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NearAndDear98
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Model Details
Model Provider
NearAndDear98
Model Tree
google/gemma-4-E4B-it
Input Modalities
Output Modalities
Supported Functionality
| Hyperparameter | Value |
|---|---|
| Method | GRPO, 1 epoch, LoRA |
| Reward | accuracy (1.0 if the chosen letter is correct) + format (0.2) |
| Format reward | non-empty <think> and an <answer> tag |
| Optimizer / LR schedule | AdamW, cosine |
| Learning rate / warmup ratio | 5e-6 / 0.03 |
| Rollouts per prompt | 8 |
| Sampling temperature / top-p | 0.8 / 0.9 |
| KL coefficient β | 0.1 |
| Clip ε | 0.2 |
| Per-device batch × grad. accumulation | 8 × 4 |
| GPUs | 8 |
| LoRA rank / α / dropout | 8 / 32 / 0.05 |
| LoRA targets | all linear layers; audio encoder frozen |
| Precision / seed | bf16 / 42 |
python
import torchfrom peft import PeftModelfrom transformers import AutoProcessor, Gemma4ForConditionalGeneration base = "google/gemma-4-E4B-it"processor = AutoProcessor.from_pretrained(base)model = Gemma4ForConditionalGeneration.from_pretrained(base, torch_dtype=torch.bfloat16, device_map="auto")model = PeftModel.from_pretrained(model, "NearAndDear98/Audio-R1-Gemma-4-E4B-it").eval() SYSTEM = ( "Your response MUST contain two parts in this exact order:\n" "1. Your reasoning enclosed within <think> </think> tags.\n" "2. The final answer — a single letter for the correct option — enclosed " "within <answer> </answer> tags.")messages = [ {"role": "system", "content": SYSTEM}, {"role": "user", "content": [ {"type": "text", "text": "Question ...\n(A) ...\n(B) ...\n(C) ...\n(D) ..."}, {"type": "audio", "audio": "example.wav"}, # audio after text, per Gemma 4 docs ]},]inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", enable_thinking=False,).to(model.device)out = model.generate(**inputs, max_new_tokens=1024, do_sample=False)print(processor.decode(out[0, inputs["input_ids"].shape[-1]:], skip_special_tokens=True))The adapter is released under Apache 2.0, the license of the base model.
bibtex
@article{jeong2026audior1, title = {Audio-R1: Audio Reasoning through Reinforcement Learning on Self-Supervised Tasks}, author = {Jeong, Jihoon and Ravanelli, Mirco and Wang, Zhepei and Subakan, Cem}, journal = {arXiv preprint arXiv:2610.34609}, year = {2026}}import torchfrom peft import PeftModelfrom transformers import AutoProcessor, Gemma4ForConditionalGeneration base = "google/gemma-4-E4B-it"processor = AutoProcessor.from_pretrained(base)model = Gemma4ForConditionalGeneration.from_pretrained(base, torch_dtype=torch.bfloat16, device_map="auto")model = PeftModel.from_pretrained(model, "NearAndDear98/Audio-R1-Gemma-4-E4B-it").eval() SYSTEM = ( "Your response MUST contain two parts in this exact order:\n" "1. Your reasoning enclosed within <think> </think> tags.\n" "2. The final answer — a single letter for the correct option — enclosed " "within <answer> </answer> tags.")messages = [ {"role": "system", "content": SYSTEM}, {"role": "user", "content": [ {"type": "text", "text": "Question ...\n(A) ...\n(B) ...\n(C) ...\n(D) ..."}, {"type": "audio", "audio": "example.wav"}, # audio after text, per Gemma 4 docs ]},]inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", enable_thinking=False,).to(model.device)out = model.generate(**inputs, max_new_tokens=1024, do_sample=False)print(processor.decode(out[0, inputs["input_ids"].shape[-1]:], skip_special_tokens=True))@article{jeong2026audior1, title = {Audio-R1: Audio Reasoning through Reinforcement Learning on Self-Supervised Tasks}, author = {Jeong, Jihoon and Ravanelli, Mirco and Wang, Zhepei and Subakan, Cem}, journal = {arXiv preprint arXiv:2610.34609}, year = {2026}}