Instructions to use mrkwanzaa/functionalizer-100M-github-code-python-seed3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mrkwanzaa/functionalizer-100M-github-code-python-seed3 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-seed3")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mrkwanzaa/functionalizer-100M-github-code-python-seed3")
model = AutoModelForCausalLM.from_pretrained("mrkwanzaa/functionalizer-100M-github-code-python-seed3", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mrkwanzaa/functionalizer-100M-github-code-python-seed3 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-seed3"
# 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-seed3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mrkwanzaa/functionalizer-100M-github-code-python-seed3

SGLang

How to use mrkwanzaa/functionalizer-100M-github-code-python-seed3 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-seed3" \
    --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-seed3",
		"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-seed3" \
        --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-seed3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mrkwanzaa/functionalizer-100M-github-code-python-seed3 with Docker Model Runner:
```
docker model run hf.co/mrkwanzaa/functionalizer-100M-github-code-python-seed3
```

Fix metadata: remove arxiv_id, add pipeline tag and library name

by nielsr HF Staff - opened 1 day ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-4

nielsr

1 day ago

This PR improves the model card by:

Removing the arxiv field from the YAML metadata (the arxiv ID is already referenced in the markdown citation).
Adding library_name: transformers, since the model uses the GPT2LMHeadModel architecture and is compatible with the Transformers library.
Adding pipeline_tag: text-generation so the model is correctly categorized.
Adding a link to the paper in the markdown content.

Fix metadata: remove arxiv_id, add pipeline tag and library name9836600d

mrkwanzaa changed pull request status to merged about 20 hours ago

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment