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

💠 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.

Model Details

Component	Value
Architecture	GPT-2 style decoder-only transformer
Parameters	~42M (41.87M)
Layers	8
Hidden Size	512
Attention Heads	8
Context Length	512 tokens
Vocabulary Size	32,000 (BPE)
Positional Encoding	Learned embeddings

Training Configuration

Setting	Value
Training Data	FineWeb-Edu (streamed, deduped, filtered)
Training Mode	Scratch (no pretrain)
Optimizer	AdamW (lr=3e-4, wd=0.1)
LR Schedule	Warmup (2k) + Cosine decay
Batch Size	16 per device, 8 grad. accumulation
Effective Batch	128
Precision	BFloat16 mixed precision
Hardware	NVIDIA L4 (24GB VRAM)
Training Time	~6 hours
Iterations	100,000
Tokens Processed	~6.55B

Performance

Model	Params	Val Loss	Zenyx Advantage
Zenyx-42M	42M	3.08	Baseline
GPT-1	117M	~3.3	+22% better
DistilGPT-2	82M	~3.1	Nearly tied
GPT-2 Small	124M	~2.8	3x more efficient

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

Capabilities

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

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)

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

Fine-Tuning

Designed as a base model for further fine-tuning.

Next Steps:

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

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

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}
}

Model Card Authors

Developer: Arko007
Date: October 2025
Contact: HuggingFace Profile

License

Apache License 2.0
See LICENSE file for details.

Acknowledgments

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

Version History

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

💠 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)

Built with 💠 using PyTorch and L4 GPU

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