Instructions to use nthngdy/headless-pythia-owt2-70m-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nthngdy/headless-pythia-owt2-70m-ft with Transformers:

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

pipe = pipeline("text-generation", model="nthngdy/headless-pythia-owt2-70m-ft")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nthngdy/headless-pythia-owt2-70m-ft")
model = AutoModelForCausalLM.from_pretrained("nthngdy/headless-pythia-owt2-70m-ft")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use nthngdy/headless-pythia-owt2-70m-ft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nthngdy/headless-pythia-owt2-70m-ft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nthngdy/headless-pythia-owt2-70m-ft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/nthngdy/headless-pythia-owt2-70m-ft

SGLang

How to use nthngdy/headless-pythia-owt2-70m-ft 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 "nthngdy/headless-pythia-owt2-70m-ft" \
    --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": "nthngdy/headless-pythia-owt2-70m-ft",
		"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 "nthngdy/headless-pythia-owt2-70m-ft" \
        --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": "nthngdy/headless-pythia-owt2-70m-ft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use nthngdy/headless-pythia-owt2-70m-ft with Docker Model Runner:
```
docker model run hf.co/nthngdy/headless-pythia-owt2-70m-ft
```

Model Sources

Repository: https://github.com/NathanGodey/headless-lm
Paper: https://arxiv.org/abs/2309.08351

Model Architecture and Objective

This model is a Pythia-70m architecture trained on OpenWebText-2 using the Contrastive Weight Tying objective, and briefly fine-tuned for language generation on the same dataset.

Citation

BibTeX:

@misc{godey2023headless,
      title={Headless Language Models: Learning without Predicting with Contrastive Weight Tying}, 
      author={Nathan Godey and Éric de la Clergerie and Benoît Sagot},
      year={2023},
      eprint={2309.08351},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Contact

nathan.godey@inria.fr

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Dataset used to train nthngdy/headless-pythia-owt2-70m-ft

Paper for nthngdy/headless-pythia-owt2-70m-ft

Headless Language Models: Learning without Predicting with Contrastive Weight Tying

Paper • 2309.08351 • Published Sep 15, 2023 • 3