aquiffoo/aquif-3.5-A0.6B-Preview

aquif-3.5

The aquif-3.5 series is the successor to aquif-3, featuring a simplified naming scheme, expanded Mixture of Experts (MoE) options, and across-the-board performance improvements. This release streamlines model selection while delivering enhanced capabilities across reasoning, multilingual support, and general intelligence tasks.

Model Repository Links

Model	HuggingFace Repository
aquif-3.5-A0.6B-Preview	aquiffoo/aquif-3.5-A0.6B-Preview
aquif-3.5-3B	aquiffoo/aquif-3.5-3B
aquif-3.5-7B	aquiffoo/aquif-3.5-7B
aquif-3.5-8B-Think	aquiffoo/aquif-3.5-8B-Think
aquif-3.5-A4B-Think	aquiffoo/aquif-3.5-A4B-Think

Model Overview

Model	Size (B)	Active Params (B)	Reasoning	MoE	Multilingual	MMLU	Context Window
aquif-3.5-A0.6B	2.61	0.6	❌	✅	✅	60.5%	4k
aquif-3.5-3B	2.67	2.67	❌	❌	✅	70.2%	32k
aquif-3.5-7B	7.3	7.3	❌	❌	✅	78.5%	16k
aquif-3.5-8B-Think	8.2	8.2	✅	❌	✅	81.1%	40k
aquif-3.5-A4B-Think	12	4	✅	✅	✅	86.9%	128k

Model Details

aquif-3.5-A0.6B (Experimental MoE)

An experimental small-scale Mixture of Experts model designed for multilingual applications with minimal computational overhead. Despite its compact active parameter count, it demonstrates competitive performance against larger dense models.

Performance Comparison:

Metric	aquif-3.5 (2.6B A0.6B)	Qwen3 (0.8B)	LFM2 (0.7B)	aquif-3 (0.4B)
MMLU	60.5	44.9	49.9	55.6
GPQA	30.2	22.1	28.5	28.5
GSM8K	50.7	36.5	46.4	52.1
HumanEval	45.2	36.0	40.0	37.4
Average	46.7	34.9	41.2	43.4

aquif-3.5-3B (State-of-the-Art Dense)

The new standard for small dense models, offering optimal performance-per-parameter efficiency for general-purpose applications.

Performance Comparison:

Metric	aquif-3.5 (2.7B)	EXAONE 3.5 (2.4B)	Qwen3 (4B)	Gemma 3 (4B)	Phi-4-mini (3.8B)	Apriel-5B-Instruct (4.8B)	aquif-3 (3.2B)
MMLU (General Knowledge)	70.2	60.4	70.4	59.6	67.3	64.6	67.5
GPQA Diamond (Science)	35.8	28.4	39.3	30.9	25.2	28.4	36.1
LiveCodeBench (Coding)	23.1	12.5	21.3	11.2	10.4	11.6	15.4
IFEval (Instruction Following)	78.9	73.6	71.2	80.2	68.6	80.8	78.9
AIME 2025 (Competition Math)	13.4	4.5	9.8	12.7	5.3	4.3	9.6
Average	44.3	35.9	42.4	38.9	35.4	37.9	41.5

aquif-3.5-7B (Multilingual Long Context)

A Qwen-based architecture optimized for multilingual applications with extended context capabilities, delivering state-of-the-art performance in its size class.

Performance Comparison:

Metric	aquif-3.5 (7.3B)	EXAONE 3.5 (7.8B)	Qwen3 (8.2B)	Gemma 3 (12B)	Llama 3.1 (8B)	Kanana 1.5 (8B)	aquif-3 (3.2B)
MMLU (General Knowledge)	78.5	72.2	82.9	74.5	69.2	68.8	67.5
GPQA Diamond (Science)	42.3	39.4	39.3	40.9	32.8	37.5	36.1
LiveCodeBench (Coding)	21.3	18.0	23.9	13.7	10.8	16.5	15.4
IFEval (Instruction Following)	85.6	82.6	85.4	80.2	75.0	80.1	78.9
AIME 2025 (Competition Math)	23.4	18.3	20.9	18.8	2.7	13.4	9.6
Average	50.2	46.1	50.4	45.6	38.1	43.3	41.5

aquif-3.5-8B-Think & aquif-3.5-A4B-Think (Reasoning Models)

Advanced reasoning-capable models designed for complex problem-solving tasks. The A4B variant leverages MoE architecture for enhanced efficiency while maintaining superior reasoning performance.

Performance Comparison:

Metric	aquif-3.5 (12B A4B)	aquif-3.5 (8B)	Qwen3 Thinking 2507 (31B A3B)	gpt-oss-20b (21B A4B)	Nemotron Nano v2 (9B)	Solar Pro 2
MMLU-Pro	78.5	78.1	80.5	73.6	74.2	80.5
GPQA Diamond	70.8	66.8	70.7	61.7	64.0	68.7
AIME 2025	84.4	81.4	56.3	61.7	69.7	61.3
LiveCodeBench	66.1	61.5	70.7	72.1	71.1	61.6
Humanity's Last Exam	8.9	8.2	9.8	8.5	6.5	7.0
TAU-Bench v2 (avg)	43.7	36.8	35.7	43.2	34.9	38.7
Average	58.7	55.5	54.0	53.5	53.4	53.0

Key Improvements Over aquif-3

• Simplified Naming: Clear size-based nomenclature for easier model selection
• Enhanced MoE Support: Multiple MoE configurations across different model sizes
• Reasoning Capabilities: Dedicated thinking models for complex problem-solving
• Extended Context: Up to 128k context window for long-form applications
• Multilingual by Default: Native multilingual support across all variants
• Performance Gains: 5-15% improvement across benchmarks compared to aquif-3

Usage Recommendations

• aquif-3.5-A0.6B: Experimental applications, resource-constrained environments
• aquif-3.5-3B: General-purpose applications, balanced performance/efficiency
• aquif-3.5-7B: Multilingual applications, long-context tasks
• aquif-3.5-8B-Think: Complex reasoning, scientific analysis
• aquif-3.5-A4B-Think: Advanced reasoning with efficiency optimization

Technical Specifications

All models support:

• BF16 and FP16 precision
• Standard transformer architecture optimizations
• Efficient attention mechanisms
• Multi-head attention with optimized KV caching

Acknowledgements

• Qwen Team: Base architecture for 7B, 8B, and 12B-A4B models
• Meta Llama Team: Base architecture for 3B and 2.6B-A0.6B models
• Hugging Face: Model hosting infrastructure and training libraries

License

This project is released under the Apache 2.0 License. See LICENSE file for details.

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