GLM-5.2 is live. #1 throughput on OpenRouter, pay-per-token on FriendliAI. Try it today ➜
mclemcrew
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
mclemcrew
Model Tree
Qwen/Qwen2-Audio-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
python
import librosa, torchfrom peft import PeftModelfrom transformers import AutoProcessor, Qwen2AudioForConditionalGenerationbase = "Qwen/Qwen2-Audio-7B-Instruct"processor = AutoProcessor.from_pretrained(base)model = PeftModel.from_pretrained(Qwen2AudioForConditionalGeneration.from_pretrained(base, torch_dtype=torch.float16, device_map={"": 0}),"mclemcrew/Qwen2-Audio-MixAssist-LoRA",)audio, _ = librosa.load("my_mix_segment.wav", sr=16000)conversation = [{"role": "system", "content": "You are an expert audio engineer assisting with music production and mixing. Provide clear, specific advice on audio engineering techniques, mixing adjustments, and production decisions based on the audio samples and the user's questions. Focus on practical, actionable guidance."},{"role": "user", "content": [{"type": "audio", "audio_url": "my_mix_segment.wav"},{"type": "text", "text": "The vocals feel buried in the chorus. What should I do?"},]},]text = processor.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)inputs = processor(text=text, audios=[audio], sampling_rate=16000, return_tensors="pt").to(model.device)out = model.generate(**inputs, do_sample=True, temperature=0.7, top_k=20, top_p=0.5,repetition_penalty=1.1, max_new_tokens=300)print(processor.batch_decode(out[:, inputs["input_ids"].size(1):], skip_special_tokens=True)[0])
Decoding matters: use sampling with repetition_penalty=1.1 (the released
model's own generation config). Greedy decoding without a repetition penalty
produces degenerate loops on any model trained on this data.
Full training code, config, and eval tooling: github.com/mclemcrew/MixAssist/tree/main/training
| Setting | Value | Status |
|---|---|---|
| Base model | Qwen/Qwen2-Audio-7B-Instruct | ground truth |
| Quantization | QLoRA 4-bit nf4 + double quant, fp16 compute | ground truth |
| Epochs | 1 | ground truth (paper: 4 overfit) |
| Data | MixAssist, has_content==True → 241 train / 34 dev / 156 test | ground truth |
| LoRA | r=8, α=32, dropout 0.05, LM q/v projections only (audio tower frozen) | reconstructed default |
| LR / schedule | 2e-4, linear, warmup 0.03 | reconstructed default |
| Batch |
Trained in ~11 minutes on a single NVIDIA GB10; final eval loss 2.81. Audio comes from Zenodo record 15848443.
bibtex
@misc{clemens2025mixassistaudiolanguagedatasetcocreative,title={MixAssist: An Audio-Language Dataset for Co-Creative AI Assistance in Music Mixing},author={Michael Clemens and Ana Marasović},year={2025},eprint={2507.06329},archivePrefix={arXiv},primaryClass={cs.SD},url={https://arxiv.org/abs/2507.06329},}
| 1 × grad accum 8 |
| reconstructed default |