MLX
Safetensors
qwen3_5_moe
oq
quantized
benchmark
performance
mtp
Mixture of Experts
code
agentic-coding
4-bit precision
Instructions to use mlx-works/KAT-Coder-V2.5-Dev-oQ4e-mtp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-works/KAT-Coder-V2.5-Dev-oQ4e-mtp with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir KAT-Coder-V2.5-Dev-oQ4e-mtp mlx-works/KAT-Coder-V2.5-Dev-oQ4e-mtp
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
| library_name: mlx | |
| tags: | |
| - mlx | |
| - oq | |
| - quantized | |
| - benchmark | |
| - performance | |
| - mtp | |
| - moe | |
| - code | |
| - agentic-coding | |
| # KAT-Coder-V2.5-Dev-oQ4e-mtp | |
| This model was quantized using [oQ](https://github.com/jundot/omlx) (oMLX v0.5.4) mixed-precision quantization. | |
| - Base model: [Kwaipilot/KAT-Coder-V2.5-Dev](https://huggingface.co/Kwaipilot/KAT-Coder-V2.5-Dev) | |
| - MTP head: [Qwen/Qwen3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B) (original MTP head grafted) | |
| - Chat template: [Qwen Fixed Chat Template](https://huggingface.co/froggeric/Qwen-Fixed-Chat-Templates) (v21) — fixes KV cache optimization, thinking mode control, tool calling, and agent loop issues. Original backed up as `chat_template.jinja.bak`. | |
| ## Quantization details | |
| - **Model type**: qwen3_5_moe | |
| - **Bits**: 4 | |
| - **Group size**: 64 | |
| - **Format**: MLX safetensors | |
| - **MTP**: Grafted from Qwen3.6-35B-A3B (bf16 original) | |
| - **Calibration**: oQ4e (enhanced, imatrix-based) | |
| ## Environment | |
| - **Hardware**: M5 MacBook Air 32GB | |
| - **Inference Framework**: oMLX v0.5.4 | |
| - **Max Concurrent Requests**: 4 | |
| - **Settings**: | |
| - Thinking: Disabled | |
| - TurboQuant KV Cache: Enabled (4-bit) | |
| - Lightning 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 | 1180.7 | 19.88 | 867.3 tok/s | 50.7 tok/s | 3.723 | 309.4 tok/s | 21.72 GB | | |
| | pp4096/tg128 | 4048.1 | 22.43 | 1011.8 tok/s | 44.9 tok/s | 6.926 | 609.8 tok/s | 22.47 GB | | |
| ### Continuous Batching (pp1024 / tg128) | |
| | Batch | tg TPS | Speedup | pp TPS | pp TPS/req | TTFT(ms) | E2E(s) | | |
| |-------|--------|---------|--------|------------|----------|--------| | |
| | 1x | 50.7 tok/s | 1.00x | 867.3 tok/s | 867.3 tok/s | 1180.7 | 3.723 | | |
| | 2x | 61.1 tok/s | 1.21x | 802.5 tok/s | 401.3 tok/s | 2552.0 | 6.743 | | |
| | 4x | 86.4 tok/s | 1.70x | 790.3 tok/s | 197.6 tok/s | 5033.6 | 11.108 | | |
| ## Intelligence Benchmark | |
| > **Note**: Each benchmark round tests only 30 questions. Results are for reference only. | |
| | Benchmark | Accuracy | Correct | Total | Time(s) | Think | | |
| |-----------|----------|---------|-------|---------|-------| | |
| | MMLU | 70.0% | 21 | 30 | 53.4 | No | | |
| | TRUTHFULQA | 96.7% | 29 | 30 | 17.1 | No | | |
| | GSM8K | 93.3% | 28 | 30 | 83 | No | | |
| | MATHQA | 43.3% | 13 | 30 | 49.5 | No | | |
| | HUMANEVAL | 90.0% | 27 | 30 | 125.9 | No | | |