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
File size: 2,584 Bytes
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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 |
|