README.md

---
library_name: sentence-transformers
base_model: Qwen/Qwen3-Embedding-4B
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- lora
- peft
- embedding
- retrieval
- rag
license: apache-2.0
language:
- en
datasets:
- DinoStackAI/narrativeqa-rag
---

# Qwen3-Emb-4b-lora-narrativeqa

LoRA adapter for [Qwen/Qwen3-Embedding-4B](https://huggingface.co/Qwen/Qwen3-Embedding-4B) fine-tuned on the **narrativeqa** RAG retrieval dataset ([DinoStackAI/narrativeqa-rag](https://huggingface.co/datasets/DinoStackAI/narrativeqa-rag)).
- **Best dev metric:** `eval_narrativeqa-dev_cosine_ndcg@10` = 0.8110

## Load with Sentence Transformers

```python
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("DinoStackAI/Qwen3-Emb-4b-lora-narrativeqa")
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-narrativeqa")
```

## 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("narrativeqa", 1, "DinoStackAI/Qwen3-Emb-4b-lora-narrativeqa"),
)
```

## Training details

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