Instructions to use Mayko2995/gpt2-quote-fr-v1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mayko2995/gpt2-quote-fr-v1.1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Mayko2995/gpt2-quote-fr-v1.1")
model = AutoModelForCausalLM.from_pretrained("Mayko2995/gpt2-quote-fr-v1.1")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Mayko2995/gpt2-quote-fr-v1.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mayko2995/gpt2-quote-fr-v1.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mayko2995/gpt2-quote-fr-v1.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Mayko2995/gpt2-quote-fr-v1.1

SGLang

How to use Mayko2995/gpt2-quote-fr-v1.1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Mayko2995/gpt2-quote-fr-v1.1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mayko2995/gpt2-quote-fr-v1.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Mayko2995/gpt2-quote-fr-v1.1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mayko2995/gpt2-quote-fr-v1.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Mayko2995/gpt2-quote-fr-v1.1 with Docker Model Runner:
```
docker model run hf.co/Mayko2995/gpt2-quote-fr-v1.1
```

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

Downloads last month: 4

Safetensors

Model size

0.2B params

Tensor type

F32

Model tree for Mayko2995/gpt2-quote-fr-v1.1

Base model

asi/gpt-fr-cased-small

Finetuned

(4)

this model

Mayko2995
/

gpt2-quote-fr-v1.1