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, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("LorMolf/Qwen-Embedding-ProcCode") model = AutoModelForCausalLM.from_pretrained("LorMolf/Qwen-Embedding-ProcCode") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3594938186920bb94004bd476adc3897510e19415e853c769ed8d891385dd630
- Size of remote file:
- 1.19 GB
- SHA256:
- f52e49cc2821b67f6ab379201264933b301950e87d59c7c86f4bca5f13f7c2e5
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