mellum2-12b-a2_5b-thinking-optiq-5bpw-mlx

MLX quantization of JetBrains/Mellum2-12B-A2.5B-Thinking for Apple Silicon.

Variant: OptiQ mixed-precision (target 5.0 bpw)
Disk size: 12024 MB
Quantized by: sahilchachra

About this quantization

Unlike uniform 4-bit quantization (which forces every layer onto the same bit grid and often collapses reasoning at low bit widths), this model was quantized with mlx-optiq using per-layer KL-sensitivity analysis:

A small calibration set (32 samples spanning prose, multi-step reasoning, code, and constraint-following instructions) is run through the FP16 reference and through trial quantizations of each layer.
The output drift per layer is measured. Layers whose outputs are most affected by quantization (typically the final attention projections, the lm_head, and a few middle blocks) get more bits; layers that tolerate aggressive quantization get fewer.
The final assignment hits the target average bits-per-weight while keeping the bits where they matter. This trades off precision unequally so the average comes out near the target (5.0 bits/weight), but the bits that matter most for output fidelity stay high.

Quantization config

Method: optiq_mixed_precision (mlx-optiq)
Target bits/weight: 5.0
Achieved bits/weight: 5.006
Candidate bits: [3, 4, 6, 8]
Group size: 64
Sensitivity reference: uniform_4bit
Calibration: 32-sample 4-domain mix (prose + reasoning + code + constraints)

Per-layer bit allocation

141 quantizable components total. OptiQ allocated bits non-uniformly based on KL sensitivity:

Bits	Components	Share
8-bit	33	23.4%
6-bit	57	40.4%
4-bit	43	30.5%
3-bit	8	5.7%
Total	141	100.0%

Benchmark results

Evaluated on Apple M5 Pro with MLX. Model loaded once; performance and quality measured in a single pass.

Performance

	This model	FP16 baseline
Decode tok/s (steady-state)	95.19	N/A
Prefill tok/s (steady-state)	243.98	N/A
Decode tok/s (avg, long traces)	89.1	N/A
Peak memory (GB)	13.051	N/A
Disk size (MB)	12024	23183

Warmed, short-prompt, chat-templated, thinking disabled. Represents steady-state decode for typical chat use; long thinking traces will be slower due to KV-cache growth.

Quality

Benchmarks the upstream card also reports (JetBrains card (bf16))

The JetBrains card (bf16) column is the score published on the original model card. Our column is measured locally with this quant variant; sample sizes and prompts differ, so treat as directional.

Benchmark	This model	JetBrains card (bf16)	n
IFEval (instruction following)	63.6%	76.5%	44
MMLU (knowledge, accuracy)	90.0%	86.2% (MMLU-Redux)	50

Additional benchmarks (our suite)

These benchmarks are not on the upstream card. No external reference; FP16 baseline column reflects local fp16 runs if any.

Benchmark	This model	FP16 baseline	n
MATH-500 (math reasoning)	90.0% (answered 30/30)	N/A	30
HumanEval (code, pass@1)	100.0%	N/A	30

MATH-500 per-level accuracy

Level	This model	FP16 baseline
level 1	83.3%	N/A
level 2	100.0%	N/A
level 3	83.3%	N/A
level 4	83.3%	N/A
level 5	100.0%	N/A

Context scaling (decode tok/s)

Context length	Decode tok/s
~128 tokens	95.2
~256 tokens	95.4
~512 tokens	95.4
~1024 tokens	93.3

Usage

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("sahilchachra/mellum2-12b-a2_5b-thinking-optiq-5bpw-mlx")
response = generate(model, tokenizer, prompt="Your prompt here", max_tokens=256, verbose=True)

Heads-up for Mellum2: mlx-lm support landed in PR #1339 and may not yet be in the released pypi package. If load(...) complains about an unknown mellum model type, install the PR branch:
pip install "git+https://github.com/ml-explore/mlx-lm.git@refs/pull/1339/head"
Also note: this repo ships a fixed eos_token_id=28 (<|im_end|>) in config.json and generation_config.json — the JetBrains source has eos_token_id=0 (<|endoftext|>) which the chat template never emits, so generation runs to max_tokens every call. The fix is already applied here.

All variants in this collection

Model	Variant
sahilchachra/mellum2-12b-a2_5b-thinking-mxfp4-mlx	Block float MX FP4
sahilchachra/mellum2-12b-a2_5b-thinking-optiq-5bpw-mlx	OptiQ mixed-precision (target 5.0 bpw) ← this model

Notes

Requires Apple Silicon (M1 or later) with MLX
Benchmarks run on Apple M5 Pro, 24 GB unified memory
License: see JetBrains/Mellum2-12B-A2.5B-Thinking for the original model's license

Original model

See JetBrains/Mellum2-12B-A2.5B-Thinking for full model details and intended use.

sahilchachra/mellum2-12b-a2_5b-thinking-optiq-5bpw-mlx