Feature Extraction
sentence-transformers
Safetensors
Transformers
qwen3
text-generation
sentence-similarity
text-embeddings-inference
Instructions to use 1kxia/Qwen3-Embedding-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use 1kxia/Qwen3-Embedding-0.6B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("1kxia/Qwen3-Embedding-0.6B") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use 1kxia/Qwen3-Embedding-0.6B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="1kxia/Qwen3-Embedding-0.6B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("1kxia/Qwen3-Embedding-0.6B") model = AutoModelForCausalLM.from_pretrained("1kxia/Qwen3-Embedding-0.6B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download cpp_output.bin from 1kxia/Qwen3-Embedding-0.6B: direct link, hf CLI and curl.
- Browser
- Download file 262 kB
-
https://huggingface.co/1kxia/Qwen3-Embedding-0.6B/resolve/main/cpp_output.bin
- Command line
-
hf download hf://1kxia/Qwen3-Embedding-0.6B/cpp_output.bin
-
curl -L -o cpp_output.bin https://huggingface.co/1kxia/Qwen3-Embedding-0.6B/resolve/main/cpp_output.bin
262 kB
- Xet hash:
- c036061b44954b6d286e459306e747e64aad3de812baced4d6d06360f6f22132
- Size of remote file:
- 262 kB
- SHA256:
- 6375ea18d99d44c3709b3f3958672403c67b3c276a382bcd1c53d6293855abe1
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