Qwen3-MedEmbed-0.6B-GGUF

Qwen3-MedEmbed-0.6B

This is a sentence-transformers model finetuned from Qwen/Qwen3-Embedding-0.6B on the med_embed-training-triplets-v1 dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: Qwen/Qwen3-Embedding-0.6B <!-- at revision c54f2e6e80b2d7b7de06f51cec4959f6b3e03418 -->
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 1024 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • med_embed-training-triplets-v1
• Language: en
• License: apache-2.0

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'Qwen3Model'})
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': True, 'include_prompt': True})
  (2): Normalize()
)

Evaluation

Metrics

Triplet

• Dataset: triplet-eval
• Evaluated with <code>TripletEvaluator</code>

Information Retrieval

[!NOTE] The dataset was splitted in 90:10 ratio. Eval set had 23269 samples. Extra 23269 negative samples were added for information retrieval.

• Datasets: eval-set-23269kq-46538kd and eval-set-23269kq-23269kd
• Evaluated with <code>InformationRetrievalEvaluator</code>