Instructions to use yhavinga/gpt-neo-125M-dutch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yhavinga/gpt-neo-125M-dutch with Transformers:

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

pipe = pipeline("text-generation", model="yhavinga/gpt-neo-125M-dutch")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("yhavinga/gpt-neo-125M-dutch")
model = AutoModelForMultimodalLM.from_pretrained("yhavinga/gpt-neo-125M-dutch")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yhavinga/gpt-neo-125M-dutch with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yhavinga/gpt-neo-125M-dutch"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yhavinga/gpt-neo-125M-dutch",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/yhavinga/gpt-neo-125M-dutch

SGLang

How to use yhavinga/gpt-neo-125M-dutch 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 "yhavinga/gpt-neo-125M-dutch" \
    --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": "yhavinga/gpt-neo-125M-dutch",
		"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 "yhavinga/gpt-neo-125M-dutch" \
        --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": "yhavinga/gpt-neo-125M-dutch",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use yhavinga/gpt-neo-125M-dutch with Docker Model Runner:
```
docker model run hf.co/yhavinga/gpt-neo-125M-dutch
```

gpt-neo-125M-dutch

File size: 939 Bytes

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from transformers import GPTNeoConfig

import os

# export DATASET="${HOME}/data/nedd_wiki_news/nedd_wiki_news.py" # Name of the dataset in the Huggingface Hub
# export DATASET_CONFIG="nedd_nl" # Config of the dataset in the Huggingface Hub
# export DATASET_SPLIT="train" # Split to use for training tokenizer and model
# export VOCAB_SIZE="50257"
# export MODEL_PATH="${HOME}/data/${HF_PROJECT}" # Path to the model, e.g. here inside the mount
# export CONFIG_TYPE="gpt2-medium" # Config that our model will use

config_type = os.environ.get("CONFIG_TYPE")
dataset_name = os.environ.get("DATASET")
dataset_config = os.environ.get("DATASET_CONFIG")
dataset_split = os.environ.get("DATASET_SPLIT")
vocab_size = int(os.environ.get("VOCAB_SIZE"))
model_path = os.environ.get("MODEL_PATH")


config = GPTNeoConfig.from_pretrained(config_type, embed_dropout=0.0, attention_dropout=0.0, vocab_size=vocab_size)
config.save_pretrained(model_path)