Instructions to use SandLogicTechnologies/Qwen3.5-0.8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SandLogicTechnologies/Qwen3.5-0.8B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="SandLogicTechnologies/Qwen3.5-0.8B-GGUF",
	filename="Qwen3.5-0.8B_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 Settings

llama.cpp

How to use SandLogicTechnologies/Qwen3.5-0.8B-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/Qwen3.5-0.8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf SandLogicTechnologies/Qwen3.5-0.8B-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/Qwen3.5-0.8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf SandLogicTechnologies/Qwen3.5-0.8B-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/Qwen3.5-0.8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf SandLogicTechnologies/Qwen3.5-0.8B-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/Qwen3.5-0.8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SandLogicTechnologies/Qwen3.5-0.8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/SandLogicTechnologies/Qwen3.5-0.8B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use SandLogicTechnologies/Qwen3.5-0.8B-GGUF with Ollama:
```
ollama run hf.co/SandLogicTechnologies/Qwen3.5-0.8B-GGUF:Q4_K_M
```

Unsloth Studio

How to use SandLogicTechnologies/Qwen3.5-0.8B-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/Qwen3.5-0.8B-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/Qwen3.5-0.8B-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/Qwen3.5-0.8B-GGUF to start chatting

How to use SandLogicTechnologies/Qwen3.5-0.8B-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/Qwen3.5-0.8B-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/Qwen3.5-0.8B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use SandLogicTechnologies/Qwen3.5-0.8B-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/Qwen3.5-0.8B-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/Qwen3.5-0.8B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use SandLogicTechnologies/Qwen3.5-0.8B-GGUF with Docker Model Runner:
```
docker model run hf.co/SandLogicTechnologies/Qwen3.5-0.8B-GGUF:Q4_K_M
```

Lemonade

How to use SandLogicTechnologies/Qwen3.5-0.8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SandLogicTechnologies/Qwen3.5-0.8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-0.8B-GGUF-Q4_K_M

List all available models

lemonade list

Qwen3.5-0.8B

Qwen3.5-0.8B is a lightweight 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 meaningful textual responses.

The model is optimized for environments where computational resources are limited but multimodal understanding is still required. It can interpret visual content such as objects, scenes, diagrams, and documents while leveraging natural language prompts to generate contextual explanations and answers.

Despite its compact size, the model enables a variety of multimodal tasks including visual question answering, image captioning, document understanding, and image-grounded reasoning. Its efficient architecture makes it suitable for experimentation, research, and deployment in resource-constrained environments.

Model Overview

Model Name: Qwen3.5-0.8B
Base Model: Qwen3.5-0.8B
Architecture: Decoder-only Transformer
Parameter Count: ~0.8 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. ~ 65% size reduction compared to FP16
Very low memory footprint (~ 503MB)
Optimized for CPU inference and low-VRAM GPUs
Faster token generation speeds
Slight reduction in reasoning depth on complex tasks

Q5_K_M

Approx. ~ 60% size reduction with higher fidelity (~ 557MB)
Slightly larger size than Q4_K_M
Better response quality and reasoning fidelity
Recommended when additional memory is available
Improved stability during longer conversations

Training Overview

Pretraining

The base model is trained on a large multimodal dataset consisting of paired images and text along with large-scale textual corpora. The training process focuses on learning relationships between visual features and natural language representations.

Training objectives include:
Visual-text alignment
Multimodal representation learning
Language understanding and generation
Cross-modal reasoning

Alignment and Optimization

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

Visual question answering
Image caption generation
Scene and object understanding
Document and chart interpretation

Core Capabilities

Instruction adherence Follows user prompts that may include images, textual instructions, or a combination of both.
Efficient inference Designed for fast generation and lightweight deployment.
Multilingual interaction Supports multiple languages with strong English and Chinese capabilities.
Visual question answering CInterprets visual content and answers questions related to objects, scenes, diagrams, or screenshots.
Image-grounded reasoning Performs basic reasoning using information extracted from visual inputs.
Conversational multimodal interaction Maintains context across multi-turn conversations involving both images and text.

Example Usage

llama.cpp

./llama-cli \
  -m SandlogicTechnologies\Qwen3.5-0.8B_Q4_K_M.gguf \
  -p "Explain how transformer models work."

Recommended Use Cases

Multimodal conversational assistants (image + text interactions)
Visual question answering and scene understanding
Document and screenshot analysis
Chart, diagram, and table interpretation
Educational and tutoring applications using visual materials
Image captioning and visual content description
Research assistance involving visual and textual information
Rapid prototyping of multimodal AI applications

Acknowledgments

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

Special thanks to:

The Qwen team for developing and releasing the Qwen3.5-0.8B model.
Georgi Gerganov and the entire llama.cpp open-source community for enabling efficient model quantization and inference via the GGUF format.

Contact

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

Downloads last month: 39

GGUF

Model size

0.8B params

Architecture

qwen35

Hardware compatibility

4-bit

5-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for SandLogicTechnologies/Qwen3.5-0.8B-GGUF

Base model

Qwen/Qwen3.5-0.8B-Base

Finetuned

Qwen/Qwen3.5-0.8B

Quantized

(138)

this model