testhellow

Hellow - 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: 1
• Created: 2025-06-03 04:52:55.384214
• Training Completed: ✅ Yes

📊 Training Data

This model was trained on a combined dataset with visual examples and conversations.

🛠️ Usage

Installation

pip install mlx-vlm

Loading the Model

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

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.

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This model was trained using VisualAI's MLX-optimized training pipeline.