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
LorMolf/Qwen-Embedding-ProcCode
Merged Qwen3-Embedding checkpoint for Italian public-procurement retrieval.
- Base model:
Qwen/Qwen3-Embedding-0.6B - Adapter checkpoint:
src_appalti/src_retriever/data/qwen3_embedding/outputs/qwen3-embedding-0_6b-phased-2k-b6-20260621_003550/v0-20260621-003640/checkpoint-2000 - Merge time:
2026-06-21T15:23:48.650554+00:00 - Training backend: SWIFT
qwen3_embLoRA, InfoNCE - Expected query format:
Instruct: <retrieval instruction>\nQuery: <question> - Document format: raw article/source or wiki-node text without instruction prefix
- Max context used during training/eval: 32768 tokens
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