from mlx_lm import load, generate

# Load the model
model, tokenizer = load("hebertgo/knowledgebase-intent-llm")

# Generate intent classification
prompt = '''You are a helpful AI assistant for a knowledge-management app on an iPhone. Analyze the user's request and respond with JSON in this format:
{
  "action": "Search|Create|Clarify|Conversation",
  "response": "User-friendly response message",
  "contentType": "videos|bookmarks|todos",
  "topic": "extracted topic or null"
}

User query: find videos about python'''

response = generate(model, tokenizer, prompt=prompt, max_tokens=256)
print(response)

iOS Integration

This model is designed for use in iOS apps with MLX Swift:

swift

let config = ModelConfiguration(
    id: "hebertgo/knowledgebase-intent-llm",
    defaultPrompt: ""
)

let model = try await LLMModelFactory.shared.loadContainer(
    configuration: config
)

Training Details

Fine-tuning Method: LoRA with model fusion
Export Date: 2025-06-24T17:12:38.111419
Fusion Completed: True

Expected Outputs

The model generates JSON responses with these action types:

Search: Find existing content (videos, bookmarks, todos)
Create: Add new content
Clarify: Request more information
Conversation: General chat responses

Content types supported:

videos
bookmarks
todos

Performance

Optimized for Apple Silicon devices with MLX framework for efficient on-device inference.