Model Details
Full Model IDgoogle/gemma-3-270m
Pipeline / Tasktext-generation
Librarytransformers
Downloads (all-time)6.0M
Likes1.0K
Last Modified8/14/2025
Author / Orggoogle
PrivateNo � public
⚡ Quick Usage (Python)
Using the 🤗 Transformers library. Install with pip install transformers
from transformers import pipeline
# Load the model
pipe = pipeline("text-generation", model="google/gemma-3-270m")
# Run inference
result = pipe("Your input here")
print(result)����� Tags
transformerssafetensorsgemma3gemmagoogletext-generationarxiv:2503.19786arxiv:1905.07830arxiv:1905.10044arxiv:1911.11641arxiv:1705.03551arxiv:1911.01547arxiv:1907.10641arxiv:2311.07911arxiv:2311.12022arxiv:2411.04368arxiv:1904.09728arxiv:1903.00161arxiv:2009.03300arxiv:2304.06364arxiv:2103.03874arxiv:2110.14168arxiv:2108.07732arxiv:2107.03374arxiv:2403.07974arxiv:2305.03111arxiv:2405.04520arxiv:2210.03057arxiv:2106.03193arxiv:1910.11856
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Access model files, inference API, and full documentation on Hugging Face.
Open on Hugging Face →Browse Model Files ↗�� Browse All Models���� Task: Text Generation
This model is designed for the Text Generation task. Explore more models for this use case.
All Text Generation Models →📊 Popularity
⬇ Downloads6.0M
����� Community Likes1.0K
🛠�� Requirements
- →Install: pip install transformers
- →Python 3.8+ recommended for Transformers.
- →GPU (CUDA) speeds up inference significantly.
- →Use model.half() for fp16 on limited VRAM.