Instructions to use neulab/gpt2-large-finetuned-wikitext103 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use neulab/gpt2-large-finetuned-wikitext103 with Transformers:

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

pipe = pipeline("text-generation", model="neulab/gpt2-large-finetuned-wikitext103")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("neulab/gpt2-large-finetuned-wikitext103")
model = AutoModelForCausalLM.from_pretrained("neulab/gpt2-large-finetuned-wikitext103")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use neulab/gpt2-large-finetuned-wikitext103 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "neulab/gpt2-large-finetuned-wikitext103"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "neulab/gpt2-large-finetuned-wikitext103",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/neulab/gpt2-large-finetuned-wikitext103

SGLang

How to use neulab/gpt2-large-finetuned-wikitext103 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 "neulab/gpt2-large-finetuned-wikitext103" \
    --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": "neulab/gpt2-large-finetuned-wikitext103",
		"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 "neulab/gpt2-large-finetuned-wikitext103" \
        --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": "neulab/gpt2-large-finetuned-wikitext103",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use neulab/gpt2-large-finetuned-wikitext103 with Docker Model Runner:
```
docker model run hf.co/neulab/gpt2-large-finetuned-wikitext103
```

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Check out the documentation for more information.

This is a gpt2-large model, finetuned on the Wikitext-103 dataset.

It achieves a perplexity of 10.56 using a "sliding window" context, using the run_clm.py script at https://github.com/neulab/knn-transformers.

Base LM:	`distilgpt2`	`gpt2`
base perplexity	18.25	14.84
+ kNN-LM	15.03	12.57
+ RetoMaton	14.70	12.46

This model was released as part of the paper "Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval" (ICML'2022).

For more information, see: https://github.com/neulab/knn-transformers

If you use this model, please cite:

@inproceedings{alon2022neuro,
  title={Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval},
  author={Alon, Uri and Xu, Frank and He, Junxian and Sengupta, Sudipta and Roth, Dan and Neubig, Graham},
  booktitle={International Conference on Machine Learning},
  pages={468--485},
  year={2022},
  organization={PMLR}
}

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Paper for neulab/gpt2-large-finetuned-wikitext103

Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval

Paper • 2201.12431 • Published Jan 28, 2022