101 lines
3.5 KiB
Markdown
101 lines
3.5 KiB
Markdown
---
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license: cc-by-nc-4.0
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language:
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- en
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- text-generation
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- gpt2
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- small-language-model
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- pytorch
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- safetensors
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---
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# rb-nano
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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.
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> **Run it instantly with [Ollama](https://ollama.com/rafi-dev/rb-nano):** `ollama run rafi-dev/rb-nano`
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> Or grab the quantized build from the **GGUF** repo (`rb-nano-GGUF`) for `llama.cpp`.
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("leopard-Ai/rb-nano")
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tok = AutoTokenizer.from_pretrained("leopard-Ai/rb-nano")
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prompt = "<sos>user: hello\nai:"
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ids = tok(prompt, return_tensors="pt").input_ids
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out = model.generate(
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ids, max_new_tokens=64, do_sample=True,
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temperature=0.7, top_k=40, top_p=0.9, repetition_penalty=1.3,
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eos_token_id=2, pad_token_id=0,
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)
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print(tok.decode(out[0], skip_special_tokens=True))
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```
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It loads as a standard `GPT2LMHeadModel` — **no `trust_remote_code` required**.
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## Prompt format
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Trained on a simple `user:` / `ai:` turn format, prefixed with the `<sos>` token:
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```
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<sos>user: hello
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ai: Hi there! How can I help you today?
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user: what is python?
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ai:
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```
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## Architecture
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|---|---|
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| Type | Decoder-only transformer (GPT-2 family) |
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| Parameters | ~48M |
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| Embedding dim (`n_embd`) | 512 |
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| Layers | 10 |
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| Attention heads | 8 |
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| Context length | 1024 tokens |
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| Position embeddings | Learned |
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| Norm / activation | LayerNorm, GELU-tanh (`gelu_new`) |
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| Head | Weight-tied to token embeddings |
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| Tokenizer | ByteLevel BPE, 32k vocab |
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| Format | safetensors (fp32) |
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## Training
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- **Pretrain** — FineWeb-Edu (`sample-10BT`), ~50M tokens. Final val loss ≈ 3.44.
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- **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.
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## Recommended parameters
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```
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temperature 0.7
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top_k 40
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top_p 0.9
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repeat_penalty 1.3
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```
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## Limitations
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- **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.
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- **Coherence.** Good for short exchanges; longer or more technical answers drift.
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- **Scope.** English-centric, 1024-token context. Best for demos, experimentation, and edge/CPU inference — not production use.
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## License / attribution
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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.
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## Made with care
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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.
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If you enjoy it and want to support more experiments like this, you can [buy us a coffee ☕](https://ko-fi.com/leopardAi). Thank you for trying rb-nano — we hope you like it.
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## rb-super might come
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we plan to make rb-super 120 milion parameters |