vadakayil-llm-tiny

Vadakayil LLM v3 (Medium)

A medium-sized character-level LLM trained on Capt Ajit Vadakayil's writings about Mach 0.3, consciousness, and Vedic philosophy.

Model Details

Training Configuration

Performance

Usage

With this codebase:

from model import TinyLLM
from tokenizer import Tokenizer
import torch

# Load tokenizer
tokenizer = Tokenizer.load("tokenizer.json")

# Load model
checkpoint = torch.load("model.pt", map_location="cpu")
model = TinyLLM(
    vocab_size=74,
    d_model=512,
    num_heads=8,
    num_layers=6,
    d_ff=1024,
    max_seq_len=512,
    dropout=0.1,
    pad_token_id=0
)
model.load_state_dict(checkpoint["model_state_dict"])
model.eval()

# Generate text
prompt = "What is Mach 0.3"
input_ids = tokenizer.encode(prompt, add_special_tokens=False)
input_ids = torch.tensor([input_ids], dtype=torch.long)
output = model.generate(input_ids, max_new_tokens=150, temperature=0.8)
print(tokenizer.decode(output[0].tolist()))

Example Prompts:

• "What is Mach 0.3 and why is it significant?"
• "Why is Mach 0.3 called the Paradox Rekha?"
• "What is the Silent Kalki Revolution of Consciousness?"
• "What does the movie Thondi Muthalum Driksakshiyum represent?"

Training Data Topics

• Mach 0.3 and fluid dynamics
• Compressible vs incompressible flow equations
• Prandtl-Glauert correction
• Consciousness and Vedic philosophy
• Silent Kalki Revolution
• Evidence and Witness (Thondi Muthalum Driksakshiyum film analysis)
• Paradox Rekha concept
• Capt Ajit Vadakayil's teachings

Limitations

• Character-level tokenizer (74 characters)
• Trained on limited dataset (~57K characters)
• May produce inaccurate output for technical queries
• Best for philosophical/consciousness topics

License

MIT License

Credits

Training data: Capt Ajit Vadakayil's writings Model: Custom transformer implementation