Image-Text-to-Text
MLX
Safetensors
inkling_mm_model
inkling
Mixture of Experts
multimodal
text-generation
apple-silicon
conversational
Instructions to use mlx-community/Inkling-Small-mlx-2bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Inkling-Small-mlx-2bit 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/Inkling-Small-mlx-2bit") config = load_config("mlx-community/Inkling-Small-mlx-2bit") # 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 mlx-community/Inkling-Small-mlx-2bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "mlx-community/Inkling-Small-mlx-2bit"
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": "mlx-community/Inkling-Small-mlx-2bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use mlx-community/Inkling-Small-mlx-2bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "mlx-community/Inkling-Small-mlx-2bit"
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 "mlx-community/Inkling-Small-mlx-2bit" \ --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"
- Hermes Agent
How to use mlx-community/Inkling-Small-mlx-2bit 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 "mlx-community/Inkling-Small-mlx-2bit"
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 mlx-community/Inkling-Small-mlx-2bit
Run Hermes
hermes
| """Inkling audio tower: discrete dMel-token embedding + norm. | |
| Each audio frame is ``n_mel_bins`` discretized bins (values in ``[0, mel_vocab_size)``); | |
| each bin is embedded from its own slice of a shared table (offset ``bin * mel_vocab_size``) | |
| and the per-bin embeddings are summed. Mirrors ``InklingAudioModel`` / | |
| ``InklingAudioModelEmbeddings``. Checkpoint keys: ``audio.encoder.weight`` (the | |
| ``[n_mel_bins*mel_vocab_size, hidden]`` table) and ``audio.final_norm.weight``. | |
| """ | |
| from __future__ import annotations | |
| import mlx.core as mx | |
| import mlx.nn as nn | |
| from .common import RMSNorm | |
| from .config import AudioConfig | |
| class AudioModel(nn.Module): | |
| def __init__(self, config: AudioConfig): | |
| super().__init__() | |
| self.config = config | |
| self.encoder = nn.Embedding( | |
| config.n_mel_bins * config.mel_vocab_size, config.text_hidden_size | |
| ) | |
| self.final_norm = RMSNorm(config.text_hidden_size, eps=config.rms_norm_eps) | |
| # non-persistent: arange(n_mel_bins) * mel_vocab_size | |
| self._offsets = mx.arange(config.n_mel_bins) * config.mel_vocab_size | |
| def __call__(self, audio_input_ids: mx.array) -> mx.array: | |
| # audio_input_ids: [..., n_mel_bins] with values in [0, mel_vocab_size) | |
| embeds = self.encoder(audio_input_ids + self._offsets) # [..., n_mel_bins, hidden] | |
| embeds = embeds.sum(axis=-2) # [..., hidden] | |
| return self.final_norm(embeds) | |