Image-Text-to-Text
MLX
Safetensors
gemma4
lemma
8bit
apple-silicon
multimodal
on-device
conversational
Instructions to use lthn/lemmy-mlx-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use lthn/lemmy-mlx-8bit 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("lthn/lemmy-mlx-8bit") config = load_config("lthn/lemmy-mlx-8bit") # 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
- LM Studio
- Pi new
How to use lthn/lemmy-mlx-8bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "lthn/lemmy-mlx-8bit"
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": "lthn/lemmy-mlx-8bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use lthn/lemmy-mlx-8bit 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 "lthn/lemmy-mlx-8bit"
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 lthn/lemmy-mlx-8bit
Run Hermes
hermes
Upload processor_config.json with huggingface_hub
Browse files- processor_config.json +75 -0
processor_config.json
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{
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"audio_ms_per_token": 40,
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"audio_seq_length": 750,
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"feature_extractor": {
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"dither": 0.0,
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"feature_extractor_type": "Gemma4AudioFeatureExtractor",
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"feature_size": 128,
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"fft_length": 512,
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"fft_overdrive": false,
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"frame_length": 320,
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"hop_length": 160,
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"input_scale_factor": 1.0,
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"max_frequency": 8000.0,
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"mel_floor": 0.001,
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"min_frequency": 0.0,
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"padding_side": "right",
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"padding_value": 0.0,
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"per_bin_mean": null,
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"per_bin_stddev": null,
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"preemphasis": 0.0,
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"preemphasis_htk_flavor": true,
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"return_attention_mask": true,
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"sampling_rate": 16000
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},
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"image_processor": {
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"do_convert_rgb": true,
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"do_normalize": false,
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"do_rescale": true,
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"do_resize": true,
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"image_mean": [
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0.0,
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0.0,
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0.0
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],
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"image_processor_type": "Gemma4ImageProcessor",
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"image_seq_length": 280,
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"image_std": [
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1.0,
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1.0,
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1.0
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],
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"max_soft_tokens": 280,
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"patch_size": 16,
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"pooling_kernel_size": 3,
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"resample": 3,
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"rescale_factor": 0.00392156862745098
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},
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"image_seq_length": 280,
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"processor_class": "Gemma4Processor",
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"video_processor": {
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"do_convert_rgb": true,
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"do_normalize": true,
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"do_rescale": true,
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"do_resize": true,
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"do_sample_frames": true,
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"image_mean": [
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0.0,
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0.0,
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0.0
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],
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"image_std": [
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1.0,
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1.0,
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1.0
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],
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"max_soft_tokens": 70,
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"num_frames": 32,
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"patch_size": 16,
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"pooling_kernel_size": 3,
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"resample": 3,
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"rescale_factor": 0.00392156862745098,
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"return_metadata": false,
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"video_processor_type": "Gemma4VideoProcessor"
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}
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}
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