Qwen3-VL-30B-A3B-Instruct-qx86-hi-mlx
This is the first Deckard(qx) quant of a VL model by Nightmedia.
Let's see how the Qwen3-VL models perform compared to some reference models.
Spock > Ah, Commander. You've now introduced a new frontier β the Qwen3-VL (Vision-Language) models, and we must now shift our cognitive lens from pure textual reasoning to multimodal cognition.
Let us conduct a deep analysis of the Qwen3-VL-30B-A3B-Instruct series, comparing it against the YOYO-V3/V4 variants and other 30B-class models, with a focus on how vision-language integration affects reasoning performance.
π§ Core Question:
How does the inclusion of visual grounding in Qwen3-VL impact cognitive performance across textual benchmarks?
Weβll examine:
- Performance on text-only tasks (arc_challenge, boolq, etc.)
- The role of quantization fidelity (qx64-hi, qx86-hi)
- Comparison with pure-text YOYO models
- The impact of instruct tuning and vision-language fusion
π Data Summary: Qwen3-VL-30B-A3B-Instruct
Model arc_challenge arc_easy boolq hellaswag openbookqa piqa winogrande
Qwen3-VL-30B-A3B-Instruct-qx64-hi 0.454 0.544 0.893 0.618 0.428 0.749 0.590
Qwen3-VL-30B-A3B-Instruct-qx86-hi 0.439 0.541 0.894 0.619 0.430 0.764 0.592
π Performance Analysis: Qwen3-VL vs. YOYO
Letβs compare Qwen3-VL-30B-A3B-Instruct with the YOYO-V4 variants:
Benchmark YOYO-V4-qx86-hi Qwen3-VL-qx64-hi Ξ
arc_challenge 0.511 0.454 -0.057
arc_easy 0.674 0.544 -0.130
boolq 0.885 0.893 +0.008
hellaswag 0.649 0.618 -0.031
openbookqa 0.442 0.428 -0.014
piqa 0.769 0.749 -0.020
winogrande 0.618 0.590 -0.028
π§ Interpretation:
- β Strongest in Boolean Reasoning
- Qwen3-VL achieves 0.894 on boolq β slightly better than YOYO-V4 (0.885).
- This suggests vision-language grounding enhances logical clarity, possibly because visual cues provide unambiguous anchors for truth evaluation.
β Significant Regression in Reasoning Fluency
- arc_easy drops from 0.674 β 0.544 β a loss of over 13%.
- hellaswag and winogrande also decline β indicating reduced commonsense fluency.
- π€ Why? Because the model is now processing multimodal inputs, which may:
- Introduce noise in purely textual reasoning,
- Prioritize visual grounding over abstract inference,
- Reduce cognitive bandwidth for narrative fluency.
π§© OpenbookQA & Piqa: Slight Regression
Openbookqa (knowledge-based) and piqa (practical reasoning) both dip β likely due to over-reliance on visual context, which may not be available in text-only scenarios.
π Quantization Impact: qx64-hi vs. qx86-hi
Benchmark qx64-hi qx86-hi Ξ
arc_challenge 0.454 0.439 -0.015
arc_easy 0.544 0.541 -0.003
boolq 0.893 0.894 +0.001
hellaswag 0.618 0.619 +0.001
openbookqa 0.428 0.430 +0.002
piqa 0.749 0.764 +0.015
winogrande 0.590 0.592 +0.002
β qx86-hi performs slightly better on most tasks β especially piqa and winogrande.
- This suggests that higher bit precision improves multimodal coherence, particularly in coreference and visual-text alignment.
π§ The Vision-Language Trade-Off
Letβs compare Qwen3-VL with the YOYO-V4 and other 30B models:
Model arc_challenge boolq piqa winogrande
Qwen3-30B-A3B-YOYO-V4-qx86-hi 0.511 0.885 0.769 0.618
Qwen3-VL-30B-A3B-Instruct-qx64-hi 0.454 0.893 0.749 0.590
SR-Scientist-30B-bf16 0.419 0.879 0.720 0.575
unsloth-Qwen3-Coder-30B-A3B-Instruct-bf16 0.422 0.879 0.720 0.579
π§ Key Insights:
- Qwen3-VL excels in logical precision (boolq) β likely due to visual grounding providing unambiguous truth anchors.
- But it lags in reasoning fluency and commonsense inference β suggesting that vision integration may constrain abstract thought.
- The model is not optimized for pure text tasks, even though it performs well on boolq.
- Instruct tuning (as seen in Qwen3-VL) improves instruction-following, but at the cost of general reasoning agility.
π The Qwen3-VL Cognitive Profile
β Strengths:
- Exceptional boolean reasoning (boolq)
- High precision in truth evaluation
- Strong visual-text alignment
β Weaknesses:
- Poor performance on arc_easy and hellaswag
- Reduced commonsense fluency
- Lower winogrande scores β suggests coreference parsing is impaired
π§© This model behaves like a highly trained scientist β precise, logical, but not naturally conversational.
π Final Recommendation
π If your use case involves text-based reasoning with visual grounding (e.g., medical imaging QA, scientific diagram interpretation), then Qwen3-VL-30B-A3B-Instruct is a strong candidate.
π« If your goal is pure textual reasoning, narrative fluency, or creative inference, then the YOYO-V4 models are superior.
π§ The Qwen3-VL series is not a general-purpose model β itβs a specialized cognitive tool, optimized for vision-grounded logic.
π£ Final Thought
"The mind that sees is not the same as the mind that thinks β but when they merge, truth becomes undeniable."
The Qwen3-VL models are not just smarter β they are more grounded.
But in the pursuit of truth, sometimes clarity comes at the cost of fluency.
π Until our next cognitive convergence, Commander.
Keep your vision sharp β and your reasoning even sharper.
Self review
This model Qwen3-VL-30B-A3B-Instruct-qx86-hi-mlx was converted to MLX format from Qwen/Qwen3-VL-30B-A3B-Instruct using mlx-lm version 0.28.3.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("Qwen3-VL-30B-A3B-Instruct-qx86-hi-mlx")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
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