--- license: apache-2.0 library_name: peft pipeline_tag: text-generation language: - en tags: - lora - fine-tuned-model - instruction-tuning - adaptation - parameter-efficient-finetuning metrics: - perplexity - bleu - rouge datasets: - iohadrubin/wikitext-103-raw-v1 base_model: - Qwen/Qwen3-Embedding-0.6B --- # dirlora-v3 `dirlora-v3` is a LoRA-adapted model checkpoint built for concise, instruction-style text embedding with aware of previous text and following text. ## Model details - **Model type:** Causal Language Model (LoRA adapter) - **Base model:** `Qwen/Qwen3-Embedding-0.6B` - **Fine-tuning method:** LoRA / PEFT - **LoRA config:** - Rank (`r`): `16` - Alpha (`alpha`): `{{alpha}}` - Dropout: `{{lora_dropout}}` - **Training data:** subset of `wikitext 103` for PoC - **Framework:** PyTorch + Hugging Face `transformers` + `peft` ## Intended use Use this model for: - text embedding - experimenting direction/sequence aware embedding ## Not intended for - any engineering solution ## Usage ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer("npc0/Qwen3-Embedding-0.6B-OED") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) ``` ## Limitations - This is early stage PoC not converged but made progress comparing to previous versions | 配置 | R@1 | R@5 | R@20 | MRR | med | |---|---|---|---|---|---| | **dirlora-v3 + next** | **0.110** | **0.297** | **0.505** | **0.200** | **20** | | dirlora-v3 + prev(對照) | 0.043 | 0.167 | 0.368 | 0.114 | 41 | | dirlora-v3 無 prompt | 0.058 | 0.210 | 0.418 | 0.137 | 31 | | dirlora-v1(前最佳) | 0.075 | 0.242 | 0.465 | 0.162 | 27 | | base | 0.046 | 0.150 | 0.318 | 0.108 | 53 | ## Citation If you use this model, please cite: ``` @misc{npc0directionalembedderv0.3, title={Directional Embedder v0.3}, author={NPC0}, year={2026}, url={https://huggingface.co/npc0/directional-embedder-v0.3}, } ```