DJ-AI-Radio-LoRA

🎵 Tamil AI DJ Radio - LoRA Adapter

Fine-tuned Qwen 2.5-0.5B for generating energetic Tanglish (Tamil-English) radio DJ commentary

This is a LoRA adapter trained on 5,027 examples of Tanglish DJ commentary across 68 diverse themes. Perfect for researchers, fine-tuning enthusiasts, and Apple Silicon users with MLX.

---

🎯 Model Overview

• Base Model: Qwen/Qwen2.5-0.5B-Instruct (4-bit quantized)
• Adapter Type: LoRA (Low-Rank Adaptation)
• Training Data: 5,027 Tanglish DJ commentary examples
• Best Checkpoint: Iteration 2900 (validation loss: 1.856)
• Adapter Size: 17MB
• Framework: MLX (Apple Silicon optimized)

---

📊 Training Configuration

---

🚀 Quick Start

Installation

pip install mlx mlx-lm

Using with MLX

from mlx_lm import load, generate

# Load base model with LoRA adapter
model, tokenizer = load(
    "Qwen/Qwen2.5-0.5B-Instruct",
    adapter_path="felixmanojh/DJ-AI-Radio-LoRA"
)

# Create DJ commentary
messages = [
    {"role": "system", "content": "You are a Tamil AI radio DJ who speaks energetic Tanglish. Create engaging commentary."},
    {"role": "user", "content": "Hype up a high-energy dance track for weekend party"}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
response = generate(model, tokenizer, prompt=prompt, max_tokens=150, verbose=False)
print(response)

Example Output:

Party mode activate! Friday night ah Saturday night mode activate!
Club vibes high-energy vibes! Dance floor crowded! Everyone jumping!
Party starter! Energy maximum! Music energizing! Dancefloor energy!
Party anthem! Ready for any day!

Using with Transformers + PEFT

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-0.5B-Instruct",
    torch_dtype=torch.float16,
    device_map="auto"
)

# Load LoRA adapter
model = PeftModel.from_pretrained(
    base_model,
    "felixmanojh/DJ-AI-Radio-LoRA"
)

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# Generate
messages = [
    {"role": "system", "content": "You are a Tamil AI radio DJ who speaks energetic Tanglish."},
    {"role": "user", "content": "Introduce a chill-out song"}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=150)
print(tokenizer.decode(outputs[0]))

---

💡 Why Use the Adapter?

• 🪶 Lightweight: Only 17MB vs 276MB for full model
• 🔬 Research-Friendly: Easy to experiment with different base models
• 🚀 MLX Optimized: Blazing fast on Apple Silicon (M1/M2/M3/M4)
• 💰 Storage Efficient: Share multiple variants without duplicating base model
• 🔧 Customizable: Fine-tune further or merge with other adapters

---

📚 Training Data

Data Source

• Generation: Claude API (Anthropic)
• Batches: 68 diverse theme batches
• Total Examples: 5,027 training + 503 validation
• Format: Chat template (system + user prompt → DJ commentary)

Data Coverage

Data Quality

• Diversity: 68 unique thematic batches
• Code-Mixing: Natural Tamil-English switching patterns
• Energy Levels: High-energy party to calm study vibes
• Cultural Context: Tamil cinema, festivals, street culture references

---

📈 Performance Metrics

---

🎓 Intended Use

✅ Recommended

• Radio/podcast DJ commentary generation
• Content creation for Tamil music channels
• AI voice assistant for music apps
• Educational demonstrations of code-mixing
• Fine-tuning research on multilingual models

❌ Not Recommended

• Real-time conversation (use chat models instead)
• Factual information retrieval
• Long-form content generation
• Non-Tamil/English language pairs

---

🔄 Model Variants

---

🌐 Live Demo

Try it live with voice cloning and AI music: Tamil AI DJ Radio Space

---

📝 Citation

@software{tamil_ai_dj_radio_2025,
  author = {Felix Manojh},
  title = {Tamil AI DJ Radio - Tanglish Commentary Generator},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/felixmanojh/DJ-AI-Radio-LoRA},
  note = {LoRA adapter for Qwen 2.5-0.5B with Claude-generated training data}
}

---

📄 License

Apache 2.0 (inherits from Qwen 2.5)

Free for commercial and non-commercial use.

---

🙏 Acknowledgments

• Base Model: Qwen Team (Alibaba Cloud)
• Training Framework: MLX (Apple)
• Training Data: Generated with Claude API (Anthropic)
• Inspiration: Tamil music culture and radio DJ traditions

---

Built with ❤️ for the Tamil-speaking community