Instructions to use mrkwanzaa/functionalizer-100M-github-code-python-seed1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mrkwanzaa/functionalizer-100M-github-code-python-seed1 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-seed1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mrkwanzaa/functionalizer-100M-github-code-python-seed1")
model = AutoModelForCausalLM.from_pretrained("mrkwanzaa/functionalizer-100M-github-code-python-seed1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mrkwanzaa/functionalizer-100M-github-code-python-seed1 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-seed1"
# 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-seed1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mrkwanzaa/functionalizer-100M-github-code-python-seed1

SGLang

How to use mrkwanzaa/functionalizer-100M-github-code-python-seed1 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-seed1" \
    --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-seed1",
		"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-seed1" \
        --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-seed1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mrkwanzaa/functionalizer-100M-github-code-python-seed1 with Docker Model Runner:
```
docker model run hf.co/mrkwanzaa/functionalizer-100M-github-code-python-seed1
```

nielsr HF Staff commited on 1 day ago

Commit

677cd25

verified ·

1 Parent(s): 2652292

Fix metadata, add pipeline tag and library name

Browse files

This PR improves the model card's metadata by:

- removing the `arxiv` field from the YAML metadata, since it belongs in the markdown content rather than the metadata
- adding `pipeline_tag: text-generation` so the model can be found under the text-generation pipeline
- adding `library_name: transformers` to enable the automated Transformers usage snippet
- linking to the paper on the Hugging Face Papers page

Files changed (1) hide show

README.md +7 -4

README.md CHANGED Viewed

@@ -1,11 +1,14 @@
 ---
-arxiv: 2609.15991
 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.

 ---
 tags:
+- functionalizer
+- tokenizer
+- gpt2
+library_name: transformers
+pipeline_tag: text-generation
 ---
+This model was presented in the paper [The Functionalizer: Lossless Functional Decomposition for Subword Tokenization](https://huggingface.co/papers/2609.15991).
 ## 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.