| """Matrix-BIOS-Italo-0.1 — compact (41.5M) Italian text-generation preview. |
| Custom architecture (loads via trust_remote_code) with a word-level vocabulary. |
| NOTE: this is a v0.1 research preview — it demonstrates the on-prem pipeline and |
| footprint, not production fluency. pip install torch transformers huggingface_hub |
| """ |
| import json, torch |
| from huggingface_hub import hf_hub_download |
| from transformers import AutoModelForCausalLM |
|
|
| REPO = "ruslanmv/Matrix-BIOS-Italo-0.1" |
| model = AutoModelForCausalLM.from_pretrained(REPO, trust_remote_code=True).eval() |
|
|
| |
| vocab = json.load(open(hf_hub_download(REPO, "vocab.json"))) |
| inv = {i: w for w, i in vocab.items()} |
| UNK, PAD = vocab.get("<unk>", 0), vocab.get("<pad>", 1) |
| encode = lambda t: [vocab.get(w, UNK) for w in t.lower().split()] |
| decode = lambda ids: " ".join(inv.get(int(i), "<unk>") for i in ids) |
|
|
| def generate(prompt: str, n: int = 12) -> str: |
| ids = torch.tensor([encode(prompt)]) |
| with torch.no_grad(): |
| out = model.generate(ids, max_new_tokens=n, do_sample=False, pad_token_id=PAD) |
| return decode(out[0]) |
|
|
| if __name__ == "__main__": |
| for p in ["la capitale d' italia", "matrix bios e"]: |
| print(f"[{p!r}] -> {generate(p)}") |
|
|
