Lemonade Server Management#

Interact with and manage local or remote Lemonade AI Server hardware directly via standard native network requests ( curl).

When to Use#

• Checking local GPU/NPU health and currently loaded VRAM resources
• Listing available, downloaded text/image models on a Lemonade cluster
• Pulling, loading, or unloading multimodal models
• Generating text from LLMs (chat completions)
• Generating stable-diffusion image responses

Setup Instructions#

1. Every endpoint requires a base server_url. If one is not specified by the user, assume http://localhost:8000.
2. Use the LEMONADE_API_KEY environment variable to authenticate.
3. This skill stays focused on making HTTP requests to a base server_url (default http://localhost:8000) and using an optional LEMONADE_API_KEY. It does not instruct reading arbitrary host files or other env vars.
4. Operational note: The skill will transmit prompts and model requests to whichever server_url is used. If a remote URL is supplied, the agent will send user content and model commands there (expected for this skill, but worth the user's attention).

API Operations#

System Info#

Get hardware capabilities and device enumeration limits.

Returns: JSON object with hardware capabilities.

bash

# Example Request
curl -X GET "http://localhost:8000/api/v1/system-info" \
  -H "Authorization: Bearer ${LEMONADE_API_KEY}"

Health Check#

Verify status and monitor currently loaded models to prevent VRAM overflow.

Returns: JSON object containing server status and currently loaded models.

bash

# Example Request
curl -X GET "http://localhost:8000/api/v1/health" \
  -H "Authorization: Bearer ${LEMONADE_API_KEY}"

List Models#

Get an array of downloaded models available to load into memory.

Returns: JSON list of all downloaded and available models.

bash

# Example Request
curl -X GET "http://localhost:8000/api/v1/models" \
  -H "Authorization: Bearer ${LEMONADE_API_KEY}"

Pull Model#

Download and install a new model string to the target machine.

Returns: JSON stream or object confirming download status.

bash

# Example Request
curl -X POST "http://localhost:8000/api/v1/pull" \
  -H "Authorization: Bearer ${LEMONADE_API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{"model": "llama3"}'

Load Model#

Load a model into VRAM/NPU to prepare for prompt responses.

Returns: JSON object confirming model loaded into memory.

bash

# Example Request
curl -X POST "http://localhost:8000/api/v1/load" \
  -H "Authorization: Bearer ${LEMONADE_API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{"model": "llama3"}'

Unload Model#

Force unload a model to free up memory before loading a larger variant.

Returns: JSON object confirming model unloaded.

bash

# Example Request
curl -X POST "http://localhost:8000/api/v1/unload" \
  -H "Authorization: Bearer ${LEMONADE_API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{"model": "llama3"}'

Chat Completion#

Send a standard chat request to the LLM backend.

Returns: JSON object containing standard chat completion response.

bash

# Example Request
curl -X POST "http://localhost:8000/api/v1/chat/completions" \
  -H "Authorization: Bearer ${LEMONADE_API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama3",
    "messages": [
      {"role": "user", "content": "Hello, world!"}
    ]
  }'

Generate Image#

Submit a stable-diffusion prompt for image generation.

Returns: JSON object containing base64 generated image data.

bash

# Example Request
curl -X POST "http://localhost:8000/api/v1/images/generations" \
  -H "Authorization: Bearer ${LEMONADE_API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "sdxl",
    "prompt": "A majestic lion standing on a cliff, digital art"
  }'

Tips#

• The flm, ryzenai-llm, and whispercpp backends are mutually exclusive on an NPU. Always use the /api/v1/unload endpoint on conflicting LLMs before attempting to load a Whisper model on an NPU.
• Check /api/v1/health first to assess available hardware VRAM before pulling or loading a multi-gigabyte LLM to avoid out-of-memory errors on the host.
• Provide clear feedback to the user if an API request fails due to resource constraints and suggest unloading inactive background models.