---
library_name: mlx-vlm
tags:
- mlx
- vision-language-model
- fine-tuned
- brake-components
- visual-ai
- lora-adapters
base_model: mlx-community/SmolVLM-256M-Instruct-bf16
---

# NewJob - MLX Fine-tuned Vision Language Model ⚡️

🔥 **REAL MLX FINE-TUNED WEIGHTS INCLUDED** - This model contains actual fine-tuned adapter weights!

## 🚀 Model Details
- **Base Model**: `mlx-community/SmolVLM-256M-Instruct-bf16`
- **Training Platform**: VisualAI (MLX-optimized for Apple Silicon)
- **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
- **GPU Type**: MLX (Apple Silicon)
- **Training Job ID**: 1
- **Created**: 2025-06-03 06:51:02.458447
- **Real Weights**: ✅ YES - Contains actual fine-tuned MLX adapter weights
- **Adapter Weights**: ✅ Found

## 📊 Training Data
This model was fine-tuned on visual brake component data with 3 training examples.

## 🛠️ Usage with REAL Fine-tuned Weights

### Installation
```bash
pip install mlx-vlm
```

### Loading the Fine-tuned Model
```python
from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config
from PIL import Image
import json

# Load the FINE-TUNED MLX model (not base model!)
model_path = "truworthai/Combined-mlx"  # This repo contains the fine-tuned weights

try:
    # Load the fine-tuned model with adapters
    model, processor = load(model_path)
    print("✅ Loaded FINE-TUNED MLX model with learned weights!")
    
    # Load training configuration
    config = load_config(model_path)
    
except Exception as e:
    print(f"⚠️ Loading fine-tuned model failed, falling back to base: {e}")
    # Fallback to base model
    model, processor = load("mlx-community/SmolVLM-256M-Instruct-bf16")
    config = load_config("mlx-community/SmolVLM-256M-Instruct-bf16")
```

### Inference with Fine-tuned Model
```python
# Load your brake component image
image = Image.open("brake_component.jpg")

# Ask brake-specific questions
question = "What is the OEM part number of this brake component?"

# Format the prompt
formatted_prompt = apply_chat_template(processor, config, question, num_images=1)

# Generate response using fine-tuned weights
response = generate(
    model, 
    processor, 
    formatted_prompt, 
    [image], 
    verbose=False, 
    max_tokens=100,
    temp=0.3
)
print(f"Fine-tuned model response: {response}")
```

## 📁 Model Files (REAL WEIGHTS)

This repository contains **ACTUAL fine-tuned model weights**:

### Core Model Files
- `config.json`: Model configuration
- `model.safetensors` or `model.npz`: Base model weights (if included)
- `adapters.safetensors` or `adapters.npz`: **FINE-TUNED LoRA ADAPTER WEIGHTS** ⚡️
- `adapter_config.json`: Adapter configuration
- `tokenizer.json`: Tokenizer configuration
- `preprocessor_config.json`: Image preprocessing config

### Training Artifacts
- `training_args.json`: Training hyperparameters used
- `trainer_state.json`: Training state and metrics
- `mlx_model_info.json`: Training metadata and learned mappings
- `training_images/`: Reference images from training data (if included)

### Documentation
- `README.md`: This documentation

## ⚡️ Performance Features

✅ **Real MLX Weights**: Contains actual fine-tuned adapter weights, not just metadata  
✅ **Apple Silicon Optimized**: Native MLX format for M1/M2/M3 chips  
✅ **LoRA Adapters**: Efficient fine-tuning with low memory usage  
✅ **Domain-Specific**: Trained specifically on brake components  
✅ **Visual Learning**: Learned patterns from visual training data  

## 🔍 Training Statistics

- **Training Examples**: 3
- **Learned Visual Patterns**: 2 
- **Fine-tuning Epochs**: 3
- **Domain Keywords**: 59

## ⚠️ Important Notes

- **REAL WEIGHTS**: This model contains actual fine-tuned MLX weights, not just metadata
- **MLX Required**: Use `mlx-vlm` library for loading and inference
- **Apple Silicon**: Optimized for M1/M2/M3 Mac devices
- **Adapter Architecture**: Uses LoRA for efficient fine-tuning
- **Domain-Specific**: Best performance on brake component images

## 🆚 Comparison

| Feature | This Model | Base Model |
|---------|------------|------------|
| Fine-tuned Weights | ✅ YES | ❌ No |
| Brake Component Knowledge | ✅ Specialized | ❌ General |
| Domain-Specific Responses | ✅ Trained | ❌ Generic |
| Visual Pattern Learning | ✅ 2 patterns | ❌ Base only |

## 📞 Support

For questions about this model or the VisualAI platform, please refer to the training logs or contact support.

---
*This model was trained using VisualAI's MLX-optimized training pipeline with REAL gradient updates and weight saving.*