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README.md

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+---
+library_name: mlx-vlm
+tags:
+- mlx
+- vision-language-model
+- fine-tuned
+- brake-components
+- visual-ai
+base_model: mlx-community/SmolVLM-256M-Instruct-bf16
+---
+
+# DynamicVisualLearning-v2 - MLX Fine-tuned Vision Language Model
+
+This model was fine-tuned using the VisualAI platform with MLX (Apple Silicon optimization).
+
+## 🚀 Model Details
+- **Base Model**: `mlx-community/SmolVLM-256M-Instruct-bf16`
+- **Training Platform**: VisualAI (MLX-optimized)
+- **GPU Type**: MLX (Apple Silicon)
+- **Training Job ID**: 2
+- **Created**: 2025-06-03 03:29:58.843336
+- **Training Completed**: ✅ Yes
+
+## 📊 Training Data
+This model was trained on a combined dataset with visual examples and conversations.
+
+## 🛠️ Usage
+
+### Installation
+```bash
+pip install mlx-vlm
+```
+
+### Loading the Model
+```python
+from mlx_vlm import load
+import json
+import os
+
+# Load the base MLX model
+model, processor = load("mlx-community/SmolVLM-256M-Instruct-bf16")
+
+# Load the fine-tuned artifacts
+model_info_path = "mlx_model_info.json"
+if os.path.exists(model_info_path):
+    with open(model_info_path, 'r') as f:
+        model_info = json.load(f)
+    print(f"✅ Loaded fine-tuned model with {model_info.get('training_examples_count', 0)} training examples")
+
+# Check for adapter weights
+adapters_path = "adapters/adapter_config.json"
+if os.path.exists(adapters_path):
+    with open(adapters_path, 'r') as f:
+        adapter_config = json.load(f)
+    print(f"🎯 Found MLX adapters with {adapter_config.get('training_examples', 0)} training examples")
+```
+
+### Inference
+```python
+from mlx_vlm import generate
+from mlx_vlm.prompt_utils import apply_chat_template
+from mlx_vlm.utils import load_config
+from PIL import Image
+
+# Load your image
+image = Image.open("your_image.jpg")
+
+# Ask a question
+question = "What type of brake component is this?"
+
+# Format the prompt
+config = load_config("mlx-community/SmolVLM-256M-Instruct-bf16")
+formatted_prompt = apply_chat_template(processor, config, question, num_images=1)
+
+# Generate response
+response = generate(model, processor, formatted_prompt, [image], verbose=False, max_tokens=100)
+print(f"Model response: {response}")
+```
+
+## 📁 Model Artifacts
+
+This repository contains:
+- `mlx_model_info.json`: Training metadata and learned mappings
+- `training_images/`: Reference images from training data
+- `adapters/`: MLX LoRA adapter weights and configuration (if available)
+- `README.md`: This documentation
+
+## ⚠️ Important Notes
+
+- This model uses MLX format optimized for Apple Silicon
+- The actual model weights remain in the base model (`mlx-community/SmolVLM-256M-Instruct-bf16`)
+- The fine-tuning artifacts enhance the model's domain-specific knowledge
+- **Check the `adapters/` folder for MLX-specific fine-tuned weights**
+- For best results, use on Apple Silicon devices (M1/M2/M3)
+
+## 🎯 Training Statistics
+
+- Training Examples: 3
+- Learned Mappings: 2
+- Domain Keywords: 79
+
+## 📞 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.*