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IRedDragonICY
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Model Details
Model Provider
IRedDragonICY
Model Tree
google/gemma-4-E2B-it
Input Modalities
Output Modalities
Supported Functionality
The merged adapter is the root variant: training file laguqa_train.jsonl
v1.5, learning rate 4e-4, 2 epochs, LoRA r 16, seed 1 (adapter sha256
c2f661a1…). PeftModel.merge_and_unload() was run on CPU in bfloat16 (torch
2.13.0, transformers 5.16.1, peft 0.20.0). The parameter count is unchanged at
5,104,297,504, and the vision and audio towers of the base model are kept, so
the merged model is a drop-in replacement for google/gemma-4-E2B-it. Details
are 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 as the original adapter predictions on 1,181 (98.4%), the same agreement seen when the adapter itself is rescored on different hardware. Both answer 677 questions correctly (56.4%).
| Base model | This model | |
|---|---|---|
| LaguQA multiple choice (1,200 questions) | 24.1 | 56.4 |
| IndoMMLU (2,000 questions, neutral prompt) | 34.4 | 43.6 |
One seed. Across three seeds the same setting averages 53.3 on LaguQA, with a range of 4.7 points; see the adapter card.
python
from transformers import AutoModelForCausalLM, AutoTokenizer repo = "IRedDragonICY/LaguQA-Gemma4-E2B"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)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))The model answers in Indonesian, so the prompts above are kept in Indonesian.
Apache-2.0, the licence of google/gemma-4-E2B-it. Training data: CC BY-NC
4.0; per-song rights are listed in HAK-CIPTA.md in the dataset repository.