Feature Extraction
Transformers
Safetensors
English
qwen3
text-generation
text-embeddings-inference
8-bit precision
bitsandbytes
Instructions to use ManiKumarAdapala/Qwen3-Embedding-0.6B-Q8_0-Safetensors with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ManiKumarAdapala/Qwen3-Embedding-0.6B-Q8_0-Safetensors with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ManiKumarAdapala/Qwen3-Embedding-0.6B-Q8_0-Safetensors")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ManiKumarAdapala/Qwen3-Embedding-0.6B-Q8_0-Safetensors") model = AutoModelForCausalLM.from_pretrained("ManiKumarAdapala/Qwen3-Embedding-0.6B-Q8_0-Safetensors", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b7da5a7f8531528139d534f2736dc943544ee53deff702862b236f78dc0f198a
- Size of remote file:
- 753 MB
- SHA256:
- b18024ad1257e7eef6a441f4bcbacaa0a51f2e8d4748b022d38ebfa1a06304d3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.