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

Notebooks
Google Colab
Kaggle
Local Apps Settings

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

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SpacemiT/Qwen3.5-4B
# Run inference directly in the terminal:
llama cli -hf SpacemiT/Qwen3.5-4B

Install from WinGet (Windows)

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

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 SpacemiT/Qwen3.5-4B
# Run inference directly in the terminal:
./llama-cli -hf SpacemiT/Qwen3.5-4B

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 SpacemiT/Qwen3.5-4B
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SpacemiT/Qwen3.5-4B

Use Docker

docker model run hf.co/SpacemiT/Qwen3.5-4B

LM Studio
Jan

vLLM

How to use SpacemiT/Qwen3.5-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SpacemiT/Qwen3.5-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SpacemiT/Qwen3.5-4B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/SpacemiT/Qwen3.5-4B

Ollama
How to use SpacemiT/Qwen3.5-4B with Ollama:
```
ollama run hf.co/SpacemiT/Qwen3.5-4B
```

Unsloth Studio

How to use SpacemiT/Qwen3.5-4B 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 SpacemiT/Qwen3.5-4B 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 SpacemiT/Qwen3.5-4B to start chatting

Using HuggingFace Spaces for Unsloth

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

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

Start the llama.cpp server

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

Configure the model in Pi

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

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

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

Pull the model

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

Run and chat with the model

lemonade run user.Qwen3.5-4B-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

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

Start the llama.cpp server

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

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 SpacemiT/Qwen3.5-4B

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use SpacemiT/Qwen3.5-4B with OpenClaw:

Start the llama.cpp server

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

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "SpacemiT/Qwen3.5-4B" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Qwen3.5-4B

File size: 2,335 Bytes

28677bb
 
a0f2f85
 
 
28677bb
a0f2f85

---
license: apache-2.0
pipeline_tag: image-text-to-text
library_name: llama.cpp
tags: [qwen3.5, spacemit, k1, k3, gguf, onnxruntime]
---

# Qwen3.5-4B for SpacemiT K1/K3

This is a SpacemiT conversion of [Qwen/Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B), the Apache-2.0 native multimodal model from the Qwen Team. It is intended for visual understanding, text, coding and agent tasks. Read the [official announcement](https://qwen.ai/blog?id=qwen3.5) and cite:

```bibtex
@misc{qwen3.5, title={{Qwen3.5}: Towards Native Multimodal Agents}, author={{Qwen Team}}, month={February}, year={2026}, url={https://qwen.ai/blog?id=qwen3.5}}
```

Contents: `qwen3_5_4b-text-q41.gguf`, ONNX vision encoders at 224/384/768 pixels (384 is the default), and independent K1/K3 configs. The text model is Q4_1 GGUF; vision runs through the SpaceMIT Execution Provider.

Prerequisites are the [SpacemiT ONNX Runtime releases](https://github.com/spacemit-com/onnxruntime/releases) and SMT-enabled [spacemit-com/llama.cpp](https://github.com/spacemit-com/llama.cpp). Prebuilt ORT 2.0.6 and llama.cpp v0.1.7 archives can be unpacked directly. To compile, clone recursively, set `RISCV_ROOT_PATH` and `SPACEMIT_ORT_DIR`, and run `bash build_spacemit.sh glibc`.

K1 uses AI cores 0–3, `configs/K1`, and `-t 4`; K3 uses AI cores 8–15, `configs/K3`, and `-t 8`. Never swap these configs: `ep_config` pins the vision EP to the platform's AI cores.

```bash
export MODEL_DIR=/path/to/Qwen3.5-4B-SpacemiT LLAMA_DIR=/path/to/llama.cpp-installed ORT_DIR=/path/to/spacemit-ort.riscv64.2.0.6
export LD_LIBRARY_PATH="$LLAMA_DIR/lib:$ORT_DIR/lib:${LD_LIBRARY_PATH:-}"
"$LLAMA_DIR/bin/llama-server" -m "$MODEL_DIR/qwen3_5_4b-text-q41.gguf" --media-backend smt --smt-config-dir "$MODEL_DIR/configs/K1" -t 4 --host 0.0.0.0 --port 8080 --warmup
```

For K3 select `configs/K3` and `-t 8`. Send an image data URL plus `Describe the image content.` to `/v1/chat/completions` (64 output tokens, temperature 0, thinking disabled). The bundled smoke image produced HTTP 200 on K1 (description beginning `This image captures a moment from what appears to be a formal event...`) and K3 (beginning `This image captures a moment from what appears to be a conference...`).

The Qwen3.5 weights are Apache-2.0. Runtime dependencies remain under their upstream licenses.