license, language, library_name, pipeline_tag, tags

license	language	library_name	pipeline_tag	tags
cc-by-nc-4.0	en	transformers	text-generation	text-generation gpt2 small-language-model pytorch safetensors

rb-nano

A 48M-parameter, GPT-2-style decoder-only transformer trained from scratch as part of the Leopard AI Model Suite. Small enough to run on CPU or any GPU; built as a learning/research model, not a production assistant.

Run it instantly with Ollama: ollama run rafi-dev/rb-nano Or grab the quantized build from the GGUF repo (rb-nano-GGUF) for llama.cpp.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("leopard-Ai/rb-nano")
tok = AutoTokenizer.from_pretrained("leopard-Ai/rb-nano")

prompt = "<sos>user: hello\nai:"
ids = tok(prompt, return_tensors="pt").input_ids
out = model.generate(
    ids, max_new_tokens=64, do_sample=True,
    temperature=0.7, top_k=40, top_p=0.9, repetition_penalty=1.3,
    eos_token_id=2, pad_token_id=0,
)
print(tok.decode(out[0], skip_special_tokens=True))

It loads as a standard GPT2LMHeadModel — no trust_remote_code required.

Prompt format

Trained on a simple user: / ai: turn format, prefixed with the <sos> token:

<sos>user: hello
ai: Hi there! How can I help you today?
user: what is python?
ai:

Architecture

Type	Decoder-only transformer (GPT-2 family)
Parameters	~48M
Embedding dim (n_embd)	512
Layers	10
Attention heads	8
Context length	1024 tokens
Position embeddings	Learned
Norm / activation	LayerNorm, GELU-tanh (gelu_new)
Head	Weight-tied to token embeddings
Tokenizer	ByteLevel BPE, 32k vocab
Format	safetensors (fp32)

Training

• Pretrain — FineWeb-Edu (sample-10BT), ~50M tokens. Final val loss ≈ 3.44.
• Finetune — Alpaca, Alpaca-cleaned, CodeAlpaca-20k, Dolly-15k, and ShareGPT (full multi-turn threads, loss masked to assistant turns only). Final val loss ≈ 2.67.

Recommended parameters

temperature   0.7
top_k         40
top_p         0.9
repeat_penalty 1.3

Limitations

• Knowledge. At 48M params the model has very limited factual knowledge and will confidently hallucinate (made-up libraries, wrong dates, etc.). It cannot be a reliable source of facts.
• Coherence. Good for short exchanges; longer or more technical answers drift.
• Scope. English-centric, 1024-token context. Best for demos, experimentation, and edge/CPU inference — not production use.

License / attribution

Released under CC BY-NC 4.0 (non-commercial, attribution required). The finetune mixes datasets with non-commercial terms (Alpaca, CodeAlpaca, ShareGPT — OpenAI-derived), so commercial use is not granted. Trained on publicly available datasets (FineWeb-Edu, Alpaca, Dolly, CodeAlpaca, ShareGPT); review each dataset's license before redistributing derived outputs.

Made with care

rb-nano was built by Rafi (12 years old) and Buddi (10 years old) — pretrained and finetuned from scratch on a single RTX 4070 (8 GB VRAM). It's a passion project: proof that a coherent little chat model can be trained end-to-end on consumer hardware.

If you enjoy it and want to support more experiments like this, you can buy us a coffee ☕. Thank you for trying rb-nano — we hope you like it.

rb-super might come

we plan to make rb-super 120 milion parameters