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
- Transformers
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 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. 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:
@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},
}
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docker model run hf.co/mrkwanzaa/functionalizer-100M-github-code-python-seed4