Agents-A1-oQ2

mlx-works/Agents-A1-oQ2

Agents-A1-oQ2 is a machine learning model from mlx-works. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for mlx.

This model was quantized using oQ (oMLX v0.5.3) mixed-precision quantization.

Downloads · 30 days

7% of all-time downloads

All-time downloads

320

Public

Parameters

35.1B

13.2 GB on disk

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.safetensors13.1 GB · 100%

Parameter types

How the weights are stored.

U3234.6B · 99%

Model card

From the Hugging Face model README

Agents-A1-oQ2

This model was quantized using oQ (oMLX v0.5.3) mixed-precision quantization.

Base model: Agents-A1

Quantization details

Model type: qwen3_5_moe
Bits: 2
Group size: 64
Format: MLX safetensors

Environment

Hardware: M5 MacBook Air 32GB
Inference Framework: oMLX v0.5.3
Max Concurrent Requests: 4
Settings:
- Thinking: Disabled
- TurboQuant KV Cache: Enabled

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	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

Continuous Batching (pp1024 / tg128)

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

Intelligence Benchmark

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

Library: mlx
Type: qwen3_5_moe
Created: Jul 20, 2026
Updated: Jul 26, 2026