| --- |
| license: apache-2.0 |
| library_name: mlx |
| tags: |
| - mlx |
| - inkling |
| - moe |
| - text-generation |
| base_model: thinkingmachines/Inkling |
| pipeline_tag: text-generation |
| --- |
| |
| # Inkling-mlx (4-bit, text backbone, BF16-sourced) |
|
|
| An **MLX 4-bit** build of the **text backbone** of Thinking Machines' **Inkling** |
| (975B-total / 41B-active MoE), quantized **directly from the BF16 checkpoint** (no |
| NVFP4->INT4 double-quantization), for running natively on Apple Silicon with |
| [`mlx-lm`](https://github.com/ml-explore/mlx-lm). |
|
|
| This is created for people using a one Apple Mac Studio M3 Ultra with 512 GB. |
|
|
| > **Community note.** Early, **not fully numerically-verified** |
| > conversion, shared to see whether anyone can load/run a model this large on Apple |
| > Silicon. Expect rough edges; please open a discussion with results (or failures). |
|
|
| ## Heads up |
|
|
| - **Memory:** ~**560 GB** on disk (4-bit routed experts + bf16 attention / shared |
| experts / embeddings). Loading needs roughly that much **unified memory** - beyond any |
| single Mac today (max 512 GB), so realistically distributed/multi-device MLX. Largely a |
| **research artifact**. |
| - **Not verified yet:** the custom Inkling forward (factorized attention + short-conv + |
| sigmoid MoE) is a from-reference reimplementation; logits have **not** been checked |
| against the original. |
| - **Scope:** **text decoder only.** Vision (image/video) and audio encoders are not included. |
|
|
| ## Provenance |
|
|
| - **Source:** `thinkingmachines/Inkling` (BF16) -> MLX affine **4-bit** (group size 64). |
| Only routed MoE experts are quantized; everything else is bf16. Quantizing straight from |
| BF16 avoids the NVFP4->INT4 step, so this should be slightly higher quality than the |
| NVFP4-sourced sibling ([`huckiyang/Inkling-NVFP4-mlx`](https://huggingface.co/huckiyang/Inkling-NVFP4-mlx)). |
|
|
| ## Usage (once a loader is available) |
|
|
| ```python |
| from mlx_lm import load, generate |
| model, tokenizer = load("mlx-community/Inkling-mlx-4bit") |
| print(generate(model, tokenizer, prompt="The capital of France is", max_tokens=64)) |
| ``` |
|
|
| > The custom model class lives in the conversion repo (`models/inkling_mlx.py`); until it's |
| > registered in `mlx-lm`, load via that module's `load()`. |
| > |
| > Blog: https://huckiyang.github.io/blog/inkling-audio-design.html |
