aquif-3.6-1B is a hybrid reasoning model that automatically determines when and how deeply to think based on query complexity. Built on aquif-3.5-Nano-1B with AutoThink RL data, it achieves 28% better token efficiency and 4% performance improvement across benchmarks.
Contents
Key Features - Dynamic reasoning, efficiency gains, and smart resource allocation
Performance - Benchmark results showing 4% average improvement
aquif-3.6-1B is a hybrid reasoning model that dynamically decides if and how much to think based on query complexity. Inspired by aquif-3.6-8B's approach of automatic thinking using AutoThink RL data on top of aquif-3.5-Nano-1B, the model uses the following format:
<judge>
[analyzes whether to think or not]
</judge>
<think_on/off>
<think>
[thinking content]
</think>
<answer>
</answer>
This is the same format as aquif-3.6-8B. Unlike something like aquif-3.5-Plus's toggleable reasoning that requires manual control (thinking_on/off), aquif-3.6's judge autonomously allocates reasoning depth - intelligently adapting its cognitive effort to each task automatically.
Key Features
🧠 Dynamic Reasoning: Automatically determines when and how deeply to think
⚡ 28% More Efficient: Significant token reduction while improving performance
📈 Better Performance: 4% average improvement across benchmarks
🎯 Smart Resource Allocation: 12% reduction in thinking ratio on average
Performance
Benchmark
aquif-3.6-1B
Qwen3-1.7B
Improvement
AIME 2025
75.0
39.4
+35.6%
LiveCodeBench
57.5
33.2
+24.3%
GPQA Diamond
52.8
40.1
+12.7%
Average
61.8
37.6
+24.2%
Token Efficiency
Benchmark
aquif-3.6-1B
Qwen3-1.7B
Reduction
AIME 2025
13,670
18,450
-26%
LiveCodeBench
10,270
13,890
-26%
GPQA Diamond
6,870
12,100
-43%
Average
10,270
14,813
-32%
Thinking Ratio
Benchmark
aquif-3.6-1B
Qwen3-1.7B
Reduction
AIME 2025
84.0%
100.0%
-16%
LiveCodeBench
78.0%
100.0%
-22%
GPQA Diamond
81.0%
100.0%
-19%
Average
81.0%
100.0%
-19%
Benchmark Highlights
AIME 2025: 26% fewer tokens, +35.6% performance, -16% thinking ratio
LiveCodeBench: 26% fewer tokens, +24.3% performance, -22% thinking ratio
GPQA Diamond: 43% fewer tokens, +12.7% performance, -19% thinking ratio
Model Details
Base Model: 1.7B parameters
Architecture: Hybrid reasoning with dynamic thinking allocation
Context Length: 40K tokens
License: Apache 2.0
Usage
Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
bash
1brew install llama.cpp
2
Invoke the llama.cpp server or the CLI.
CLI:
llama-cli --hf-repo Edge-Quant/aquif-3.6-1B-Q4_K_M-GGUF --hf-file aquif-3.6-1b-q4_k_m.gguf -p "The meaning to life and the universe is"
Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
git clone https://github.com/ggerganov/llama.cpp
Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
cd llama.cpp && LLAMA_CURL=1 make
Step 3: Run inference through the main binary.
./llama-cli --hf-repo Edge-Quant/aquif-3.6-1B-Q4_K_M-GGUF --hf-file aquif-3.6-1b-q4_k_m.gguf -p "The meaning to life and the universe is"
