KAT-Coder
Collection
4 items • Updated
How to use mlx-works/KAT-Coder-V2.5-Dev-oQ2e 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 mlx-works/KAT-Coder-V2.5-Dev-oQ2e
This model was quantized using oQ (oMLX v0.5.4) mixed-precision quantization.
chat_template.jinja.bak.Note: Results are for reference only and may vary depending on hardware, software configuration, and workload.
| Test | TTFT(ms) | TPOT(ms) | pp TPS | tg TPS | E2E(s) | Throughput | Peak Mem |
|---|---|---|---|---|---|---|---|
| pp1024/tg128 | 1103.1 | 20.35 | 928.3 tok/s | 49.5 tok/s | 3.700 | 311.4 tok/s | 12.62 GB |
| pp4096/tg128 | 3794.9 | 21.39 | 1079.3 tok/s | 47.1 tok/s | 6.530 | 646.9 tok/s | 13.34 GB |
| Batch | tg TPS | Speedup | pp TPS | pp TPS/req | TTFT(ms) | E2E(s) |
|---|---|---|---|---|---|---|
| 1x | 49.5 tok/s | 1.00x | 928.3 tok/s | 928.3 tok/s | 1103.1 | 3.700 |
| 2x | 67.5 tok/s | 1.36x | 836.8 tok/s | 418.4 tok/s | 2447.4 | 6.238 |
| 4x | 98.7 tok/s | 1.99x | 825.1 tok/s | 206.3 tok/s | 4822.0 | 10.150 |
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 | 39.5 | No |
| TRUTHFULQA | 86.7% | 26 | 30 | 16.3 | No |
| GSM8K | 93.3% | 28 | 30 | 99.4 | No |
| MATHQA | 20.0% | 6 | 30 | 76.1 | No |
| HUMANEVAL | 86.7% | 26 | 30 | 109.7 | No |
2-bit