Instructions to use adventists-ai/Qwen3-ASR-0.6B-Encoder-XAttn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use adventists-ai/Qwen3-ASR-0.6B-Encoder-XAttn with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="adventists-ai/Qwen3-ASR-0.6B-Encoder-XAttn", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("adventists-ai/Qwen3-ASR-0.6B-Encoder-XAttn", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Qwen3-ASR-0.6B-Encoder-XAttn
The audio encoder of Qwen/Qwen3-ASR-0.6B, extracted as a standalone
transformers model (trust_remote_code=True), 18 layers, d_model 896. Weights are unchanged from Qwen3-ASR-0.6B.
This variant adds the code for DuplexJev's cross-attention fusion block (Q = layer 18, K = layer 14, V = layer 9). The encoder weights are identical to the plain variant; the trained fusion weights ship with each DuplexJev-A connector.
Used by: DuplexJev-A-* (cross-attention connectors). You normally do not load it yourself: Decider.from_pretrained("adventists-ai/DuplexJev-...") fetches it.
License: Apache-2.0, as the original Qwen3-ASR-0.6B. See https://github.com/adventists-ai/duplexjev.
- Downloads last month
- 13
Model tree for adventists-ai/Qwen3-ASR-0.6B-Encoder-XAttn
Base model
Qwen/Qwen3-ASR-0.6B