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 / opt_state.msgpack

yhavinga

Saving weights and log at step 190000

226db97 over 4 years ago

download

history blame contribute delete

1.27 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 96d0995422fa1ea11f1cdffba46ada96187d4bc42cdb622402d99366af9608c4
Size of remote file:: 1.27 MB
SHA256:: 793d0932c9f1969f2197b5e26ac26fe77af2216ee8ff27803fa33dfd75d86993

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.