MLX
Safetensors
qwen3_5_moe
oq
quantized
benchmark
performance
mtp
Mixture of Experts
code
agentic-coding
2-bit
Instructions to use mlx-works/KAT-Coder-V2.5-Dev-oQ2e-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-oQ2e-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-oQ2e-mtp mlx-works/KAT-Coder-V2.5-Dev-oQ2e-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-oQ2e-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**: 2 | |
| - **Group size**: 64 | |
| - **Format**: MLX safetensors | |
| - **MTP**: Grafted from Qwen3.6-35B-A3B (bf16 original) | |
| - **Calibration**: oQ2e (enhanced, imatrix-based) | |
| ## Environment | |
| - **Hardware**: M5 MacBook Air 32GB | |
| - **Inference Framework**: oMLX v0.5.4 | |
| - **Max Concurrent Requests**: 4 | |
| - **Settings**: | |
| - Thinking: Disabled | |
| - 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 | 1119.8 | 17.25 | 914.4 tok/s | 58.4 tok/s | 3.330 | 346.0 tok/s | 14.20 GB | | |
| | pp4096/tg128 | 4040.2 | 21.25 | 1013.8 tok/s | 47.4 tok/s | 6.754 | 625.4 tok/s | 14.94 GB | | |
| ### Continuous Batching (pp1024 / tg128) | |
| | Batch | tg TPS | Speedup | pp TPS | pp TPS/req | TTFT(ms) | E2E(s) | | |
| |-------|--------|---------|--------|------------|----------|--------| | |
| | 1x | 58.4 tok/s | 1.00x | 914.4 tok/s | 914.4 tok/s | 1119.8 | 3.330 | | |
| | 2x | 79.3 tok/s | 1.36x | 825.0 tok/s | 412.5 tok/s | 2482.5 | 5.711 | | |
| | 4x | 112.2 tok/s | 1.92x | 827.0 tok/s | 206.8 tok/s | 4815.5 | 9.517 | | |
| ## Intelligence Benchmark | |
| > **Note**: Each benchmark round tests only 30 questions. Results are for reference only. | |
| | Benchmark | Accuracy | Correct | Total | Time(s) | Think | | |
| |-----------|----------|---------|-------|---------|-------| | |
| | MMLU | 73.3% | 22 | 30 | 36.3 | No | | |
| | TRUTHFULQA | 86.7% | 26 | 30 | 15.5 | No | | |
| | GSM8K | 96.7% | 29 | 30 | 76.7 | No | | |
| | MATHQA | 13.3% | 4 | 30 | 76.2 | No | | |
| | HUMANEVAL | 90.0% | 27 | 30 | 118.5 | No | | |