Instructions to use studioburnside/Inkling-Small-REAP25-2bE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use studioburnside/Inkling-Small-REAP25-2bE 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("studioburnside/Inkling-Small-REAP25-2bE") config = load_config("studioburnside/Inkling-Small-REAP25-2bE") # 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 studioburnside/Inkling-Small-REAP25-2bE with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "studioburnside/Inkling-Small-REAP25-2bE"
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": "studioburnside/Inkling-Small-REAP25-2bE" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use studioburnside/Inkling-Small-REAP25-2bE 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 "studioburnside/Inkling-Small-REAP25-2bE"
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 studioburnside/Inkling-Small-REAP25-2bE
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use studioburnside/Inkling-Small-REAP25-2bE with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "studioburnside/Inkling-Small-REAP25-2bE"
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 "studioburnside/Inkling-Small-REAP25-2bE" \ --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"
Inkling-Small-REAP25-2bE
A tiered re-quantization of pipenetwork/Inkling-Small-MLX-REAP25-4bit, itself a REAP-pruned MLX build of thinkingmachines/Inkling-Small. 2-bit routed experts, everything else left at 4-bit. 60GB.
Credit where it belongs: REAP pruning is Cerebras, arXiv:2510.13999;
the pruned 4-bit MLX base and the inkling_mlx loader are
PipeNetwork; the model is Thinking Machines Lab's.
This repo contributes only the expert-tier requant and the measurements below.
Why this exists
Inkling-Small is an unusually capable open multimodal MoE, and the 4-bit REAP build is ~112GB — which fits a 128GB Apple Silicon machine only by leaving no room for a long-context KV cache. The question was simple: do the routed experts survive deeper quantization while attention, embeddings and the shared "sink" experts stay at 4-bit? If yes, you buy back tens of gigabytes of working memory for context, which is the scarce resource on a single machine.
The answer turned out to be interesting in both directions.
Measured (our eval suite, temperature 0 unless noted)
| value | |
|---|---|
| intelligence | 0.90 |
| code (evalplus) | PERFECT 20/20 |
| verbosity ratio | 23x |
| decode, single stream | 58.5 tok/s |
| tools | 0.85 (engine-side parsing gaps, not weights — 18/20 with a fixed parser) |
3bE scored 0.97 on intelligence — the best result we have ever recorded on this suite, from any model. 2bE returned a perfect 20/20 on evalplus, which matters because it proves code ability survives 2-bit experts; 3bE's lower code number is an output-duplication artifact, not a weights limitation.
The honest counterpoint: at 2-bit experts, reasoning sometimes fails to terminate (4 parse failures on the intelligence suite). The capability is there; the stopping behaviour degrades.
Status: not production-ready, and the reasons are not the weights
Long-context evaluation is blocked upstream, not by this quantization:
- omlx's prefill preflight uses a static ~250-280KB/token workspace constant versus <1GB of actual KV at 99K, so large prefills are rejected on headroom the model does not need (jundot/omlx#2454)
- the community
inkling_mlxcheckpoint layout needs engine support (#2451) - nested-object tool arguments are dropped by the tool-call parser (#2453)
All three are integration-layer and all are open. Until they land, this is a research artifact: excellent on everything that reaches the weights, unproven past ~8K context.
Use
Requires the inkling_mlx loader and an engine with community-layout support.
Recommend stop=["<|end_message|>"] to suppress the duplication artifact.
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4-bit
Model tree for studioburnside/Inkling-Small-REAP25-2bE
Base model
thinkingmachines/Inkling-Small