Feature Extraction
Transformers
Safetensors
qwen3
speculative-decoding
diffusion-drafter
adaflash
custom_code
Instructions to use AdaFlash/Qwen3-8B-AdaFlash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AdaFlash/Qwen3-8B-AdaFlash with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="AdaFlash/Qwen3-8B-AdaFlash", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("AdaFlash/Qwen3-8B-AdaFlash", trust_remote_code=True) model = AutoModel.from_pretrained("AdaFlash/Qwen3-8B-AdaFlash", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c164ea73107f50c4a4f571fb26a858307e61f44dcdf1d22927e2439b6b224fc1
- Size of remote file:
- 1.31 GB
- SHA256:
- ea3e06f19f4a8ac5f0af3316c17ac948a58b77f32f978741c9711e91c13f1b01
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