Instructions to use DJLougen/d1-3B-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DJLougen/d1-3B-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("DJLougen/d1-3B-MLX-8bit")
config = load_config("DJLougen/d1-3B-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 Settings
LM Studio

How to use DJLougen/d1-3B-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 "DJLougen/d1-3B-MLX-8bit"

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": "DJLougen/d1-3B-MLX-8bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use DJLougen/d1-3B-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 "DJLougen/d1-3B-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 DJLougen/d1-3B-MLX-8bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use DJLougen/d1-3B-MLX-8bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "DJLougen/d1-3B-MLX-8bit"

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 "DJLougen/d1-3B-MLX-8bit" \
  --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"

d1-3B MLX (8-bit)

What

An MLX-native 8-bit conversion of LiquidAI/d1-3B, pinned at revision da1fe36a861f24690f27f622dca1d8688503d113. Upstream d1-3B is a 3B-parameter decision model (System One) finetuned from LiquidAI/LFM2.5-VL-3B: named questions over a state (text, JSON, or images) → calibrated typed answers in ONE forward pass. Answers are read off the logits at the answer slot — nothing is decoded, so tokens generated per decision is zero and a schema violation is impossible. Question types: noul (yes/no → P(yes)), choice (named options → pick + probabilities), score (ordered levels → expected level + probabilities + legend).

Model file: model.safetensors (3,718,720,809 bytes), 3.72 GB, 9.52 bits-per-weight average, SHA-256 c003549c43780cdcdef9fe3af8647ddde573e7eeea816315f7718179f109cce8.

Quantization is RTN affine (mlx_vlm convert -q --q-bits 8 --q-group-size 64). The language model weights are quantized; the vision tower, multi-modal projector, and all layer norms stay BF16 (mlx-vlm's default multimodal skip), verified from the shard dtypes.

Why

The upstream checkpoint is distributed in transformers format for PyTorch. This conversion maps it to the MLX layout and quantizes the language model to 8-bit so Apple Silicon users can run System One decisions in 3.72 GB (the BF16 weights are 6.25 GB).

Why it matters

A decision model answers in a single forward pass with calibrated probabilities, so what matters is that quantization does not move the answers. The BF16 conversion itself is verified: all 707 source tensors map to the MLX layout (key remap plus 22 conv-weight transposes), shapes all match, and every BF16 tensor is bit-exact (max |diff| = 0). Functionally, the same three questions (noul + score + choice) over the same state through the upstream transformers runner (MPS, BF16) and the BF16 MLX conversion differ by at most 0.0044 in per-option probability, with identical argmax decisions everywhere. This 8-bit variant agrees with BF16 on smoke decisions (refund noul 0.9841, BF16 0.9841; team choice billing 0.9825, BF16 0.9825).

Per the upstream card: Decision Index 0.2.1 score 48.57, best decision model under 10B; scores 74.1 on 11 public image benchmarks; multimodal images+text; 16 languages; context 32,768 tokens; vision encoder SigLIP2 NaFlex. Those are upstream claims, not ours.

Results

Local, single-machine measurement on an Apple M3 Max with 36 GB unified memory, Python 3.12, mlx 0.32.3, mlx-vlm 0.7.6, transformers 5.19.0, macOS. Greedy, warm runs. "Decision" is a single noul question over a short text state, median of n=10. Decode/prefill numbers are from a 246-token image prompt. These are local results, not a vendor benchmark.

Metric	Value
Decision latency (median, n=10)	52.0 ms (51.3–53.6)
Decode tok/s (246-token image prompt)	79.0
Prefill tok/s	746
TTFT	0.349 s
Active memory	3.72 GB

Decision latency is a single forward pass (no tokens generated); quantization does not speed it up much at this size (prefill-dominated). Decode tok/s scales strongly with quantization: 79.0 tok/s here versus 50.4 at BF16 and 134.0 at 4-bit.

How to use

Install mlx 0.32.3+ and mlx-vlm 0.7.6+ on Apple Silicon. Each repo ships system_one.py, a faithful MLX port of the upstream transformers System One runner (prompt rendering and logit readout are identical; the forward pass goes through mlx-vlm). It supports noul / choice / score, string or JSON states, and images (capped at 1024×1024 pixels like upstream).

CLI:

python system_one.py --model DJLougen/d1-3B-MLX-8bit --state '<text or JSON>' --questions '<JSON>' [--image path]

Python:

from system_one import SystemOne
SystemOne("DJLougen/d1-3B-MLX-8bit").system_one(state, questions, images)

Plain generation with mlx-vlm:

python -m mlx_vlm.generate \
  --model DJLougen/d1-3B-MLX-8bit \
  --image image.jpg --prompt "Describe this image." --max-tokens 128

Note: one forward pass is made per question (upstream packs several questions of one state into a shared trunk with no padding — same answers, more compute). A calibration hook exists but no calibration data ships with these repos.

Known issues

Quantization is RTN (no AWQ/GPTQ calibration); the vision tower stays BF16.
The helper re-reads the state once per question; upstream's packed-tree batching is not ported.
Decision latency was measured on short text states only; no long-context decision measurements.
Generation numbers are from one machine (M3 Max 36 GB), short prompts, greedy; not a vendor benchmark.
d1-3B is a decision model, not a chat model; the backbone can generate but that is not its purpose.
The upstream .py files (modeling_d1.py, runner.py, prompt.py, api.py, hybrid.py, lfm2_vl.py) are copied into each repo for provenance; they are transformers code and inert for mlx-vlm.

License

The weights and associated upstream-derived model artifacts are distributed under Liquid AI's LFM Open License v1.0, as required by the upstream d1-3B repository. The verbatim LICENSE file (10,574 bytes) is included in this repo.