Instructions to use SpacemiT/Qwen3.5-0.8B 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/Qwen3.5-0.8B 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/Qwen3.5-0.8B
# Run inference directly in the terminal:
llama cli -hf SpacemiT/Qwen3.5-0.8B

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SpacemiT/Qwen3.5-0.8B
# Run inference directly in the terminal:
llama cli -hf SpacemiT/Qwen3.5-0.8B

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/Qwen3.5-0.8B
# Run inference directly in the terminal:
./llama-cli -hf SpacemiT/Qwen3.5-0.8B

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/Qwen3.5-0.8B
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SpacemiT/Qwen3.5-0.8B

Use Docker

docker model run hf.co/SpacemiT/Qwen3.5-0.8B

LM Studio
Jan

vLLM

How to use SpacemiT/Qwen3.5-0.8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SpacemiT/Qwen3.5-0.8B"
# 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/Qwen3.5-0.8B",
		"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/Qwen3.5-0.8B

Ollama
How to use SpacemiT/Qwen3.5-0.8B with Ollama:
```
ollama run hf.co/SpacemiT/Qwen3.5-0.8B
```

Unsloth Studio

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

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SpacemiT/Qwen3.5-0.8B to start chatting

How to use SpacemiT/Qwen3.5-0.8B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SpacemiT/Qwen3.5-0.8B

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/Qwen3.5-0.8B"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

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

Pull the model

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

Run and chat with the model

lemonade run user.Qwen3.5-0.8B-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use SpacemiT/Qwen3.5-0.8B 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/Qwen3.5-0.8B

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/Qwen3.5-0.8B

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use SpacemiT/Qwen3.5-0.8B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SpacemiT/Qwen3.5-0.8B

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/Qwen3.5-0.8B" \
  --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"

Qwen3.5-0.8B / README.md

alex-spacemit

Fix complete Qwen3.5-0.8B README

4e16011 verified 2 days ago

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

	# Qwen3.5-0.8B for SpacemiT K1/K3

	This is the SpacemiT deployment package for [Qwen/Qwen3.5-0.8B](https://huggingface.co/Qwen/Qwen3.5-0.8B), an Apache-2.0 native multimodal vision-language model for image understanding, text, coding and agent tasks. Please see the Qwen Team's [Qwen3.5 announcement](https://qwen.ai/blog?id=qwen3.5) and cite the original work:

	```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}}
	```

	The text decoder is Q4_1 GGUF and the vision encoder is ONNX. The default configuration uses the 384-pixel encoder; 224- and 768-pixel alternatives are included.

	## Files and platforms

	`qwen3_5vl_0.8b-text-q41.gguf`, three `qwen3_5vl_0.8b-vision-*-op23.f16.onnx` files, and `configs/K1/config.json` / `configs/K3/config.json` are included. K1/X60 uses AI cores 0–3 and `-t 4`; K3/A100 uses AI cores 8–15 and `-t 8`. Use the matching configuration: its `ep_config` sets the SpaceMIT EP affinity.

	## Prerequisites

	Install the [SpacemiT ONNX Runtime release](https://github.com/spacemit-com/onnxruntime/releases) and an SMT-enabled [SpacemiT llama.cpp](https://github.com/spacemit-com/llama.cpp). Prebuilt packages can be unpacked directly:

	```bash
	wget https://github.com/spacemit-com/onnxruntime/releases/download/2.0.6/spacemit-ort.riscv64.2.0.6.tar.gz
	wget https://github.com/spacemit-com/llama.cpp/releases/download/v0.1.7/spacemit-llama.cpp.riscv64.0.1.7.tar.gz
	tar -xf spacemit-ort.riscv64.2.0.6.tar.gz; tar -xf spacemit-llama.cpp.riscv64.0.1.7.tar.gz
	```

	To build llama.cpp, clone recursively, set `RISCV_ROOT_PATH` and `SPACEMIT_ORT_DIR`, then run `bash build_spacemit.sh glibc`.

	## Run

	```bash
	export MODEL_DIR=/path/to/Qwen3.5-0.8B-SpacemiT
	export LLAMA_DIR=/path/to/llama.cpp-installed
	export ORT_DIR=/path/to/spacemit-ort.riscv64.2.0.6
	export LD_LIBRARY_PATH="$LLAMA_DIR/lib:$ORT_DIR/lib:${LD_LIBRARY_PATH:-}"
	```

	K1: `"$LLAMA_DIR/bin/llama-server" -m "$MODEL_DIR/qwen3_5vl_0.8b-text-q41.gguf" --media-backend smt --smt-config-dir "$MODEL_DIR/configs/K1" -t 4 --host 0.0.0.0 --port 8080 --warmup`

	K3: use the same command with `configs/K3` and `-t 8`.

	Send an OpenAI-compatible request to `/v1/chat/completions` with an `image_url` data URL and text such as `Describe the image content.`, `max_tokens: 64`, `temperature: 0`, and `chat_template_kwargs: {"enable_thinking": false}`.

	## Board verification

	Using `humanspeech.jpg`, ORT 2.0.6 and SMT llama.cpp, both boards returned HTTP 200. K1 began `The image captures a lively scene of a speech or presentation...`; K3 began `This image captures a lively scene, likely from a formal event or presentation...`. These are functional smoke tests, not benchmarks.

	The original Qwen3.5 model is Apache-2.0; dependency licenses remain with their respective projects.