license: mit
datasets:
- shivendrra/consolidated-datasets
language:
- en
metrics:
- perplexity
tags:
- Basemodel
- text-generation
- nlp
- custom_code
- casual-llm
library_name: transformers
TinyWay-1.2.0
TinyWay-1.2.0 is a lightweight GPT-style causal language model (~110M parameters) trained from scratch on a mixed streaming corpus (web text, stories, and code). The model is designed for research, experimentation, and educational purposes, with an emphasis on transparent architecture and reproducible training.
⚡ Trained end-to-end using a custom PyTorch pipeline with mixed precision, gradient accumulation, and streaming datasets.
Model Overview
| Property | Value |
|---|---|
| Model type | Decoder-only Transformer (GPT-style) |
| Parameters | ~109.6M |
| Layers | 10 |
| Hidden size | 768 |
| Attention heads | 12 |
| Context length | 256 tokens |
| Activation | GELU |
| Dropout | 0.1 |
| Precision | fp16 / bf16 |
| Weight tying | Token embedding tied with LM head |
| Position encoding | Learned absolute embeddings |
Training Details
Dataset
The model was trained using streaming data from:
- 🌍 Web text
- 📚 Stories
- 💻 Code
via the HuggingFace dataset:
shivendrra/consolidated-datasets
Streaming was used to avoid large local storage and to allow continuous sampling directly from HuggingFace.
Tokenization
Tokenizer: GPT2TokenizerFast
Vocabulary size: 50,257
Special tokens:
bos_token_id = eos_token_id = pad_token_id = 50256
Training Configuration
| Setting | Value |
|---|---|
| Sequence length | 256 |
| Effective batch size | 64 sequences |
| Optimizer | AdamW |
| Learning rate | 3e-4 (cosine decay + warmup) |
| Betas | (0.9, 0.95) |
| Weight decay | 0.1 |
| Gradient clipping | 1.0 |
| Mixed precision | AMP (fp16 / bf16) |
| Gradient accumulation | Yes |
| Training steps | ~60k |
| Total tokens | ~1B (approx) |
Final training loss ≈ 3.0 Final perplexity ≈ ~20
Usage
Load with Transformers (Custom Code Required)
This repository uses a custom model definition (modeling_tinyway.py).
Make sure it is available in your environment before loading.
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("NNEngine/TinyWay-1.2.0")
tokenizer = AutoTokenizer.from_pretrained("gpt2")
Text Generation Example
import torch
prompt = "Once upon a time"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=200,
temperature=0.8,
top_k=50,
top_p=0.95,
do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Example Generations
The model demonstrates:
- ✅ Coherent sentence structure
- ✅ Narrative flow in stories
- ✅ Reasonable grammar and punctuation
- ⚠️ Occasional repetition and topic drift (expected for this scale)
This is a research-grade small LLM, not instruction-aligned by default.
Limitations
- ❌ Not instruction-tuned
- ❌ Limited reasoning depth compared to large LLMs
- ❌ Context length limited to 256 tokens
- ⚠️ May hallucinate or generate inconsistent facts
- ⚠️ Training data may contain noise from web sources
Use responsibly.
Intended Use
- Research experiments
- Educational purposes
- Model scaling studies
- Training pipeline benchmarking
- Custom fine-tuning experiments
Not recommended for production or safety-critical applications.
Reproducibility
The model was trained using:
- Custom PyTorch training loop
- Streaming datasets via HuggingFace
- Mixed precision training
- Gradient accumulation
- Periodic checkpointing
- Full monitoring (loss, perplexity, gradient norm, attention entropy)
If you’d like the full training code or configs, feel free to reach out.
License
This model follows the license of the underlying datasets and tokenizer. Please ensure compliance before commercial usage.
Acknowledgements
- HuggingFace 🤗
- PyTorch
- GPT-2 tokenizer
- Open research community