Feature Extraction
Transformers
Safetensors
qwen3
text-generation
embeddings
legal-retrieval
procurement
lora-merged
text-embeddings-inference
Instructions to use LorMolf/Qwen-Embedding-ProcCode with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LorMolf/Qwen-Embedding-ProcCode with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="LorMolf/Qwen-Embedding-ProcCode")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("LorMolf/Qwen-Embedding-ProcCode") model = AutoModelForMultimodalLM.from_pretrained("LorMolf/Qwen-Embedding-ProcCode") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 515ea6325c7f0723b7c1344a3fd2f2abd6630970b2d54bf56aee18e43da5f6f9
- Size of remote file:
- 11.4 MB
- SHA256:
- 93623af029cdc69b87f2864d3b2cc2424fdf16684f15e139b5b9d08ec34ced91
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