Zenyx-42M

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base_model: null model_creator: Arko007 model_version: v1.0 model_date: 2025-10 model_card_authors:

• Arko007

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💠 Zenyx-42M: Where Calm Meets Power

Zenyx-42M is a 42M parameter GPT-2 style decoder-only transformer trained from scratch on high-quality educational web text.

The name "Zenyx" fuses Zen (calm, focused intelligence) and Onyx (strength, power)—embodying an efficient, capable language model.

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Model Details

Training Configuration

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Performance

• Training Loss: 0.398
• Validation Loss: 3.08
• Tokens Processed: ~6.55B

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Capabilities

• ✅ Text completion
• ✅ Topic consistency
• ✅ Grammar & syntax
• ✅ Technical vocabulary
• ⚠️ Base model (fine-tuning required for instructions)
• ⚠️ 512 token context

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Usage

Install dependencies

pip install torch transformers tokenizers

Quick Start Example

import torch
from model import NanoGPT
from config import NanoGPTConfig
from tokenizers import Tokenizer

# Load model
checkpoint = torch.load('best_model.pt', map_location='cpu', weights_only=False)
config = checkpoint['config']
model = NanoGPT(config)
model.load_state_dict(checkpoint['model'])
model.eval()

# Load tokenizer
tokenizer = Tokenizer.from_file('tokenizer.json')

def generate(prompt, max_tokens=100, temperature=0.6):
    tokens = tokenizer.encode(prompt).ids
    x = torch.tensor(tokens).unsqueeze(0)
    with torch.no_grad():
        output = model.generate(x, max_new_tokens=max_tokens, temperature=temperature, top_k=40)
    return tokenizer.decode(output.tolist())

prompt = "Artificial intelligence is"
output = generate(prompt)
print(output)

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Limitations

• ⚠️ Base model only (no instruction fine-tuning)
• ⚠️ Can repeat phrases (use repetition penalty)
• ⚠️ 512 token context window
• ⚠️ Small parameter count limits knowledge
• ⚠️ Possible factual errors
• ⚠️ Not suitable for reliable code generation

Not recommended for:

• ❌ Production applications (without fine-tuning)
• ❌ Factual QA
• ❌ Instruction following
• ❌ Mission-critical tasks

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Fine-Tuning

Designed as a base model for further fine-tuning.

Next Steps:

1. Instruction fine-tuning (Alpaca/Dolly datasets)
2. Code specialization (GitHub/StackOverflow)
3. Domain adaptation (medical/legal/etc)
4. RLHF (human feedback alignment)

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Ethical Considerations

• Training data may contain bias
• Intended for educational/experimental use
• Not suitable for high-stakes decisions
• Fully open source and documented
• No explicit safety fine-tuning

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Citation

@misc{zenyx_42m_2025,
  author = {Arko007},
  title = {Zenyx-42M: Efficient Language Model Trained From Scratch},
  year = {2025},
  month = {October},
  publisher = {Hugging Face},
  url = {https://huggingface.co/Arko007/Zenyx-42M}
}

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Model Card Authors

• Developer: Arko007
• Date: October 2025
• Contact: HuggingFace Profile

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License

Apache License 2.0
See LICENSE file for details.

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Acknowledgments

• HuggingFace FineWeb-Edu team (dataset)
• GPT-2 (OpenAI, architecture inspiration)
• PyTorch (framework)
• NVIDIA L4 GPU (compute)

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Version History

• v1.0 (Oct 2025): Initial release — 42M params, 100k iterations, val loss 3.08

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💠 Branding

ZENYX
Symbol: 💠 diamond/geometric
Colors: Deep purple (#5B21B6) + Cyan (#06B6D4)
Font: Modern, clean, geometric
Taglines:

• "Where Calm Meets Power" 💪
• "Efficient Intelligence, Powerful Results" ⚡
• "Small Model, Big Impact" 💎
• "Zen Precision, Onyx Strength" 🔷
  Brand Personality:
• Calm & efficient
• Powerful
• Modern
• Balanced (Zen meets power)

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Built with 💠 using PyTorch and L4 GPU