Instructions to use Baekpica/Qwen3.8-Flash-Next-Mixed-Quant-SSD-PLE-GGUF 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 Baekpica/Qwen3.8-Flash-Next-Mixed-Quant-SSD-PLE-GGUF 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 Baekpica/Qwen3.8-Flash-Next-Mixed-Quant-SSD-PLE-GGUF:BF16 # Run inference directly in the terminal: llama cli -hf Baekpica/Qwen3.8-Flash-Next-Mixed-Quant-SSD-PLE-GGUF:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Baekpica/Qwen3.8-Flash-Next-Mixed-Quant-SSD-PLE-GGUF:BF16 # Run inference directly in the terminal: llama cli -hf Baekpica/Qwen3.8-Flash-Next-Mixed-Quant-SSD-PLE-GGUF:BF16
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 Baekpica/Qwen3.8-Flash-Next-Mixed-Quant-SSD-PLE-GGUF:BF16 # Run inference directly in the terminal: ./llama-cli -hf Baekpica/Qwen3.8-Flash-Next-Mixed-Quant-SSD-PLE-GGUF:BF16
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 Baekpica/Qwen3.8-Flash-Next-Mixed-Quant-SSD-PLE-GGUF:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Baekpica/Qwen3.8-Flash-Next-Mixed-Quant-SSD-PLE-GGUF:BF16
Use Docker
docker model run hf.co/Baekpica/Qwen3.8-Flash-Next-Mixed-Quant-SSD-PLE-GGUF:BF16
- LM Studio
- Jan
- vLLM
How to use Baekpica/Qwen3.8-Flash-Next-Mixed-Quant-SSD-PLE-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Baekpica/Qwen3.8-Flash-Next-Mixed-Quant-SSD-PLE-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Baekpica/Qwen3.8-Flash-Next-Mixed-Quant-SSD-PLE-GGUF", "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/Baekpica/Qwen3.8-Flash-Next-Mixed-Quant-SSD-PLE-GGUF:BF16
- Ollama
How to use Baekpica/Qwen3.8-Flash-Next-Mixed-Quant-SSD-PLE-GGUF with Ollama:
ollama run hf.co/Baekpica/Qwen3.8-Flash-Next-Mixed-Quant-SSD-PLE-GGUF:BF16
- Unsloth Desktop
- Pi
How to use Baekpica/Qwen3.8-Flash-Next-Mixed-Quant-SSD-PLE-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Baekpica/Qwen3.8-Flash-Next-Mixed-Quant-SSD-PLE-GGUF:BF16
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": "Baekpica/Qwen3.8-Flash-Next-Mixed-Quant-SSD-PLE-GGUF:BF16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Baekpica/Qwen3.8-Flash-Next-Mixed-Quant-SSD-PLE-GGUF with Docker Model Runner:
docker model run hf.co/Baekpica/Qwen3.8-Flash-Next-Mixed-Quant-SSD-PLE-GGUF:BF16
- Lemonade
How to use Baekpica/Qwen3.8-Flash-Next-Mixed-Quant-SSD-PLE-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Baekpica/Qwen3.8-Flash-Next-Mixed-Quant-SSD-PLE-GGUF:BF16
Run and chat with the model
lemonade run user.Qwen3.8-Flash-Next-Mixed-Quant-SSD-PLE-GGUF-BF16
List all available models
lemonade list
- Hermes Agent
How to use Baekpica/Qwen3.8-Flash-Next-Mixed-Quant-SSD-PLE-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Baekpica/Qwen3.8-Flash-Next-Mixed-Quant-SSD-PLE-GGUF:BF16
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 Baekpica/Qwen3.8-Flash-Next-Mixed-Quant-SSD-PLE-GGUF:BF16
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Baekpica/Qwen3.8-Flash-Next-Mixed-Quant-SSD-PLE-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Baekpica/Qwen3.8-Flash-Next-Mixed-Quant-SSD-PLE-GGUF:BF16
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 "Baekpica/Qwen3.8-Flash-Next-Mixed-Quant-SSD-PLE-GGUF:BF16" \ --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"
Metal compatibility?
How is your fork running on Metal? I love ds4 and am stoked to see you working on this for 3.8-Flash, but I don't see any discussion of Metal? I am down to give it a try and work on the implementation, but I'd like to get an idea of the starting point.
EDIT: forgot to say cheers for being so responsive to my suggestion! I love this community. Unsloth revised their Q5+ quants to implement my llama.cpp SSD offloading implementation suggestion as well, and it works perfectly!
@nazeshinjite Thanks — and really appreciate the offer to the Metal side.
To be transparent, I don’t currently have enough Metal environment available for development or testing, so since forking ds4 I’ve been fairly focused on Blackwell/CUDA, particularly the DGX Spark. That’s also why there hasn’t been much discussion of Metal in the current Qwen3.8 work.
I do have a Rust rewrite of ds4 planned.('rust-host' in my forked ds4 branch.) One thing I want to be careful about there is preserving ds4’s philosophy of keeping abstractions minimal and the execution path explicit.
At the same time, as more model families are added(because of my personal interest😅), I think it makes sense to establish just enough portability at the model/backend boundaries so that bringing up a new target doesn’t require reshaping the whole runtime every time.
As part of that work, I’ll consider putting Metal support on the roadmap as an explicit milestone. I’ll probably start with a smaller model rather than Qwen3.8-Flash-Next due to a lack of resources, mainly to establish and validate the Metal execution path first.
So at this moment I unfortunately don’t have a useful Metal starting point to hand you, but I’d definitely like to make it one of the targets as the rewrite progresses. Contributions there would be very welcome.
And thanks again for the SSD-offload suggestion — it ended up pushing this implementation in a much more interesting direction than I originally expected.