Instructions to use mrkwanzaa/functionalizer-100M-github-code-python-seed4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mrkwanzaa/functionalizer-100M-github-code-python-seed4 with Transformers:

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

pipe = pipeline("text-generation", model="mrkwanzaa/functionalizer-100M-github-code-python-seed4")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mrkwanzaa/functionalizer-100M-github-code-python-seed4")
model = AutoModelForCausalLM.from_pretrained("mrkwanzaa/functionalizer-100M-github-code-python-seed4", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mrkwanzaa/functionalizer-100M-github-code-python-seed4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mrkwanzaa/functionalizer-100M-github-code-python-seed4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mrkwanzaa/functionalizer-100M-github-code-python-seed4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mrkwanzaa/functionalizer-100M-github-code-python-seed4

SGLang

How to use mrkwanzaa/functionalizer-100M-github-code-python-seed4 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 "mrkwanzaa/functionalizer-100M-github-code-python-seed4" \
    --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": "mrkwanzaa/functionalizer-100M-github-code-python-seed4",
		"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 "mrkwanzaa/functionalizer-100M-github-code-python-seed4" \
        --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": "mrkwanzaa/functionalizer-100M-github-code-python-seed4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mrkwanzaa/functionalizer-100M-github-code-python-seed4 with Docker Model Runner:
```
docker model run hf.co/mrkwanzaa/functionalizer-100M-github-code-python-seed4
```

functionalizer-100M-github-code-python-seed4 / README.md

nielsr HF Staff

Fix metadata and add library name and pipeline tag

62e9412 verified 2 days ago

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	---
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- functionalizer
	- tokenizer
	- gpt2
	---

	## Functionalizer 100M model

	This is a 100M example model. Each model is trained on the dataset specified in the name for 50000 epochs with the given seed.

	The model was trained using the Functionalizer framework, presented in [The Functionalizer: Lossless Functional Decomposition for Subword Tokenization](https://huggingface.co/papers/2609.15991). Training code and detailed performance analysis are available: https://github.com/connor-makowski/functionalizer

	### Running the model

	To run the model, use the custom tokenizer fork available here: https://github.com/connor-makowski/tokenizers/tree/functionalizer

	### Citation

	If you find this model or the Functionalizer framework useful, please cite:

	```bibtex
	@misc{makowski2026functionalizerlosslessfunctionaldecomposition,
	title={The Functionalizer: Lossless Functional Decomposition for Subword Tokenization},
	author={Connor Makowski and Willem Guter},
	year={2026},
	eprint={2609.15991},
	archivePrefix={arXiv},
	primaryClass={cs.CL},
	url={https://arxiv.org/abs/2609.15991},
	}
	```