from sentence_transformers import SentenceTransformer

model = SentenceTransformer("DinoStackAI/Qwen3-Emb-4b-lora-bioasq-resplit")
embeddings = model.encode(["Instruct: ...\nQuery:your query", "document text"])

Or load the base model and adapter explicitly:

python

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("Qwen/Qwen3-Embedding-4B")
model.load_adapter("DinoStackAI/Qwen3-Emb-4b-lora-bioasq-resplit")

Load with vLLM (LoRA)

python

from vllm import LLM
from vllm.lora.request import LoRARequest

llm = LLM(
    model="Qwen/Qwen3-Embedding-4B",
    task="embed",
    enable_lora=True,
    max_lora_rank=16,
)
outputs = llm.embed(
    ["Instruct: ...\nQuery:your query"],
    lora_request=LoRARequest("bioasq-resplit", 1, "DinoStackAI/Qwen3-Emb-4b-lora-bioasq-resplit"),
)

Training details

Base model: Qwen/Qwen3-Embedding-4B
Fine-tuning dataset: DinoStackAI/bioasq-rag-13b-resplit
Method: LoRA (r=16, lora_alpha=32, targets q_proj / v_proj)
Loss: CachedMultipleNegativesRankingLoss
Best checkpoint selection: dev IR NDCG@10