Agents-A1
Collection
1 item • Updated
How to use mlx-works/Agents-A1-oQ2 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Agents-A1-oQ2 mlx-works/Agents-A1-oQ2
This model was quantized using oQ (oMLX v0.5.3) mixed-precision quantization.
Base model: Agents-A1
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 | 1059.4 | 20.91 | 966.6 tok/s | 48.2 tok/s | 3.728 | 309.0 tok/s | 12.59 GB |
| pp4096/tg128 | 3665.4 | 21.35 | 1117.5 tok/s | 47.2 tok/s | 6.400 | 660.0 tok/s | 13.30 GB |
| Batch | tg TPS | Speedup | pp TPS | pp TPS/req | TTFT(ms) | E2E(s) |
|---|---|---|---|---|---|---|
| 1x | 48.2 tok/s | 1.00x | 966.6 tok/s | 966.6 tok/s | 1059.4 | 3.728 |
| 2x | 67.0 tok/s | 1.39x | 864.7 tok/s | 432.4 tok/s | 2368.4 | 6.192 |
| 4x | 97.5 tok/s | 2.02x | 856.3 tok/s | 214.1 tok/s | 4625.5 | 10.032 |
Note: Each benchmark round tests only 30 questions. Results are for reference only.
| Benchmark | Accuracy | Correct | Total | Time(s) | Think |
|---|---|---|---|---|---|
| MMLU | 83.3% | 25 | 30 | 29.4 | No |
| TRUTHFULQA | 83.3% | 25 | 30 | 15.1 | No |
| GSM8K | 90.0% | 27 | 30 | 93.3 | No |
| MATHQA | 46.7% | 14 | 30 | 17.5 | No |
| HUMANEVAL | 83.3% | 25 | 30 | 158 | No |
2-bit