Instructions to use DimInfer/Qwen3.8-27B-Dspark-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use DimInfer/Qwen3.8-27B-Dspark-v1 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 DimInfer/Qwen3.8-27B-Dspark-v1:Q8_0 # Run inference directly in the terminal: llama cli -hf DimInfer/Qwen3.8-27B-Dspark-v1:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf DimInfer/Qwen3.8-27B-Dspark-v1:Q8_0 # Run inference directly in the terminal: llama cli -hf DimInfer/Qwen3.8-27B-Dspark-v1:Q8_0
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 DimInfer/Qwen3.8-27B-Dspark-v1:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf DimInfer/Qwen3.8-27B-Dspark-v1:Q8_0
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 DimInfer/Qwen3.8-27B-Dspark-v1:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf DimInfer/Qwen3.8-27B-Dspark-v1:Q8_0
Use Docker
docker model run hf.co/DimInfer/Qwen3.8-27B-Dspark-v1:Q8_0
- LM Studio
- Jan
- Ollama
How to use DimInfer/Qwen3.8-27B-Dspark-v1 with Ollama:
ollama run hf.co/DimInfer/Qwen3.8-27B-Dspark-v1:Q8_0
- Unsloth Studio
How to use DimInfer/Qwen3.8-27B-Dspark-v1 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 DimInfer/Qwen3.8-27B-Dspark-v1 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 DimInfer/Qwen3.8-27B-Dspark-v1 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for DimInfer/Qwen3.8-27B-Dspark-v1 to start chatting
- Pi
How to use DimInfer/Qwen3.8-27B-Dspark-v1 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf DimInfer/Qwen3.8-27B-Dspark-v1:Q8_0
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": "DimInfer/Qwen3.8-27B-Dspark-v1:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use DimInfer/Qwen3.8-27B-Dspark-v1 with Docker Model Runner:
docker model run hf.co/DimInfer/Qwen3.8-27B-Dspark-v1:Q8_0
- Lemonade
How to use DimInfer/Qwen3.8-27B-Dspark-v1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull DimInfer/Qwen3.8-27B-Dspark-v1:Q8_0
Run and chat with the model
lemonade run user.Qwen3.8-27B-Dspark-v1-Q8_0
List all available models
lemonade list
- Hermes Agent
How to use DimInfer/Qwen3.8-27B-Dspark-v1 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf DimInfer/Qwen3.8-27B-Dspark-v1:Q8_0
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 DimInfer/Qwen3.8-27B-Dspark-v1:Q8_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use DimInfer/Qwen3.8-27B-Dspark-v1 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf DimInfer/Qwen3.8-27B-Dspark-v1:Q8_0
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 "DimInfer/Qwen3.8-27B-Dspark-v1:Q8_0" \ --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"
Beats native MTP head by 10-20% across the board
The quantization targeting here is very effective, I am running mostly Q4 and Q5 models and this dspark head is winning against all built-in MTP heads.
Also, I'd like to point out that your model beats magnitudedev's Dspark head, by quite a margin.
A trade off for VRAM, for sure. I am interested in Q6 or Q4 quantizations' relative performance now. I will report back to let you know whether they represent useful alternatives.
Yeah man the draft model seems to generalize well. I'm interested in Q6 to save a bit more space for mmprojector or context. If it works we can potentially remove the vendor mtp head completely for further saving.
Thanks everyone for trying it out! We'll keep training new versions to improve the acceptance rate in agent scenarios β this version was actually trained on just 1/10 of our data.