Text Generation
Safetensors
MLX
English
mlx-node
gemma4
quantized
awq
mxfp8
micro-scaling-fp
Mixture of Experts
sliding-window-attention
vision-language
apple-silicon
unsloth-dynamic
conversational
8-bit precision
Instructions to use Brooooooklyn/Gemma-4-26B-A4B-IT-UD-MXFP8_K_XL-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Brooooooklyn/Gemma-4-26B-A4B-IT-UD-MXFP8_K_XL-mlx with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("Brooooooklyn/Gemma-4-26B-A4B-IT-UD-MXFP8_K_XL-mlx") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use Brooooooklyn/Gemma-4-26B-A4B-IT-UD-MXFP8_K_XL-mlx with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Brooooooklyn/Gemma-4-26B-A4B-IT-UD-MXFP8_K_XL-mlx"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Brooooooklyn/Gemma-4-26B-A4B-IT-UD-MXFP8_K_XL-mlx" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Brooooooklyn/Gemma-4-26B-A4B-IT-UD-MXFP8_K_XL-mlx with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "Brooooooklyn/Gemma-4-26B-A4B-IT-UD-MXFP8_K_XL-mlx"
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default Brooooooklyn/Gemma-4-26B-A4B-IT-UD-MXFP8_K_XL-mlx
Run Hermes
hermes
- MLX LM
How to use Brooooooklyn/Gemma-4-26B-A4B-IT-UD-MXFP8_K_XL-mlx with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "Brooooooklyn/Gemma-4-26B-A4B-IT-UD-MXFP8_K_XL-mlx"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "Brooooooklyn/Gemma-4-26B-A4B-IT-UD-MXFP8_K_XL-mlx" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Brooooooklyn/Gemma-4-26B-A4B-IT-UD-MXFP8_K_XL-mlx", "messages": [ {"role": "user", "content": "Hello"} ] }'
| { | |
| "audio_ms_per_token": 40, | |
| "audio_seq_length": 750, | |
| "feature_extractor": { | |
| "dither": 0.0, | |
| "feature_extractor_type": "Gemma4AudioFeatureExtractor", | |
| "feature_size": 128, | |
| "fft_length": 512, | |
| "fft_overdrive": false, | |
| "frame_length": 320, | |
| "hop_length": 160, | |
| "input_scale_factor": 1.0, | |
| "max_frequency": 8000.0, | |
| "mel_floor": 0.001, | |
| "min_frequency": 0.0, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "per_bin_mean": null, | |
| "per_bin_stddev": null, | |
| "preemphasis": 0.0, | |
| "preemphasis_htk_flavor": true, | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000 | |
| }, | |
| "image_processor": { | |
| "do_convert_rgb": true, | |
| "do_normalize": false, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.0, | |
| 0.0, | |
| 0.0 | |
| ], | |
| "image_processor_type": "Gemma4ImageProcessor", | |
| "image_seq_length": 280, | |
| "image_std": [ | |
| 1.0, | |
| 1.0, | |
| 1.0 | |
| ], | |
| "max_soft_tokens": 280, | |
| "patch_size": 16, | |
| "pooling_kernel_size": 3, | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098 | |
| }, | |
| "image_seq_length": 280, | |
| "processor_class": "Gemma4Processor", | |
| "video_processor": { | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "do_sample_frames": true, | |
| "image_mean": [ | |
| 0.0, | |
| 0.0, | |
| 0.0 | |
| ], | |
| "image_std": [ | |
| 1.0, | |
| 1.0, | |
| 1.0 | |
| ], | |
| "max_soft_tokens": 70, | |
| "num_frames": 32, | |
| "patch_size": 16, | |
| "pooling_kernel_size": 3, | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "return_metadata": false, | |
| "video_processor_type": "Gemma4VideoProcessor" | |
| } | |
| } | |