mlx-community/pplx-decider-v1.1-27b-4bit

An MLX 4-bit conversion of perplexity-ai/pplx-decider-v1.1-27b, for Apple Silicon. It includes a standalone inference script and a llama.cpp-compatible /v1/systemone server.

This is a classifier, not a chat model. Give it a state (text, JSON, chat messages and/or up to 8 images) and a question. One forward pass returns calibrated probabilities over the answer options. There is no generation step.

Question type	Criteria	Answer
choice	{"label": "description", ...}, 1-255 options	choice, probabilities, confidence
score	["lowest level", ..., "highest level"], 2-10 levels	score (expected level), probabilities, legend, confidence
noul	optional {"true": "...", "false": "..."}	noul = P(true)

Why it needs the included code

mlx_lm.load / mlx_vlm.load will not give correct results with this checkpoint:

• The 16 full-attention layers are noncausal: every token sees the whole prompt, and only padding is masked. The 48 Gated DeltaNet layers stay causal. Running with a causal mask still picks the same top answer most of the time, but the probabilities are clearly wrong (mean KL 0.038 vs 0.0014).
• The output is a 255-row decision readout (stored as language_model.lm_head), not a vocabulary head. It reads only the last token, uses calibration temperature 1.00874, and masks options past the question's count.

decider_mlx.py implements all of this. The prompt format and answer logic are copied from the official autojev/model.py.

Quick start

Requires an Apple Silicon Mac with at least 24 GB of memory (the model uses about 16 GB at runtime), and Python 3.12+.

hf download mlx-community/pplx-decider-v1.1-27b-4bit --local-dir pplx-decider-4bit
cd pplx-decider-4bit
pip install -r requirements.txt     # mlx, mlx-vlm, transformers, pillow (no torch needed)

from decider_mlx import Decider

model = Decider.from_pretrained(".")   # or "mlx-community/pplx-decider-v1.1-27b-4bit"
answers = model.decide(
    "Customer message: I was charged twice for my order last week.",
    {
        "route":   {"type": "choice", "instructions": "Which team should handle this?",
                    "criteria": {"billing": "payments, refunds", "shipping": "delivery", "technical": "bugs, login"}},
        "angry":   {"type": "noul",   "instructions": "Is the customer angry?"},
        "urgency": {"type": "score",  "instructions": "How urgent is this?",
                    "criteria": ["can wait", "this week", "today", "right now"]},
    },
    images=None,  # optional: file paths, data:image/... URLs or PIL images
)
print(answers["route"]["choice"], answers["angry"]["noul"], answers["urgency"]["score"])

For raw probabilities on many rows, use model.predict([{"state": ..., "question": ..., "images": [...]}, ...]). It groups rows of similar length into padded batches.

CLI:

python decider_mlx.py --model . --state "Win a FREE iPhone, click now!!!" \
  --question '{"type": "noul", "instructions": "Is this spam?"}'

Server

python server.py --model . --port 8092    # loads in ~10 s

curl localhost:8092/v1/systemone -H 'Content-Type: application/json' -d '{
  "state": "Customer message: I was charged twice for my order last week.",
  "questions": {
    "route":   {"type": "choice", "instructions": "Which team should handle this?",
                "criteria": {"billing": "payments, refunds", "shipping": "delivery", "technical": "bugs, login"}},
    "angry":   {"type": "noul",   "instructions": "Is the customer angry?"},
    "urgency": {"type": "score",  "instructions": "How urgent is this?", "criteria": ["can wait", "this week", "today", "right now"]}
  }}'

The request and response use the same format as llama.cpp's llama-server /v1/systemone:

• state: a string, JSON, or chat messages with image_url parts
• questions
• images: data URLs, up to 8

The response has answers and usage. Also available: GET /v1/models, /health and /stats. Set DECIDER_API_KEY to require a bearer token.

The server runs MLX on a single GPU worker. Concurrent requests are batched together by similar length. Each question is its own sequence: noncausal attention means questions about the same state can't share a prompt prefix.

Accuracy

Compared with the official PyTorch implementation (bf16), on 33 test rows covering choice/score/noul, 2-255 options, 100-7k tokens and images:

	argmax agree	mean KL	max abs Δp
this model (single row)	33/33	0.0014	0.029
this model (batched, padded)	33/33	0.0014	0.025
control: reference run with causal attention (the wrong mode)	33/33	0.038	0.27

Speed

Measured on an Apple M5 Max (128 GB) in High Power mode, over HTTP, sustained:

Workload	decisions/s
~100-token decisions, 32 concurrent clients	6.5
mixed 100-1.8k tokens + images (avg ~360), 16 clients	1.8

That's 1.4-1.6x faster than llama.cpp Q8_0/Q4_K_M on the same machine. Speed depends on prefill, which is compute-bound: about 975 tok/s with a cool GPU, about 730 tok/s sustained. Tips:

• Use High Power mode: sudo pmset -a powermode 2. On our machine it roughly doubled the speed.
• Batch rows of similar length. Padding is the main waste, and small batches run as efficiently as large ones.
• The default input limit is 8192 tokens per decision (Decider(..., max_tokens=...)). Prompts can't be split into chunks, because each full-attention layer needs every key at once.

Conversion details

• Source: perplexity-ai/pplx-decider-v1.1-27b @ 3b45dead, with the Qwen3_5Model backbone mapped to the mlx_vlm.models.qwen3_5 layout and the readout (readout.safetensors, [255, 5120]) stored as language_model.lm_head.
• Quantization: affine, 4 bits, group size 64, applied to the language backbone. The vision tower and the readout stay in bf16. Total size 15.3 GB.
• Tested with mlx 0.32.3, mlx-vlm 0.7.6, transformers 5.19.0 (see requirements.txt).

License and credits

Apache-2.0, the same license as the original model. See LICENSE and NOTICE. All credit for the model goes to Perplexity (original model card). It is built on Qwen3.8-27B, and much of its training data comes from tasksource:

@inproceedings{sileo-2024-tasksource,
    title = "tasksource: A Large Collection of {NLP} tasks with a Structured Dataset Preprocessing Framework",
    author = "Sileo, Damien",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    year = "2024",
    url = "https://aclanthology.org/2024.lrec-main.1361",
    pages = "15655--15684",
}