Qwopus3.5-9B-Coder-oQ4e-mtp

This model was quantized using oQ (oMLX v0.5.0) mixed-precision quantization.

Quantization details

• Model type: qwen3_5
• Bits: 4
• Group size: 64
• Format: MLX safetensors

Performance Benchmarks

Note: Results are for reference only and may vary depending on hardware, software configuration, and workload.

Environment

• Hardware: M5 MacBook Air 32GB
• Inference Framework: oMLX v0.5.0
• Settings:
  • Thinking: Disabled
  • Chat template parameter: enable_thinking=false (forced)
  • TurboQuant KV Cache: Disabled (if enable: reduces intelligence)
  • Native MTP: Enabled (key speed improvement)

Speed Comparison (pp1024/tg128 single request)

Model	tg TPS	TTFT (ms)	E2E (s)	Peak Mem	Test Date
Qwopus3.5-4B-Coder-oQ4e-mtp	67.0	868.8	2.795	3.51 GB	07-11
Qwopus3.5-4B-Coder-oQ5e-mtp	61.5	895.4	2.996	3.93 GB	07-11
Qwopus3.5-4B-Coder-oQ4-mtp	63.4	1053.1	3.072	3.51 GB	07-01
Qwen3.5-9B-oQ4e-mtp	49.7	1515.7	4.111	5.96 GB	07-11
Qwopus3.5-9B-Coder-oQ4e-mtp	48.0	1475.0	4.164	5.96 GB	07-11
Qwopus3.5-9B-Coder-oQ4-mtp	38.1	1735.2	5.092	5.96 GB	07-08
Qwopus3.5-9B-Coder-oQ5e-mtp	28.9	2390.5	6.850	7.00 GB	07-11

Intelligence Comparison (Accuracy %)

Note: Each benchmark round tests only 30 questions. Results are for reference only.

Model	MMLU	TRUTHFULQA	GSM8K	MATHQA	HUMANEVAL	Average
Qwopus3.5-9B-Coder-oQ5e-mtp	80.0	90.0	93.3	36.7	80.0	76.0
Qwopus3.5-9B-Coder-oQ4-mtp	80.0	86.7	83.3	40.0	80.0	74.0
Qwopus3.5-9B-Coder-oQ4e-mtp	76.7	76.7	96.7	36.7	90.0	75.3
Qwen3.5-9B-oQ4e-mtp	76.7	86.7	90.0	26.7	86.7	73.3
Qwopus3.5-4B-Coder-oQ5e-mtp	70.0	66.7	93.3	53.3	76.7	72.0
Qwopus3.5-4B-Coder-oQ4-mtp	63.3	66.7	96.7	43.3	83.3	70.7
Qwopus3.5-4B-Coder-oQ4e-mtp	63.3	63.3	96.7	53.3	76.7	70.7

Key Findings

oQ4 vs oQ4e vs oQ5 Differences

Version	Speed Characteristics	Intelligence Characteristics
oQ4	Medium speed	Better MATHQA/HUMANEVAL
oQ4e	Fastest speed	Highest MATHQA (53.3% for 4B series), but slightly lower MMLU/TRUTHFULQA
oQ5	Slowest speed	Highest MMLU/TRUTHFULQA (more conservative quantization)

4B vs 9B Series

Dimension	4B Coder	9B Coder
Speed	40%+ faster (61-67 vs 29-48 tok/s)	Slower
MMLU	63-70%	76-80%
GSM8K	93-97%	83-97%
MATHQA	43-53%	27-40%
HUMANEVAL	77-83%	80-90%
Memory	3.5-4.0 GB	6.0-7.0 GB

Specialized Capabilities

Task	Best Model	Accuracy
Knowledge Q&A (MMLU)	9B-Coder-oQ4/oQ5e	80.0%
Truthfulness (TRUTHFULQA)	9B-Coder-oQ5e	90.0%
Math Reasoning (GSM8K)	4B-Coder-oQ4/oQ4e	96.7%
Math Calculation (MATHQA)	4B-Coder-oQ4e/oQ5e	53.3%
Code Generation (HUMANEVAL)	9B-Coder-oQ4e	90.0%

Recommendations

Scenario	Recommended Model	Reason
Ultra-fast Interaction	Qwopus3.5-4B-Coder-oQ4e-mtp	67 tok/s + 3.5GB memory
Balanced Intelligence	Qwopus3.5-9B-Coder-oQ5e-mtp	76% average, strongest knowledge+truthfulness
Code Generation	Qwopus3.5-9B-Coder-oQ4e-mtp	HUMANEVAL 90%
Math Reasoning	Qwopus3.5-4B-Coder-oQ4e-mtp	GSM8K 96.7% + MATHQA 53.3%
Low Memory Devices	Qwopus3.5-4B-Coder-oQ4e-mtp	Only 3.51 GB
Balanced Choice	Qwen3.5-9B-oQ4e-mtp	Balanced speed+intelligence, 73.3% average

Usage

from mlx_lm import load, generate

# Load model
model, tokenizer = load("mlx-works/Qwopus3.5-9B-Coder-oQ4e-mtp")

# Generate text
prompt = "Hello, how are you?"
response = generate(model, tokenizer, prompt=prompt, max_tokens=100)
print(response)

Notes

• This model uses Native MTP (Multi-Token Prediction) for improved speed
• Thinking mode is disabled for faster inference
• For detailed benchmark methodology, see the oQ repository