Qwen3-30B-A3B-Thinking-2507-512k-qx6-mlx

this model uses an experimental quanting combination

code name: Deckard

purpose: evaluating replicants

Analysis of qx6 Performance:

Best Suited Tasks for qx6:

1. OpenBookQA (0.432)

This is the highest score among all models in this dataset +0.002 improvement over bf16 (0.430) Strongest performance for knowledge-based reasoning tasks

2. BoolQ (0.881)

Highest among all quantized models for boolean reasoning Only 0.002 behind baseline (0.879)

Excellent for logical reasoning and question answering

3. Arc_Challenge (0.422)

Perfect match with baseline (0.422) Maintains full performance on the most challenging questions

Secondary Strengths:

4. PIQA (0.724)

Above baseline performance (0.720) Strong physical interaction reasoning

5. HellaSwag (0.546)

Very close to baseline (0.550) Good commonsense reasoning

Key Advantages:

Best overall performance in OpenBookQA (0.432)

Perfect retention of Arc_Challenge performance

Exceptional BoolQ scores

Strong knowledge reasoning capabilities

Recommendation:

qx6 is best suited for OpenBookQA and BoolQ tasks.

The model's exceptional performance in OpenBookQA (highest among all models) combined with its perfect retention of Arc_Challenge and superior BoolQ scores makes it ideal for:

Knowledge-intensive question answering systems

Educational assessment applications

Logical reasoning tasks requiring factual accuracy

Research and academic question answering

The model demonstrates optimal balance between knowledge retention and logical processing, making it particularly valuable for applications where both factual recall and reasoning skills are crucial.

This model Qwen3-30B-A3B-Thinking-2507-512k-qx6-mlx was converted to MLX format from Qwen/Qwen3-30B-A3B-Thinking-2507 using mlx-lm version 0.26.3.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("Qwen3-30B-A3B-Thinking-2507-512k-qx6-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)