Instructions to use AGI-Eval/Auto-ATT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AGI-Eval/Auto-ATT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="AGI-Eval/Auto-ATT")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("AGI-Eval/Auto-ATT") model = AutoModelForMultimodalLM.from_pretrained("AGI-Eval/Auto-ATT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from AGI-Eval/Auto-ATT: direct link, hf CLI and curl.
- Browser
- Download file 804 Bytes
-
https://huggingface.co/AGI-Eval/Auto-ATT/resolve/main/config.json
- Command line
-
hf download hf://AGI-Eval/Auto-ATT/config.json
-
curl -L -o config.json https://huggingface.co/AGI-Eval/Auto-ATT/resolve/main/config.json
804 Bytes
| { | |
| "_name_or_path": "/mnt/dolphinfs/hdd_pool/docker/user/hadoop-aipnlp/EVA/lishuailin/qwen-audio-sft/Qwen2-Audio-7B-Instruct", | |
| "architectures": [ | |
| "Qwen2AudioForConditionalGeneration" | |
| ], | |
| "audio_config": { | |
| "model_type": "qwen2_audio_encoder" | |
| }, | |
| "audio_token_index": 151646, | |
| "hidden_size": 4096, | |
| "ignore_index": -100, | |
| "keys_to_ignore_at_inference": [ | |
| "past_key_values" | |
| ], | |
| "model_type": "qwen2_audio", | |
| "text_config": { | |
| "bos_token_id": 151643, | |
| "eos_token_id": 151645, | |
| "intermediate_size": 11008, | |
| "max_position_embeddings": 8192, | |
| "model_type": "qwen2", | |
| "rms_norm_eps": 1e-05, | |
| "torch_dtype": "bfloat16", | |
| "use_mrope": false, | |
| "vocab_size": 156032 | |
| }, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.46.3", | |
| "vocab_size": 156032 | |
| } | |