text-generation",
    model="Mayko2995/gpt2-quote-fr-v1.1"
)

result = generator(
    "Le bonheur est",
    max_new_tokens=20,
    temperature=0.8,
    do_sample=True,
    repetition_penalty=2.0,
    no_repeat_ngram_size=3
)

print(result[0]["generated_text"])

Generated Examples

"Le bonheur est une petite chose que l'on invente 
 avec la sagesse de chaque instant."

"Dans l'existence, il faut avoir une âme pour 
 comprendre que tout existe."

"La vie est un voyage vers son avenir qu'il nous 
 faudra parcourir avec sagesse et humilité."

Dataset

• 975 French quotes
• Collected from Wikiquote and other sources
• Various authors including Victor Hugo, Albert Camus, Pablo Neruda, Einstein and others

Limitations

• Relatively small dataset — 975 quotes
• May generate incomplete sentences
• Best results with max_new_tokens between 15 and 25
• Designed for French language only

Intended Uses

✅ Generating inspirational French quotes ✅ Creative writing assistance ✅ Educational NLP projects ✅ French language generation research

Out of Scope

❌ Factual information generation ❌ Translation tasks ❌ Other languages

Author

Jonasson Claubert Joachin — Self-taught AI Engineer 🇭🇹 Based in Haiti — passionate about NLP and LLMs

Building AI tools adapted to the Haitian context

Versions

Version	Dataset	Val Loss
v1.0	300 quotes	1.12
v1.1	975 quotes	0.71 ✅

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 2
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 8
• 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
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss
1.5437	1.0	49	1.2013
1.0311	2.0	98	0.9540
0.8106	3.0	147	0.8370
0.7146	4.0	196	0.7859
0.6868	5.0	245	0.7580
0.5707	6.0	294	0.7380
0.5532	7.0	343	0.7201
0.5705	8.0	392	0.7145
0.5311	9.0	441	0.7129
0.5057	10.0	490	0.7113

Framework versions

• Transformers 5.0.0
• Pytorch 2.10.0+cu128
• Datasets 4.0.0
• Tokenizers 0.22.2