Feature Extraction
Transformers
Safetensors
qwen3
text-generation
embeddings
legal-retrieval
procurement
lora-merged
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("LorMolf/Qwen-Embedding-ProcCode")
model = AutoModelForCausalLM.from_pretrained("LorMolf/Qwen-Embedding-ProcCode")Quick Links
LorMolf/Qwen-Embedding-ProcCode
Merged Qwen3-Embedding checkpoint for Italian public-procurement retrieval.
- Base model:
Qwen/Qwen3-Embedding-0.6B - Merge base model:
src_appalti/src_retriever/data/qwen3_embedding/merged/Qwen-Embedding-ProcCode-checkpoint-2000 - Initialization adapter checkpoint:
src_appalti/src_retriever/data/qwen3_embedding/merged/Qwen-Embedding-ProcCode-checkpoint-2000 - Adapter checkpoint:
src_appalti/src_retriever/data/qwen3_embedding/outputs/qwen3-embedding-wiki-juris-2k-wiki-juris-20260625-001424/v0-20260625-004807/checkpoint-2000 - Merge time:
2026-06-25T06:14:38.717382+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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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="LorMolf/Qwen-Embedding-ProcCode")