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:
- e1c5ae75d50677cf0cce3ad0441fbf25396122838224b9473c03004e92077538
- Size of remote file:
- 11.4 MB
- SHA256:
- def76fb086971c7867b829c23a26261e38d9d74e02139253b38aeb9df8b4b50a
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