DGPT

DGPT v1-base

A small, from-scratch base language model. Not an instruction-tuned assistant.

DGPT v1-base is a 13,049,856-parameter decoder-only Transformer trained from scratch (manual forward pass, manual backward pass, manual AdamW — no autograd, no PyTorch/JAX/TensorFlow) on the TinyStories dataset. It generates short, simple, TinyStories-style children's narratives and nothing more.

Do not expect: instruction following, multi-turn conversation, reasoning, factual world knowledge, or ChatGPT-comparable capability of any kind. This model was never trained or tuned for any of those.

Model description

• Model type: decoder-only Transformer, Pre-LN, GELU (tanh approx), tied token embedding / LM head (no output bias), learned positional embeddings.
• Parameters: 13,049,856
• Context length: 256 tokens
• Vocabulary: 6,000 (locked byte-level BPE, bpe_6000.json)
• Framework: none — hand-implemented NumPy/CuPy. Every layer's backward pass was independently verified against finite-difference gradient checks before training.

Architecture

Training data

TinyStories (TinyStoriesV2-GPT4-train.txt): 2,717,495 synthetically generated (GPT-3.5/ GPT-4) short stories using a deliberately small vocabulary (Eldan & Li, 2023), tokenized to 371,525,259 tokens. Licensed by its authors under CDLA-Sharing-1.0 — this model card does not redistribute the dataset itself.

Training procedure

• Optimizer: manually implemented AdamW (lr, betas, weight decay applied only to matrix params — biases/LayerNorm params excluded from decay).
• Stage 2 (validation run): 50k-story subset, 2000 steps, batch size 64, LR 3e-4 with warmup, used to gate correctness before full training.
• Full run: resumed from the Stage 2 checkpoint, continued on the full 371.5M-token corpus at LR 3e-5, to a final checkpoint at step 5000.
• Tokenizer: locked, pre-trained externally, never retrained during model training.

Hardware

• 1x NVIDIA Tesla T4 (Turing, SM75, 16 GB VRAM), Kaggle.
• CuPy 14.0.1 as the GPU numerical execution backend (no autograd usage).
• Measured throughput: ~3,300–3,440 tokens/sec at batch size 64 (directly measured, not extrapolated).

Intended use

• Educational reference for from-scratch Transformer implementation (manual forward/backward/AdamW) at small scale.
• Generating short, TinyStories-style children's narratives from a prompt.
• Portfolio / ML-engineering demonstration.

Out-of-scope use

• Any production or consumer-facing assistant use case.
• Instruction following, chat, question answering, factual retrieval, reasoning tasks, or code generation.
• Anything requiring broad world knowledge — the model's effective knowledge is bounded by TinyStories' simplified vocabulary and narrative style.
• Any use that assumes safety alignment or content filtering — none was performed.

Evaluation

From the training notebook (full-data run, step 5000):

• Train loss ≈ 3.3
• Val loss ≈ 3.3–3.4
• Val perplexity ≈ 27–29

No held-out benchmark suite (e.g. downstream NLP tasks) was run — TinyStories train/val loss and perplexity are the only reported metrics. Treat any numbers as approximate; see the training notebook's step-by-step log for the exact source values.

Known generation issues

• Occasional run-on or abruptly concatenated sentences (short stories sometimes blend into the next without a clean boundary).
• Repetition of simple phrases/character names across generations.
• No factual grounding — names, objects, and events are generated freely and are not to be treated as accurate about anything.
• Context is capped at 256 tokens; longer prompts are truncated from the left before generation.

How to use

from src.generate import load_dgpt, generate_text
from src.tokenizer import BPETokenizer

tok = BPETokenizer("tokenizer/bpe_6000.json")
model, _ = load_dgpt("model.npz")

print(generate_text(model, tok, "Once upon a time", max_new_tokens=150))

Licensing

License is marked unknown above deliberately. See this repository's main README.md → "Licensing" for the full breakdown across code, weights, tokenizer, and the TinyStories dataset — the weights and tokenizer do not have an established license and none is invented here.