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mlx-triage Validation Results

Last updated: 2026-03-08 Hardware: Apple M2 Max, 96 GB unified memory mlx-triage: v0.1.1 MLX: 0.30.6

Summary

33 models tested across 13 model families, 7 quantization formats, and sizes from 0.6B to 35B parameters.

  • 33/33 completed Tier 0 diagnostics successfully
  • 14/14 Tier 0 + Tier 1 models completed full diagnostics
  • 1 VLM model (Qwen3.5-4B-4bit) completed Tier 0 but Tier 1 blocked — mlx-lm cannot load VLM architectures
  • 0 false negatives — all known-good models pass
  • 100% determinism at temp=0 across all Tier 1 tested models
  • Perplexity range: 1.8 – 4.8 (all within PASS threshold of <15)

Full Results

Tier 0 + Tier 1 Validated (14 models)

# Model Size Quant T0 1.1 Det 1.3 PPL Status
1 Qwen3-0.6B-8bit 0.6B 8-bit SKIP PASS 3.6 ✅
2 Llama-3.2-1B-Instruct-4bit 1B 4-bit SKIP PASS 3.6 ✅
3 LFM2.5-1.2B-Instruct-bf16 1.2B bf16 WARN PASS 2.7 ✅
4 LFM2.5-1.2B-Thinking-8bit 1.2B 8-bit WARN PASS 3.2 ✅
5 LFM2.5-1.2B-Thinking-4bit-DWQ 1.2B 4-bit DWQ WARN PASS 3.3 ✅
6 Nanbeige4.1-3B-8bit 3B 8-bit WARN PASS 4.8 ✅
7 Phi-4-mini-instruct-4bit 3.8B 4-bit SKIP PASS 3.8 ✅
8 Qwen3-4B-Instruct-2507-4bit 4B 4-bit WARN PASS 2.6 ✅
9 Qwen3-4B-Instruct-2507-4bit-DWQ 4B 4-bit DWQ WARN PASS 2.6 ✅
10 Qwen3-4B-Thinking-2507-4bit 4B 4-bit WARN PASS 2.6 ✅
11 Qwen3-30B-A3B-4bit (MoE) 30B 4-bit SKIP PASS 2.3 ✅
12 Qwen3-30B-A3B-Instruct-2507-4bit (MoE) 30B 4-bit WARN PASS 2.2 ✅
13 Qwen3.5-27B-4bit 27B 4-bit WARN PASS 1.8 ✅
14 Qwen3-8B-4bit 8B 4-bit WARN PASS 2.2 ✅

Test 1.2 (Reference Divergence): SKIP across all models — transformers/torch not installed. This is expected behavior; the check gracefully skips with remediation instructions.

Expanded Tier 0 Validated (19 additional models)

# Model Size Quant T0 Arch Status
14 Qwen3-1.7B-4bit 1.7B 4-bit WARN qwen3 ✅
15 Qwen3-4B-4bit 4B 4-bit WARN qwen3 ✅
16 Qwen3.5-4B-4bit (VLM) 4B 4-bit WARN qwen3_5 ⚠️ T1 blocked
17 Qwen3.5-35B-A3B-4bit (MoE) 35B 4-bit WARN qwen3_5_moe ✅
18 gemma-3-1b-it-qat-4bit 1B QAT 4-bit WARN gemma3_text ✅
19 gemma-3-4b-it-qat-4bit 4B QAT 4-bit WARN gemma3 ✅
20 gemma-3-12b-it-qat-4bit 12B QAT 4-bit WARN gemma3 ✅
21 gemma-3-27b-it-qat-4bit 27B QAT 4-bit WARN gemma3 ✅
22 gpt-oss-20b-MXFP4-Q8 20B MXFP4-Q8 WARN gpt_oss ✅
23 Devstral-Small-2-24B-Instruct-2512-4bit 24B 4-bit WARN mistral3 ✅
24 Ministral-3-3B-Instruct-2512-4bit 3B 4-bit WARN mistral3 ✅
25 Ministral-3-8B-Instruct-2512-4bit 8B 4-bit WARN mistral3 ✅
26 GLM-4.7-Flash-4bit 9B 4-bit WARN glm4_moe_lite ✅
27 GLM-4.7-Flash-6bit 9B 6-bit WARN glm4_moe_lite ✅
28 GLM-4.7-Flash-8bit 9B 8-bit WARN glm4_moe_lite ✅
29 NVIDIA-Nemotron-3-Nano-30B-A3B-4bit (MoE) 30B 4-bit WARN nemotron_h ✅
30 LFM2-2.6B-4bit 2.6B 4-bit WARN lfm2 ✅
31 LFM2.5-1.2B-Instruct-4bit 1.2B 4-bit WARN lfm2 ✅
32 LFM2.5-1.2B-Thinking-6bit 1.2B 6-bit WARN lfm2 ✅

