G0-nano-base

A 62M-parameter GPT trained completely from scratch on a single 8GB-RAM NVIDIA Jetson device, without cloud infrastructure or multi-GPU setups. Base pretrained checkpoint for raw text completion, not instruction following.

Overview

G0-nano-base is a small decoder-only causal language model trained end-to-end under an 8GB unified-memory constraint. The project focuses on making the full training process — tokenizer, pretraining, fine-tuning infrastructure and export — work on modest hardware.

This is the base checkpoint. It predicts the next token and completes text; it is not a chat model and should not be expected to follow instructions.

Model variants

The instruction-tuned version of the same model is available as G0-nano-instruct.

What this version adds

This checkpoint is the pretrained foundation of the G0 Nano model line. It does not include supervised instruction fine-tuning or a chat format.

Architecture

Llama-style decoder-only Transformer:

Property	Value
Parameters	62.1M, with embeddings shared with the language-model head
Layers	12
Hidden size	640
Attention	Grouped-Query Attention, 10 query heads / 2 key-value heads, head dimension 64
Position encoding	RoPE, θ=10000
Feed-forward network	SwiGLU, hidden dimension 1728
Normalization	RMSNorm
Context length	1024 tokens
Vocabulary	16,384 SentencePiece tokens

Training

Pretraining data: approximately 1.5B tokens of English web and book text
Sources: FineWeb-Edu, BookCorpus, OpenWebText, PG-19 and WikiHow
Objective: causal next-token prediction
Training hardware: a single NVIDIA Jetson with 8GB of unified memory

Usage

Hugging Face Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AZERDSQ/G0-nano-base"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)

inputs = tokenizer("The city of Paris is", return_tensors="pt")
outputs = model.generate(
    **inputs,
    max_new_tokens=50,
    do_sample=True,
    top_k=50,
    temperature=0.8,
)
print(tokenizer.decode(outputs[0]))

trust_remote_code=True is required because this repository uses a custom Transformer implementation.

Ollama

ollama run azerdsq/g0-nano-base "The city of Paris is"

This is a base model: it completes text rather than answering questions.

Limitations

62M parameters impose a hard limit on factual knowledge; expect fluent but frequently incorrect completions on knowledge-intensive prompts.
Maximum context length is 1024 tokens.
English-only training data.
Single-sequence generation only; padded batched inference is not supported by the custom model code.
No instruction tuning and no chat format.

This model should not be used for high-stakes decisions, factual verification, medical advice, legal advice or autonomous actions.

License

Apache 2.0. This release contains model weights and the code required to load them; it does not include the training data or private training infrastructure.

AZERDSQ
/

G0-nano-base