Model weights for Resp-Agent - An intelligent respiratory sound analysis and generation system.
Modellquelle
Quellenbeschreibung
Model weights for Resp-Agent - An intelligent respiratory sound analysis and generation system.
📦 GitHub Repository: AustinZhang/Resp-Agent
Quellen
1 QuelleVerifiziert 13. Sept.
Modellartefakte
5 ArtefakteDiagnoser/checkpoints/longformer/best_longformer_loss_0.3374_epoch_2.pth
pth · 951 MB · SHA-256 de24ce91e39b…a134 · Hugging Face
HerunterladenDiagnoser/pretrained_models/BEATs_iter3_plus_AS2M.pt
pt · 345 MB · SHA-256 d43cbfad4d7b…8d34 · Hugging Face
HerunterladenQuellenauszüge
2 Auszüge| Model | Size | Description |
|---|
| Diagnoser/checkpoints/longformer | 952 MB | Fine-tuned Longformer for EHR + audio analysis |
| Diagnoser/pretrained_models | 695 MB | BEATs & Tokenizer pretrained weights |
| Generator/checkpoints/llm | 3.8 GB | Fine-tuned LLM for audio generation |
| Generator/checkpoints/flow | 2.0 GB | CFM flow matching model |
| Generator/pretrained_models | 695 MB | BEATs & Tokenizer pretrained weights |
| audio_descriptions.jsonl | 87 MB×2 | Audio description data |
Note: DeepSeek-R1 model is NOT included here. Please download separately from:
🔗 deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
from huggingface_hub import snapshot_download
# Download all models
snapshot_download(
repo_id="AustinZhang/resp-agent-models",
local_dir="./",
ignore_patterns=["*.md", ".gitattributes"]
)
# Download DeepSeek-R1 separately
snapshot_download(
repo_id="deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
local_dir="./Diagnoser/checkpoints/deepseek-r1"
)
After downloading, your project should look like:
Resp-Agent/
├── Diagnoser/
│ ├── checkpoints/
│ │ ├── deepseek-r1/ # From deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
│ │ └── longformer/ # From this repo
│ └── pretrained_models/ # From this repo
└── Generator/
├── checkpoints/
│ ├── llm/ # From this repo
│ └── flow/ # From this repo
└── pretrained_models/ # From this repo
Resp-Agent: An Agent-Based System for Multimodal Respiratory Sound Generation and Disease Diagnosis (ICLR 2026)
If you find this work useful, please cite our paper:
@inproceedings{zhangresp,
title={Resp-Agent: An Agent-Based System for Multimodal Respiratory Sound Generation and Disease Diagnosis},
author={ZHANG, Pengfei and Xie, Tianxin and Yang, Minghao and Liu, Li},
booktitle={The Fourteenth International Conference on Learning Representations}
}
These model weights are released for academic research purposes only.
Generator/checkpoints/flow/best_ep5_val_loss_0.0638_step164485.pt
pt · 1,96 GB · SHA-256 e496c66ed429…beea · Hugging Face
HerunterladenGenerator/checkpoints/llm/best_model_loss_1.2393_epoch_3.pth
pth · 3,72 GB · SHA-256 f4d6f8f0e0f0…1d1f · Hugging Face
HerunterladenGenerator/pretrained_models/BEATs_iter3_plus_AS2M.pt
pt · 345 MB · SHA-256 d43cbfad4d7b…8d34 · Hugging Face
Herunterladen--- license: mit tags: - respiratory-sound - medical-ai - audio-generation - audio-classification language: - en --- # Resp-Agent Models Model weights for **Resp-Agent** - An intelligent respiratory sound analysis and generation system. 📦 **GitHub Repository**: [AustinZhang/Resp-Agent](https://github.com/zpforlove/Resp-Agent) ## 📁 Contents | Model | Size | Description | |-------|------|-------------| | **Diagnoser/checkpoints/longformer** | 952 MB | Fine-tuned Longformer for EHR + audio analysis | | **Diagnoser/pretrained_models** | 695 MB | BEATs & Tokenizer pretrained weights | | **Generator/checkpoints/llm** | 3.8 GB | Fine-tuned LLM for audio generation | | **Generator/checkpoints/flow** | 2.0 GB | CFM flow matching model | | **Generator/pretrained_models** | 695 MB | BEATs & Tokenizer pretrained weights | | **audio_descriptions.jsonl** | 87 MB×2 | Audio description data | > **Note**: DeepSeek-R1 model is **NOT** included here. Please download separately from: > 🔗 [deepseek-ai/DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) ## 🚀 Quick Download ```python from huggingface_hub import snapshot_download # Download all models snapshot_download( repo_id="AustinZhang/resp-agent-models", local_dir="./", ignore_patterns=["*.md", ".gitattributes"] ) # Download DeepSeek-R1 separately snapshot_download( repo_id="deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", local_dir="./Diagnoser/checkpoints/deepseek-r1" ) ``` ## 📂 Expected Directory Structure After downloading, your project should look like: ``` Resp-Agent/ ├── Diagnoser/ │ ├── checkpoints/ │ │ ├── deepseek-r1/ # From deepseek-ai/DeepSeek-R1-Distill-Qwen-7B │ │ └── longformer/ # From this repo │ └── pretrained_models/ # From this repo └── Generator/ ├── checkpoints/ │ ├── llm/ # From this repo │ └── flow/ # From this repo └── pretrained_models/ # From this repo ``` ## 📝 Paper **[Resp-Agent: An Agent-Based System for Multimodal Respiratory Sound Generation and Disease Diagnosis](https://openreview.net/forum?id=ZkoojtEm3W&referrer=%5BAuthor%20Console%5D(%2Fgroup%3Fid%3DICLR.cc%2F2026%2FConference%2FAuthors%23your-submissions))** (ICLR 2026) If you find this work useful, please cite our paper: ```bibtex @inproceedings{zhangresp, title={Resp-Agent: An Agent-Based System for Multimodal Respiratory Sound Generation and Disease Diagnosis}, a...
Source context: 0 downloads · 0 likes · Pipeline audio-classification · Repo AustinZhang/resp-agent-models