Instructions to use crossroderick/occitan-distilgpt2-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use crossroderick/occitan-distilgpt2-medium with Transformers:

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

pipe = pipeline("text-generation", model="crossroderick/occitan-distilgpt2-medium")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("crossroderick/occitan-distilgpt2-medium")
model = AutoModelForCausalLM.from_pretrained("crossroderick/occitan-distilgpt2-medium")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use crossroderick/occitan-distilgpt2-medium with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "crossroderick/occitan-distilgpt2-medium"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "crossroderick/occitan-distilgpt2-medium",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/crossroderick/occitan-distilgpt2-medium

SGLang

How to use crossroderick/occitan-distilgpt2-medium 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 "crossroderick/occitan-distilgpt2-medium" \
    --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": "crossroderick/occitan-distilgpt2-medium",
		"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 "crossroderick/occitan-distilgpt2-medium" \
        --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": "crossroderick/occitan-distilgpt2-medium",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use crossroderick/occitan-distilgpt2-medium with Docker Model Runner:
```
docker model run hf.co/crossroderick/occitan-distilgpt2-medium
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

occitan-distilgpt2-medium

Adieusatz brave monde!

This model is a fine-tuned version of distilgpt2 on the Occitan language section of the Wikipedia dataset, and is part of a personal effort to study the Occitan language in an NLP environment. It achieves the following results on the evaluation set:

Loss: 2.0163

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: 5e-05
train_batch_size: 12
eval_batch_size: 12
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 48
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss
2.1497	1.0	854	2.0202
2.1145	2.0	1708	2.0195
2.097	3.0	2562	2.0163

Framework versions

Transformers 4.38.2
Pytorch 2.1.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

Downloads last month: 6

Safetensors

Model size

81.9M params

Tensor type

F32

Model tree for crossroderick/occitan-distilgpt2-medium

Unable to build the model tree, the base model loops to the model itself. Learn more.