Image-Text-to-Text
MLX
English
inkling_mm_model
quantized
conversational
audio-text-to-text
Mixture of Experts
Instructions to use inferencerlabs/Inkling-Small-MLX-Q9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use inferencerlabs/Inkling-Small-MLX-Q9 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("inferencerlabs/Inkling-Small-MLX-Q9") config = load_config("inferencerlabs/Inkling-Small-MLX-Q9") # 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
- Pi
How to use inferencerlabs/Inkling-Small-MLX-Q9 with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "inferencerlabs/Inkling-Small-MLX-Q9"
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "inferencerlabs/Inkling-Small-MLX-Q9" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use inferencerlabs/Inkling-Small-MLX-Q9 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 "inferencerlabs/Inkling-Small-MLX-Q9"
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 inferencerlabs/Inkling-Small-MLX-Q9
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use inferencerlabs/Inkling-Small-MLX-Q9 with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "inferencerlabs/Inkling-Small-MLX-Q9"
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "inferencerlabs/Inkling-Small-MLX-Q9" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Upload model file
Browse files- processor_config.json +46 -0
processor_config.json
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{
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"audio_token": "<|unused_200053|>",
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"audio_bos_token": "<|content_audio_input|>",
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"dmel_max_value": 2.0,
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"dmel_min_value": -7.0,
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"feature_extractor": {
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"audio_token_duration_s": 0.05,
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"feature_extractor_type": "InklingFeatureExtractor",
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"feature_size": 80,
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"hop_length": 800,
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"n_fft": 1600,
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"padding_side": "right",
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"padding_value": 0.0,
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"return_attention_mask": true,
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"sampling_rate": 16000,
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"window_size": 1600,
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"window_size_multiplier": 2.0
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},
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"image_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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"image_mean": [
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0.48145466,
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0.4578275,
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0.40821073
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],
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"image_processor_type": "InklingImageProcessor",
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"image_std": [
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0.26862954,
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0.26130258,
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0.27577711
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],
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"resample": 3,
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"rescale_factor": 0.00392156862745098,
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"size": {
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"height": 40,
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"width": 40
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}
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},
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"image_token": "<|unused_200054|>",
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"image_bos_token": "<|content_image|>",
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"num_dmel_bins": 16,
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"processor_class": "InklingProcessor"
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}
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