Observations

Tier 0

  • T0 WARNING is the most common verdict. The WARNINGs come from Check 0.4 (version check) which flags advisory-level known bugs for the installed MLX version. These are capped at WARNING severity to avoid false CRITICAL verdicts.
  • T0 SKIP on some models means all checks passed (PASS/INFO/SKIP), with the version check skipping.
  • Missing chat_template detected on Ministral, GLM, Nemotron, Qwen3.5-MoE, and LFM models. These ship without a chat_template in tokenizer config, causing runtime fallback to ChatML/default formatting.

Tier 1

  • Perfect determinism (100%) across all 14 Tier 1 models. Every model produced identical outputs across 10 runs × 5 prompts at temp=0. This validates the determinism check against both quantized (4-bit, 8-bit) and full-precision (bf16) models.
  • Perplexity range: 1.8 – 4.8 — all well within the PASS threshold (<15). Larger models tend toward lower perplexity as expected. The 0.6B model (4.8) had the highest, and the 27B model (1.8) had the lowest.
  • DWQ (Dynamic Weight Quantization) shows no quality degradation compared to standard 4-bit quantization for the same base model (both Qwen3-4B variants: 2.6 PPL).

Model compatibility

  • The tool correctly handles MoE (Mixture of Experts) architectures across multiple families: Qwen3 A3B, Qwen3.5 A3B, Nemotron-H A3B, and GLM-4 MoE Lite.
  • QAT (Quantization-Aware Training) models from Gemma 3 are handled correctly — the quantization_config is detected and dtype checks adapt accordingly.
  • MXFP4-Q8 (Microscaling) format on gpt-oss-20b is recognized as a quantized model with correct metadata extraction.
  • VLM (Vision-Language Model) architectures complete Tier 0 successfully but Tier 1 is blocked — mlx-lm cannot load models with vision_tower weights (297 extra parameters rejected). Qwen3.5-4B-4bit (Qwen3_5ForConditionalGeneration) is the first VLM tested. Check T0.5 (VLM detection) is planned to catch this cleanly before model load.
  • Mixed-precision MoE quantization observed on Qwen3-30B-A3B-Instruct-2507-4bit: 4-bit for attention/FFN weights, 8-bit for all 48 MoE gate layers. This preserves routing precision and is correctly surfaced in quantization metadata.

Model Families Tested

Family Models Architectures
Qwen (3, 3.5) 12 Dense, MoE (A3B), VLM
Gemma (3) 4 Dense (QAT)
GLM (4.7) 3 MoE Lite
Mistral/Devstral (3) 3 Dense
LiquidAI LFM (2, 2.5) 5 Hybrid (RNN/Transformer)
GPT-OSS 1 Dense (MXFP4)
NVIDIA Nemotron (3) 1 MoE (Nemotron-H)
Llama (3.2) 1 Dense
Phi (4) 1 Dense
Nanbeige (4.1) 1 Dense

10 distinct families, 13 unique architectures

Quantization Coverage

Quantization Models Tested All Pass?
bf16 (full) 1 ✅
8-bit 7 ✅
6-bit 2 ✅
4-bit 16 ✅
4-bit DWQ 2 ✅
QAT 4-bit 4 ✅
MXFP4-Q8 1 ✅

Size Distribution

Size Range Models
< 2B 8
2B – 5B 9
5B – 15B 6
15B – 35B 10