M1n1A1/MiniAI-Quata1-4b

TEXT GENERATIONConcurrency Cost:1Model Size:4BQuant:BF16Ctx Length:32kTool Calling:SupportedPublished:Jun 18, 2026License:apache-2.0Architecture:Transformer Open Weights Cold

MiniAI's Quata1-4B-Instruct is a 4 billion parameter instruction-tuned model, fine-tuned from Qwen3-4B. Developed by MiniAI, it is optimized for efficient local deployment on consumer GPUs, offering a balance of size and performance. This model is designed for general instruction-following tasks and is notable as one of the first models developed in the Balkans.

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Quata1-4B-Instruct Overview

Quata1-4B-Instruct, developed by MiniAI, is a 4 billion parameter instruction-tuned model based on Qwen3-4B. It is specifically designed to be efficient and run on consumer-grade GPUs, making it accessible for local deployment. This model is also notable as the first fully made instruct model from the Balkans.

Key Capabilities & Features

  • Optimized for Local Use: Engineered to run efficiently on a single consumer GPU, supporting platforms like LM Studio, Ollama, and transformers.
  • Fully Merged Weights: LoRA weights are integrated directly into the base model, simplifying deployment and further quantization (e.g., GGUF, AWQ) without adapter management.
  • VRAM Friendly: Designed with hardware constraints in mind, making it suitable for systems with limited VRAM.
  • Training Details: Fine-tuned using Unsloth on the Alpaca-Cleaned dataset, with 4-bit quantization during training over 2 epochs.

Recommended Use Cases

  • Hobbyist and Research Projects: Ideal for experimentation and development due to its small size and local-first design.
  • Resource-Constrained Environments: Suitable for users with limited computational resources who require a capable instruction-following model.
  • General Instruction Following: Performs well on a variety of instruction-based tasks, punching above its weight class for a 4B model.

Limitations

  • Inherits biases and limitations from its Qwen3-4B base model.
  • Has not undergone formal safety alignment beyond the base model's provisions.
  • Intended for research and hobbyist use, not evaluated for production or safety-critical applications.

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