Instructions to use felixmanojh/DJ-AI-Radio with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use felixmanojh/DJ-AI-Radio with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="felixmanojh/DJ-AI-Radio")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("felixmanojh/DJ-AI-Radio")
model = AutoModelForCausalLM.from_pretrained("felixmanojh/DJ-AI-Radio")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use felixmanojh/DJ-AI-Radio with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "felixmanojh/DJ-AI-Radio"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "felixmanojh/DJ-AI-Radio",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/felixmanojh/DJ-AI-Radio

SGLang

How to use felixmanojh/DJ-AI-Radio with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "felixmanojh/DJ-AI-Radio" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "felixmanojh/DJ-AI-Radio",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "felixmanojh/DJ-AI-Radio" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "felixmanojh/DJ-AI-Radio",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use felixmanojh/DJ-AI-Radio with Docker Model Runner:
```
docker model run hf.co/felixmanojh/DJ-AI-Radio
```

Tamil AI DJ Radio - Merged Model

Format: Merged (base + adapter fused)

Fine-tuned Qwen 2.5-0.5B model for generating energetic Tanglish (Tamil-English mix) radio DJ commentary. This is the merged model with LoRA adapter fused into the base model for easy deployment.

Model Details

Base Model: Qwen/Qwen2.5-0.5B-Instruct
Parameters: 494M (0.5B)
Precision: FP16
Training: Fine-tuned with LoRA on 5,027 DJ commentary examples
Language: Tanglish (Tamil-English code-mixed)
Use Case: Radio DJ commentary generation

Other Formats

This model is available in three formats:

DJ-AI-Radio (this repo) - Merged model for deployment
DJ-AI-Radio-LoRA - LoRA adapter for researchers
DJ-AI-Radio-MLX - MLX format for Mac users

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "felixmanojh/DJ-AI-Radio"

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    trust_remote_code=True,
    torch_dtype=torch.float16
)

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"}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt")

outputs = model.generate(
    **inputs,
    max_new_tokens=150,
    temperature=0.8,
    top_p=0.9,
    do_sample=True
)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Demo

Try the live demo: Tamil AI DJ Radio Space

Training Data

Fine-tuned on 5,027 diverse DJ commentary examples covering:

Party & dance vibes
Chill & relaxation
Workout motivation
Study focus music
Late night romance
Morning energy

License

Apache 2.0

Citation

@misc{tamil-ai-dj-radio,
  author = {Felix Manojh},
  title = {Tamil AI DJ Radio - Tanglish Radio DJ Commentary Generator},
  year = {2026},
  publisher = {HuggingFace},
  url = {https://huggingface.co/felixmanojh/DJ-AI-Radio}
}

Built with ❤️ for the Tamil-speaking community

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Safetensors

Model size

0.5B params

Tensor type

F16

felixmanojh
/

DJ-AI-Radio