Instructions to use SandLogicTechnologies/Qwen3.5-9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SandLogicTechnologies/Qwen3.5-9B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="SandLogicTechnologies/Qwen3.5-9B-GGUF",
	filename="Qwen3.5-9B_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/Qwen3.5-9B-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/Qwen3.5-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf SandLogicTechnologies/Qwen3.5-9B-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/Qwen3.5-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf SandLogicTechnologies/Qwen3.5-9B-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/Qwen3.5-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf SandLogicTechnologies/Qwen3.5-9B-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/Qwen3.5-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SandLogicTechnologies/Qwen3.5-9B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/SandLogicTechnologies/Qwen3.5-9B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use SandLogicTechnologies/Qwen3.5-9B-GGUF with Ollama:
```
ollama run hf.co/SandLogicTechnologies/Qwen3.5-9B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use SandLogicTechnologies/Qwen3.5-9B-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/Qwen3.5-9B-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/Qwen3.5-9B-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/Qwen3.5-9B-GGUF to start chatting

Pi new

How to use SandLogicTechnologies/Qwen3.5-9B-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/Qwen3.5-9B-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/Qwen3.5-9B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use SandLogicTechnologies/Qwen3.5-9B-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/Qwen3.5-9B-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/Qwen3.5-9B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use SandLogicTechnologies/Qwen3.5-9B-GGUF with Docker Model Runner:
```
docker model run hf.co/SandLogicTechnologies/Qwen3.5-9B-GGUF:Q4_K_M
```

Lemonade

How to use SandLogicTechnologies/Qwen3.5-9B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SandLogicTechnologies/Qwen3.5-9B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-9B-GGUF-Q4_K_M

List all available models

lemonade list

Qwen3.5-9B

Qwen3.5-9B is a high-capacity multimodal model from the Qwen 3.5 family, designed to handle complex reasoning tasks across both visual and textual inputs. With a significantly larger parameter count compared to smaller variants, it delivers stronger logical reasoning, deeper contextual understanding, and improved performance on challenging tasks.

The model supports multimodal interactions, enabling it to process text, images, and even extended content such as long documents or structured visual inputs. It is optimized for advanced conversational AI, analytical tasks, and real-world applications that require higher intelligence and scalability.

Qwen3.5-9B is particularly effective in scenarios involving multi-step reasoning, academic problem solving, and multilingual communication, while maintaining efficient deployment characteristics for its size.

Model Overview

Model Name: Qwen3.5-9B
Base Model: Qwen3.5-9B
Architecture: Decoder-only Transformer with multimodal extensions
Parameter Count: ~ 9 Billion
Context Window: Up to ~262K tokens
Modalities: Text, Image (multimodal input support)
Primary Languages: English, Chinese, multilingual support
Developer: Qwen (Alibaba Cloud)
License: Apache 2.0

Quantization Details

Q4_K_M

Approx. ~ 68% size reduction compared to FP16
Model size ~ 5.24 GB
Suitable for local inference on consumer hardware
Balanced trade-off between performance and efficiency
Recommended for general-purpose usage with limited VRAM

Q5_K_M

Higher precision compared to Q4 variants
Approx. ~ 63% size reduction compared to FP16
Model size ~ 6.07 GB
Improved reasoning consistency and response quality
Better suited for longer context tasks and multi-turn conversations
Recommended when additional GPU/CPU memory is available

Training Overview

Pretraining

The model is trained on a large-scale dataset combining diverse textual corpora and multimodal data sources, enabling it to understand both language and visual information in a unified manner.

Training objectives include:

Cross-modal representation learning
Large-scale language modeling
Visual-text alignment
Contextual reasoning across long sequences

Alignment and Optimization

Post-training steps refine the model for real-world usability and instruction-following:

Instruction tuning for conversational tasks
Reinforcement learning and alignment techniques
Optimization for reasoning-heavy prompts
Enhanced multimodal grounding and response coherence

Core Capabilities

Advanced instruction following
Accurately interprets complex prompts involving text and visual inputs.
Strong reasoning performance
Handles multi-step logical problems, mathematical reasoning, and analytical tasks effectively.
Long-context understanding
Processes very long documents and conversations with extended context windows.
Multilingual support
Capable of understanding and generating content across multiple languages.
Multimodal intelligence
Interprets images and combines them with textual input for richer responses.
Conversational consistency
Maintains coherent dialogue across long multi-turn interactions.

Example Usage

llama.cpp

./llama-cli \
  -m SandlogicTechnologies\Qwen3.5-9B_Q4_K_M.gguf \
  -p "Explain the concept of attention in transformer models."

Recommended Use Cases

Advanced conversational AI systems
Research assistants for complex problem-solving
Multimodal question answering
Document and report analysis
Educational tools and tutoring systems
Code explanation and technical reasoning
Long-context summarization and analysis
Prototyping intelligent multimodal 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 Qwen3.5-9B 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.

Downloads last month: 9

GGUF

Model size

9B 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/Qwen3.5-9B-GGUF

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Quantized

(251)

this model