Instructions to use StanzaAPI/qwen-moe-stream with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use StanzaAPI/qwen-moe-stream 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 StanzaAPI/qwen-moe-stream:F16
# Run inference directly in the terminal:
llama cli -hf StanzaAPI/qwen-moe-stream:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf StanzaAPI/qwen-moe-stream:F16
# Run inference directly in the terminal:
llama cli -hf StanzaAPI/qwen-moe-stream:F16

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf StanzaAPI/qwen-moe-stream:F16
# Run inference directly in the terminal:
./build/bin/llama cli -hf StanzaAPI/qwen-moe-stream:F16

Use Docker

docker model run hf.co/StanzaAPI/qwen-moe-stream:F16

Llama new
LM Studio
Jan
Ollama
How to use StanzaAPI/qwen-moe-stream with Ollama:
```
ollama run hf.co/StanzaAPI/qwen-moe-stream:F16
```
Unsloth Desktop

How to use StanzaAPI/qwen-moe-stream with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf StanzaAPI/qwen-moe-stream:F16

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": "StanzaAPI/qwen-moe-stream:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use StanzaAPI/qwen-moe-stream with Docker Model Runner:
```
docker model run hf.co/StanzaAPI/qwen-moe-stream:F16
```

Lemonade

How to use StanzaAPI/qwen-moe-stream with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull StanzaAPI/qwen-moe-stream:F16

Run and chat with the model

lemonade run user.qwen-moe-stream-F16

List all available models

lemonade list

Hermes Agent

How to use StanzaAPI/qwen-moe-stream with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf StanzaAPI/qwen-moe-stream:F16

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 StanzaAPI/qwen-moe-stream:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use StanzaAPI/qwen-moe-stream with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf StanzaAPI/qwen-moe-stream:F16

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 "StanzaAPI/qwen-moe-stream:F16" \
  --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"

nirdaddy commited on 14 days ago

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@@ -92,7 +92,7 @@ for the oracle ceiling and the negative result on gradient-trained routers.
 ## Calibration and provenance
-The ridge router was fitted on 384 hidden states from public project READMEs
 and specification documents, dumped with a llama.cpp eval callback. No model
 weights were trained. No private user data was used. The conversion scripts
 and full methodology live at:

 ## Calibration and provenance
+The ridge router was fitted on 384 token positions per layer from public project READMEs
 and specification documents, dumped with a llama.cpp eval callback. No model
 weights were trained. No private user data was used. The conversion scripts
 and full methodology live at: