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---
language: en
license: mit
base_model: mlx-community/Qwen2.5-1.5B-Instruct-4bit
tags:
- mlx
- intent-detection
- fine-tuned
- knowledge-management
- ios
library_name: mlx
---
# knowledgebase-intent-llm
Fine-tuned model for intent detection in a knowledge management iOS app.
## Model Details
- **Base Model**: mlx-community/Qwen2.5-1.5B-Instruct-4bit
- **Model Type**: intent_detection
- **Format**: complete_merged
- **Framework**: MLX
- **Training Examples**: 5000
- **Training Iterations**: 100
## Usage
```python
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.
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