SandLogicTechnologies/Qwen3.5-4B-GGUF

Qwen3.5-4B

Qwen3.5-4B is a mid-scale vision-language model (VLM) from the Qwen family designed to process and reason over both visual and textual inputs. The model supports multimodal interactions where images and text prompts can be combined to generate coherent and context-aware textual responses.

Compared to smaller models in the series, Qwen3.5-4B provides stronger reasoning ability, improved contextual understanding, and more robust multimodal grounding while still maintaining a manageable computational footprint.

The model is capable of interpreting visual content such as objects, scenes, diagrams, screenshots, and documents while leveraging natural language prompts to generate explanations, summaries, or answers.

Its balanced size makes it suitable for research, multimodal AI applications, advanced conversational assistants, and real-world deployments requiring stronger reasoning than lightweight models.

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Model Overview

• Model Name: Qwen3.5-4B
• Base Model: Qwen3.5-4B
• Architecture: Decoder-only Transformer
• Parameter Count: ~4 Billion
• Context Window: Up to 128K tokens
• Modalities: Text, Image
• Primary Languages: English, Chinese, multilingual capability
• Developer: Qwen (Alibaba Cloud)
• License: Apache 2.0

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Quantization Details

Q4_K_M

• Approx. ~66% size reduction compared to FP16
• Model size ~2.52 GB
• Optimized for CPU inference and consumer GPUs
• Suitable for low-VRAM environments
• Faster generation speeds with moderate quality trade-offs

Q5_K_M

• Approx. ~63% size reduction compared to FP16
• Model size ~2.90 GB
• Higher response quality and reasoning stability
• Recommended when additional memory is available
• Better performance in longer conversations

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Training Overview

Pretraining

The base model is trained on a large multimodal dataset containing both image-text pairs and extensive text corpora. This training process enables the model to understand relationships between visual elements and natural language.

Training objectives include:

• Visual-text alignment
• Multimodal representation learning
• Natural language understanding and generation
• Cross-modal reasoning

Alignment and Optimization

Additional fine-tuning stages improve the model’s performance across multimodal and conversational tasks such as:

• Visual question answering
• Image caption generation
• Scene and object recognition
• Document and chart interpretation
• Instruction-following dialogue

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Core Capabilities

• Instruction following
  Responds accurately to user instructions involving text prompts, images, or both.

• Enhanced reasoning ability
  Larger parameter capacity enables stronger reasoning and contextual understanding compared to lightweight variants.

• Multilingual interaction
  Supports multiple languages with particularly strong performance in English and Chinese.

• Visual question answering
  Interprets visual content and answers questions about objects, diagrams, screenshots, or scenes.

• Image-grounded reasoning
  Performs reasoning tasks using information extracted from visual inputs.

• Multimodal conversation
  Maintains coherent dialogue across multiple turns involving images and text.

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Example Usage

llama.cpp

./llama-cli \
  -m SandlogicTechnologies\Qwen3.5-4B_Q4_K_M.gguf \
  -p "Explain how transformer models work."

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Recommended Use Cases

• Multimodal conversational assistants
• Visual question answering systems
• Document and screenshot analysis
• Chart and diagram interpretation
• AI tutoring and educational tools
• Image captioning and visual explanation
• Research assistants combining image and text analysis
• Rapid prototyping of multimodal AI applications

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Acknowledgments

These quantized models are based on the original work by the Qwen development team.

Special thanks to:

• The Qwen team for developing and releasing the Qwen3.5-4B model.

• Georgi Gerganov- and the llama.cpp open-source community for enabling efficient quantization and inference via the GGUF format.

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Contact

For any inquiries or support, please contact us at support@sandlogic.com or visit our Website.