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aquif-3-micro-GGUF

Text Generation
Transformers
GGUF
English
language
aquif
gpt2
text-generation-inference
small
efficient
conversational
Model card Files
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aquif-3-micro-GGUF
File size: 116,049 Bytes 
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---
pipeline_tag: text-generation
inference: false
license: apache-2.0
library_name: transformers
tags:
- language
- aquif
- gpt2
- text-generation-inference
- small
- efficient
language:
- en
---

# aquif-3-micro

A compact and efficient 434M parameter language model optimized for general-purpose text generation tasks. aquif-3-micro delivers competitive performance while maintaining a small footprint, making it ideal for resource-constrained environments and applications requiring fast inference.

## Model Details

**Parameter Count**: 434 million parameters  
**Base Architecture**: GPT-2 style transformer  
**License**: Apache 2.0  
**Primary Focus**: General text generation with emphasis on efficiency

## Performance Benchmarks

aquif-3-micro demonstrates strong performance across multiple evaluation benchmarks compared to other models in similar parameter ranges:

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" width="512px" alt="Benchmark Performance Comparison">

| Model | Parameters | GSM8K | MMLU | GPQA | Average |
|-------|------------|-------|------|------|---------|
| **aquif-3-micro** | 434M | **52.1** | **55.6** | **28.5** | **45.4** |
| LFM2 | 742M | 46.4 | 49.9 | 28.5 | 41.6 |
| Gemma 3 | 1B | 59.9 | 40.1 | 21.1 | 40.4 |
| Llama 3.2 | 1B | 35.7 | 46.6 | 28.8 | 37.0 |
| Qwen3 | 752M | 36.5 | 44.9 | 22.1 | 34.5 |

The model achieves the highest average score (45.4) while using fewer parameters than most competitors, demonstrating excellent parameter efficiency.

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width="512px" alt="Parameter Efficiency Scatter Plot">

## Key Features

- **Compact Size**: 434M parameters for efficient deployment
- **Strong Performance**: Competitive results across diverse benchmarks
- **General Purpose**: Capable across multiple domains including reasoning, knowledge, and problem-solving
- **Resource Efficient**: Optimized for inference speed and memory usage

## Usage

Load the model using Hugging Face transformers:

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "aquiffoo/aquif-3-micro"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
inputs = tokenizer("Your prompt here", return_tensors="pt")
outputs = model.generate(**inputs, max_length=100, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
```

## Intended Use

aquif-3-micro is designed for:
- General text generation tasks
- Applications requiring efficient inference
- Educational and research purposes
- Prototyping and development environments
- Edge deployment scenarios

## Limitations

As a compact model, aquif-3-micro may have limitations in:
- Very specialized domain knowledge
- Complex multi-step reasoning tasks
- Tasks requiring extensive world knowledge

## License

This model is released under the Apache 2.0 license.