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IRedDragonICY
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Model Details
Model Provider
IRedDragonICY
Model Tree
openbmb/MiniCPM5-2B
Input Modalities
Output Modalities
Supported Functionality
PeftModel.merge_and_unload() on CPU in bfloat16 (torch 2.13.0, transformers
5.16.1, peft 0.20.0). The parameter count is unchanged at 2,516,756,480. The
sha256 of the merged adapter and the base revision are recorded in
asal-gabungan.json.
Merging in bfloat16 rounds each weight once more, so the merged model is not bit-identical to base plus adapter. On the 1,200 LaguQA multiple-choice questions it picks the same option on 1,187 (98.9%); the rest are near ties. It answers 445 correctly against 443 for base plus adapter, both scored with the template default.
Build prompts with enable_thinking=False, the format the adapter was trained
on (see the adapter card for why this matters).
python
from transformers import AutoModelForCausalLM, AutoTokenizer repo = "IRedDragonICY/LaguQA-MiniCPM5-2B"tok = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo, dtype="bfloat16") pesan = [{"role": "system", "content": "Kamu asisten yang menguasai lagu nasional dan lagu daerah " "Indonesia, termasuk notasi angka dan notasi ABC-nya."}, {"role": "user", "content": "Siapa pencipta lagu Gugur Bunga?"}]teks = tok.apply_chat_template(pesan, tokenize=False, add_generation_prompt=True, enable_thinking=False)keluar = model.generate(**tok(teks, return_tensors="pt").to(model.device), max_new_tokens=192, do_sample=False)print(tok.decode(keluar[0], skip_special_tokens=True))LaguQA multiple choice, one seed: 24.3 before training and 51.0 after with
enable_thinking=False (24.2 and 36.9 with the template default). IndoMMLU
falls from 33.6 to 27.2 with enable_thinking=False, so general Indonesian
ability is weaker than in the base model.
Apache-2.0, the licence of openbmb/MiniCPM5-2B. Training data: CC BY-NC 4.0;
per-song rights are listed in HAK-CIPTA.md in the dataset repository.