Instructions to use mlx-community/translategemma-27b-it-6bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/translategemma-27b-it-6bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir translategemma-27b-it-6bit mlx-community/translategemma-27b-it-6bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
mlx-community/translategemma-27b-it-6bit
This model mlx-community/translategemma-27b-it-6bit was converted to MLX format from google/translategemma-27b-it (via mlx-community/translategemma-27b-it-bf16) using mlx-lm version 0.30.4.
6-bit (6.50 bits per weight, 22.0 GB) targets 32 GB unified-memory Macs: the 8-bit conversion needs more memory than a 32 GB machine can dedicate to inference, while 6-bit affine quantization stays close to 8-bit output quality. For smaller machines see the 4-bit conversion.
Model Description
TranslateGemma 27B is Google's translation-specialized Gemma 3 model supporting 55 languages. It uses a structured translation message rendered through the model's chat template.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/translategemma-27b-it-6bit")
messages = [{
"role": "user",
"content": [{
"type": "text",
"source_lang_code": "en",
"target_lang_code": "pl",
"text": "The city council approved the new bike path along the river.",
}],
}]
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
License
Gemma is provided under and subject to the Gemma Terms of Use found at ai.google.dev/gemma/terms.
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6-bit
Model tree for mlx-community/translategemma-27b-it-6bit
Base model
google/translategemma-27b-it