Instructions to use kdf/python-docstring-generation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kdf/python-docstring-generation with Transformers:

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

pipe = pipeline("text-generation", model="kdf/python-docstring-generation")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kdf/python-docstring-generation")
model = AutoModelForCausalLM.from_pretrained("kdf/python-docstring-generation")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use kdf/python-docstring-generation with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/kdf/python-docstring-generation

SGLang

How to use kdf/python-docstring-generation 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 "kdf/python-docstring-generation" \
    --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": "kdf/python-docstring-generation",
		"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 "kdf/python-docstring-generation" \
        --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": "kdf/python-docstring-generation",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kdf/python-docstring-generation with Docker Model Runner:
```
docker model run hf.co/kdf/python-docstring-generation
```

python-docstring-generation

File size: 1,080 Bytes

8caa0be

{
  "\t\t": 50294,
  "\t\t\t": 50293,
  "\t\t\t\t": 50292,
  "\t\t\t\t\t": 50291,
  "\t\t\t\t\t\t": 50290,
  "\t\t\t\t\t\t\t": 50289,
  "\t\t\t\t\t\t\t\t": 50288,
  "\t\t\t\t\t\t\t\t\t": 50287,
  "  ": 50286,
  "   ": 50285,
  "    ": 50284,
  "     ": 50283,
  "      ": 50282,
  "       ": 50281,
  "        ": 50280,
  "         ": 50279,
  "          ": 50278,
  "           ": 50277,
  "            ": 50276,
  "             ": 50275,
  "              ": 50274,
  "               ": 50273,
  "                ": 50272,
  "                 ": 50271,
  "                  ": 50270,
  "                   ": 50269,
  "                    ": 50268,
  "                     ": 50267,
  "                      ": 50266,
  "                       ": 50265,
  "                        ": 50264,
  "                         ": 50263,
  "                          ": 50262,
  "                           ": 50261,
  "                            ": 50260,
  "                             ": 50259,
  "                              ": 50258,
  "                               ": 50257
}