Instructions to use marco-molinari/python-code-millenials-1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use marco-molinari/python-code-millenials-1b with Transformers:

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

pipe = pipeline("text-generation", model="marco-molinari/python-code-millenials-1b", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("marco-molinari/python-code-millenials-1b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("marco-molinari/python-code-millenials-1b", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use marco-molinari/python-code-millenials-1b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "marco-molinari/python-code-millenials-1b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "marco-molinari/python-code-millenials-1b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/marco-molinari/python-code-millenials-1b

SGLang

How to use marco-molinari/python-code-millenials-1b 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 "marco-molinari/python-code-millenials-1b" \
    --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": "marco-molinari/python-code-millenials-1b",
		"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 "marco-molinari/python-code-millenials-1b" \
        --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": "marco-molinari/python-code-millenials-1b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use marco-molinari/python-code-millenials-1b with Docker Model Runner:
```
docker model run hf.co/marco-molinari/python-code-millenials-1b
```

python-code-millenials-1b

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---
library_name: transformers
tags: []
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

### Model Description

I fine tune: [code-millenials-1b](https://huggingface.co/budecosystem/code-millenials-1b) on the provided dataset. The model is good at conding and small enough to allow portability, but not trained on python specifically. I fine tune on python.

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** Marco Molinari
- **Language(s) (NLP):** Python
- **Finetuned from model [optional]:** code-millenials-1b

### Model Sources [optional]

## Uses
Light weight python coding

### Training Data
https://huggingface.co/datasets/ArtifactAI/arxiv_python_research_code