Downloads · 30 days
50
12% of all-time downloads
Qwopus3.5-9B-Coder-oQ4e-mtp
mlx-works/Qwopus3.5-9B-Coder-oQ4e-mtp
Qwopus3.5-9B-Coder-oQ4e-mtp is a machine learning model from mlx-works. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for mlx.
This model was quantized using oQ (oMLX v0.5.0) mixed-precision quantization.
All-time downloads
423
Public
Parameters
9.7B
6.3 GB on disk
Likes
1
Public
Repo makeup
Click a slice to open those files.
.safetensors6.2 GB · 100%
Parameter types
How the weights are stored.
U329.2B · 95%
Model card
From the Hugging Face model README
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
- Library
- mlx
- Type
- qwen3_5
- Created
- Jul 11, 2026
- Updated
- Jul 11, 2026