Model Details
Full Model IDchbae624/vllm-translategemma-12b-it
Pipeline / Tasktext-generation
Librarytransformers
Downloads (all-time)174.4K
Likes4
Last Modified1/29/2026
Author / Orgchbae624
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="chbae624/vllm-translategemma-12b-it")
# Run inference
result = pipe("Your input here")
print(result)🏷️ Tags
transformerssafetensorsgemma3image-text-to-texttranslationvllmtranslategemmatext-generationconversationalmultilingualarxiv:2601.09012base_model:google/translategemma-12b-itbase_model:finetune:google/translategemma-12b-itlicense:gemmatext-generation-inferenceendpoints_compatibleregion:us
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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
⬇ Downloads174.4K
❤️ Community Likes4
🛠️ 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.