Instructions to use SandLogicTechnologies/Qwen3.5-4B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SandLogicTechnologies/Qwen3.5-4B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="SandLogicTechnologies/Qwen3.5-4B-GGUF",
	filename="Qwen3.5-4B_Q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use SandLogicTechnologies/Qwen3.5-4B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SandLogicTechnologies/Qwen3.5-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf SandLogicTechnologies/Qwen3.5-4B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SandLogicTechnologies/Qwen3.5-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf SandLogicTechnologies/Qwen3.5-4B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf SandLogicTechnologies/Qwen3.5-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf SandLogicTechnologies/Qwen3.5-4B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf SandLogicTechnologies/Qwen3.5-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SandLogicTechnologies/Qwen3.5-4B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/SandLogicTechnologies/Qwen3.5-4B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use SandLogicTechnologies/Qwen3.5-4B-GGUF with Ollama:
```
ollama run hf.co/SandLogicTechnologies/Qwen3.5-4B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use SandLogicTechnologies/Qwen3.5-4B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SandLogicTechnologies/Qwen3.5-4B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SandLogicTechnologies/Qwen3.5-4B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SandLogicTechnologies/Qwen3.5-4B-GGUF to start chatting

Pi new

How to use SandLogicTechnologies/Qwen3.5-4B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf SandLogicTechnologies/Qwen3.5-4B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "SandLogicTechnologies/Qwen3.5-4B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use SandLogicTechnologies/Qwen3.5-4B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf SandLogicTechnologies/Qwen3.5-4B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default SandLogicTechnologies/Qwen3.5-4B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use SandLogicTechnologies/Qwen3.5-4B-GGUF with Docker Model Runner:
```
docker model run hf.co/SandLogicTechnologies/Qwen3.5-4B-GGUF:Q4_K_M
```

Lemonade

How to use SandLogicTechnologies/Qwen3.5-4B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SandLogicTechnologies/Qwen3.5-4B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-4B-GGUF-Q4_K_M

List all available models

lemonade list

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.

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

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

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

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.

Example Usage

llama.cpp

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

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

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.

Contact

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

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GGUF

Model size

4B params

Architecture

qwen35

Hardware compatibility

4-bit

5-bit

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Model tree for SandLogicTechnologies/Qwen3.5-4B-GGUF

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