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Qwopus3.5-4B-Coder-oQ4e-mtp
mlx-works/Qwopus3.5-4B-Coder-oQ4e-mtp
Qwopus3.5-4B-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 v0.5.1) mixed-precision quantization.
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.safetensors3.3 GB · 99%
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How the weights are stored.
U324.3B · 93%
Model card
From the Hugging Face model README
Qwopus3.5-4B-Coder-oQ4e-mtp
This model was quantized using oQ (oMLX v0.5.1) mixed-precision quantization.
Quantization details
- Model type: qwen3_5
- Bits: 4
- Group size: 64
- Format: MLX safetensors
Environment
- Hardware: M5 MacBook Air 32GB
- Inference Framework: oMLX v0.5.1
- Settings:
- Thinking: Disabled
- Chat template parameter: enable_thinking=false (forced)
- TurboQuant KV Cache: Disabled (if enable: reduces speed)
- Native MTP: Enabled (key speed improvement)
Performance Benchmarks
Note: Results are for reference only and may vary depending on hardware, software configuration, and workload.
Single Request Results
| Test | TTFT(ms) | TPOT(ms) | pp TPS | tg TPS | E2E(s) | Throughput | Peak Mem |
|---|---|---|---|---|---|---|---|
| pp1024/tg128 | 868.8 | 15.05 | 1178.7 tok/s | 67.0 tok/s | 2.795 | 412.1 tok/s | 3.51 GB |
| pp4096/tg128 | 3546.2 | 14.97 | 1155.1 tok/s | 67.3 tok/s | 5.470 | 772.2 tok/s | 4.12 GB |
Continuous Batching (pp1024 / tg128)
| Batch | tg TPS | Speedup | pp TPS | pp TPS/req | TTFT(ms) | E2E(s) |
|---|---|---|---|---|---|---|
| 1x | 67.0 tok/s | 1.00x | 1178.7 tok/s | 1178.7 tok/s | 868.8 | 2.795 |
| 2x | 82.9 tok/s | 1.24x | 1058.3 tok/s | 529.1 tok/s | 1935.2 | 5.025 |
| 4x | 134.4 tok/s | 2.01x | 1053.2 tok/s | 263.3 tok/s | 3758.2 | 7.698 |
Intelligence Benchmark
Note: Each benchmark round tests only 30 questions. Results are for reference only.
| Benchmark | Accuracy | Correct | Total | Time(s) | Think |
|---|---|---|---|---|---|
| MMLU | 63.3% | 19 | 30 | 26.0 | No |
| TRUTHFULQA | 63.3% | 19 | 30 | 10.3 | No |
| GSM8K | 96.7% | 29 | 30 | 121.4 | No |
| MATHQA | 53.3% | 16 | 30 | 15.5 | No |
| HUMANEVAL | 76.7% | 23 | 30 | 123.7 | No |
Summary
| Benchmark | Accuracy |
|---|---|
| MMLU | 63.3% |
| TRUTHFULQA | 63.3% |
| GSM8K | 96.7% |
| MATHQA | 53.3% |
| HUMANEVAL | 76.7% |
| Average | 70.7% |
- Library
- mlx
- Type
- qwen3_5
- Created
- Jul 11, 2026
- Updated
- Jul 15, 2026