| """Minimal inference with the Mango T30 1.7B LoRA adapter. |
| |
| Loads the pinned Qwen3-1.7B base model and applies the Mango adapter with |
| PEFT. This reproduces the language-model component only; it does not |
| reproduce the Mango T30 orchestration runtime that was officially evaluated. |
| """ |
| import os |
|
|
| from peft import PeftModel |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
|
|
| BASE_MODEL = "Qwen/Qwen3-1.7B" |
| BASE_REVISION = "70d244cc86ccca08cf5af4e1e306ecf908b1ad5e" |
| |
| ADAPTER_MODEL = os.environ.get("MANGO_ADAPTER", "ComradeRt/Mango-T30-1.7B") |
|
|
|
|
| def main() -> None: |
| tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, revision=BASE_REVISION) |
| base = AutoModelForCausalLM.from_pretrained( |
| BASE_MODEL, |
| revision=BASE_REVISION, |
| device_map="auto", |
| dtype="auto", |
| ) |
| model = PeftModel.from_pretrained(base, ADAPTER_MODEL) |
| model.eval() |
|
|
| messages = [ |
| {"role": "user", |
| "content": "Solve x^2 - 5x + 6 = 0 and verify the roots."}, |
| ] |
| text = tokenizer.apply_chat_template( |
| messages, tokenize=False, add_generation_prompt=True) |
| inputs = tokenizer(text, return_tensors="pt").to(model.device) |
| output = model.generate(**inputs, max_new_tokens=512, do_sample=False) |
| new_tokens = output[0][inputs["input_ids"].shape[-1]:] |
| print(tokenizer.decode(new_tokens, skip_special_tokens=True)) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
