Instructions to use mlx-community/gemma-3-12b-pt-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mlx-community/gemma-3-12b-pt-bf16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="mlx-community/gemma-3-12b-pt-bf16")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("mlx-community/gemma-3-12b-pt-bf16") model = AutoModelForImageTextToText.from_pretrained("mlx-community/gemma-3-12b-pt-bf16") - MLX
How to use mlx-community/gemma-3-12b-pt-bf16 with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("mlx-community/gemma-3-12b-pt-bf16") config = load_config("mlx-community/gemma-3-12b-pt-bf16") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- vLLM
How to use mlx-community/gemma-3-12b-pt-bf16 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mlx-community/gemma-3-12b-pt-bf16" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mlx-community/gemma-3-12b-pt-bf16", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mlx-community/gemma-3-12b-pt-bf16
- SGLang
How to use mlx-community/gemma-3-12b-pt-bf16 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 "mlx-community/gemma-3-12b-pt-bf16" \ --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": "mlx-community/gemma-3-12b-pt-bf16", "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 "mlx-community/gemma-3-12b-pt-bf16" \ --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": "mlx-community/gemma-3-12b-pt-bf16", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use mlx-community/gemma-3-12b-pt-bf16 with Docker Model Runner:
docker model run hf.co/mlx-community/gemma-3-12b-pt-bf16
Upload folder using huggingface_hub
Browse files- README.md +1 -1
- generation_config.json +8 -0
- model.safetensors.index.json +0 -0
- processor_config.json +4 -0
README.md
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# mlx-community/gemma-3-12b-pt-bf16
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This model was converted to MLX format from [`google/gemma-3-12b-pt`]() using mlx-vlm version **0.1.
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Refer to the [original model card](https://huggingface.co/google/gemma-3-12b-pt) for more details on the model.
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## Use with mlx
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---
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# mlx-community/gemma-3-12b-pt-bf16
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This model was converted to MLX format from [`google/gemma-3-12b-pt`]() using mlx-vlm version **0.1.18**.
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Refer to the [original model card](https://huggingface.co/google/gemma-3-12b-pt) for more details on the model.
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## Use with mlx
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 2,
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"cache_implementation": "hybrid",
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"eos_token_id": 1,
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"pad_token_id": 0,
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"transformers_version": "4.50.0.dev0"
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}
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model.safetensors.index.json
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processor_config.json
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{
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"image_seq_length": 256,
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"processor_class": "Gemma3Processor"
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}
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