Quatfit/Quatfit-Mini

🚀 Quatfit Mini

Fast • Compact • Multimodal • Long Context • Agentic

📄 Technical Report

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Quatfit Mini is an 8-billion-parameter multimodal foundation model developed by Quatfit AI Research.

Built for practical intelligence, Quatfit Mini combines advanced reasoning, multimodal understanding, coding capabilities, long-context processing, and agentic tool use in an efficient architecture optimized for real-world deployment.

Supporting 131K context, native vision and audio understanding, and up to 4× faster inference than conventional 8B models, Quatfit Mini delivers frontier-level capabilities while remaining accessible on consumer hardware.

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✨ Key Features

• 🧠 Native Multimodal Architecture
• ⚡ Up to 4× Faster Inference
• 📚 131K Token Context Window
• 💻 Strong Coding Performance
• 🖼️ Vision Understanding
• 🎙️ Audio Understanding
• 🤖 Agentic Tool Calling
• 🪶 Consumer GPU Optimized
• 🌍 Multilingual

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📊 Performance Highlights

Benchmark	Score
Overall Accuracy	89.08%
Coding	92.5%
Science	91.7%
Agentic Tasks	92.5%
CLI	95.0%
Exams	93.3%
Finance	90.0%
Social Intelligence	90.0%

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🏗 Architecture

Quatfit Mini is built on the Quatfit 1 Architecture, engineered for efficient multimodal intelligence.

Language Model

Component	Value
Parameters	8B
Layers	42
Hidden Size	2560
Attention Heads	8
KV Heads	2
Shared KV Layers	18
Feed Forward	GeGLU
Precision	BF16
Vocabulary	262K
Context Length	131,072

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Vision Encoder

• Vision Transformer
• 16 Transformer Layers
• 280 Visual Tokens
• Patch Size: 16×16
• Pan & Scan High-Resolution Support

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Audio Encoder

• Conformer Architecture
• 12 Layers
• Streaming Compatible
• Causal Chunk Attention

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⚡ Performance Optimizations

Quatfit Mini integrates multiple inference optimizations, including:

• Flash Attention 3
• Sliding Window Attention
• Grouped Query Attention (GQA)
• KV Cache Sharing
• Speculative Decoding
• GGUF Quantization

Inference Speed

Configuration	Relative Speed
Standard 8B Model	1×
Quatfit Mini BF16	2.5×
BF16 + Speculative Decoding	3.9×
GGUF Q4_K_M	4.1×

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📈 Benchmark Breakdown

Domain	Accuracy
Coding	92.5%
Science	91.7%
Agentic Tasks	92.5%
CLI	95.0%
Finance	90.0%
Security	90.0%
Reasoning	88.9%
Expert Knowledge	83.8%
Mathematics	81.3%

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🚀 Quick Start

from transformers import AutoProcessor, AutoModelForImageTextToText

model = AutoModelForImageTextToText.from_pretrained(
    "Quatfit/Quatfit-Mini",
    torch_dtype="auto",
    device_map="auto"
)

processor = AutoProcessor.from_pretrained(
    "Quatfit/Quatfit-Mini"
)

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💬 Example

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "Explain this image."
            },
            {
                "type": "image",
                "image": "example.jpg"
            }
        ]
    }
]

inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    return_tensors="pt"
)

outputs = model.generate(
    **inputs,
    max_new_tokens=512
)

print(processor.decode(outputs[0]))

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💻 GGUF Support

Optimized GGUF builds are available for:

• llama.cpp
• Ollama
• LM Studio
• Jan
• Open WebUI

Recommended Quantizations

Quantization	Approx. VRAM
Q4_K_M	~5 GB
Q5_K_M	~6 GB
Q6_K	~7 GB
Q8_0	~9 GB

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🎯 Recommended Applications

Quatfit Mini is designed for practical AI systems, including:

• AI Assistants
• Agentic AI
• Workflow Automation
• Tool Calling
• Research Copilots
• Long-Document Analysis
• OCR
• Vision-Language Tasks
• Audio Understanding
• Information Retrieval
• General Chat
• MVP Software Development

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📚 Training

Quatfit Mini was trained on approximately 10 trillion tokens, including:

• Web Data
• Programming Code
• Mathematics
• Scientific Literature
• Wikipedia
• Books
• Multilingual Data
• Image-Text Pairs
• Audio Transcriptions

Post-training

• Supervised Fine-Tuning (SFT)
• Reinforcement Learning from Human Feedback (RLHF)
• Constitutional AI Alignment

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🌟 Core Strengths

• ✅ Agentic AI
• ✅ Long-Context Reasoning
• ✅ Tool Use
• ✅ Coding Assistance
• ✅ Vision Understanding
• ✅ Audio Understanding
• ✅ Scientific Knowledge
• ✅ Multilingual Intelligence

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🎯 Intended Use

Quatfit Mini is an 8B multimodal foundation model primarily optimized for agentic AI applications.

It excels at:

• Multi-step reasoning
• Autonomous workflows
• Tool orchestration
• Long-context understanding
• Research assistance
• Document analysis
• Vision-language tasks
• Audio understanding
• Productivity automation

While Quatfit Mini delivers strong programming performance, it is designed as a general-purpose reasoning model rather than a specialized coding model.

It performs well for:

• Code generation
• Debugging
• API development
• Script writing
• Code explanation
• MVP application development

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⚠️ Limitations

Quatfit Mini prioritizes reasoning, multimodal intelligence, and agentic capabilities over benchmark-focused coding performance.

Although highly capable for everyday software development, it is not specifically optimized for:

• Repository-scale software engineering
• Competitive programming
• Enterprise-scale refactoring
• Performance-critical code synthesis

As with all foundation models, outputs should be reviewed before deployment in production or safety-critical environments.

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📖 Citation

@article{quatfitmini2026,
  title={Quatfit Mini: A Compact Multimodal Foundation Model with Up to 4× Faster Inference},
  author={Quatfit AI Research},
  year={2026}
}

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📜 License

Quatfit Mini is released under the Quatfit Non-Commercial License v1.

Commercial licensing is available through Quatfit AI Research.

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🌍 Quatfit AI Research

Building practical AI systems that think, reason, create, and collaborate.

Performance First • Practical Intelligence • Open Innovation

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