Docubrief Qwen2.5 1.5b

Training

• Base model: Qwen/Qwen2.5-1.5B-Instruct
• Method: QLoRA (4-bit NF4), r=16, alpha=32
• Steps: 200, LR: 2e-4, max seq len: 1024
• Dataset: CNN/DailyMail 3.0.0, articles truncated to <= 500 words

Limitations

• English only. Small model - pair with the map-reduce/RAG layer described in the DocuBrief AI project guide for accurate summarization of large or arbitrary documents.
• Trained on news-style text; may need a larger base model or more training for highly technical or domain-specific documents (legal, medical, scientific).

--- license: apache-2.0 base_model: Qwen/Qwen2.5-1.5B-Instruct tags: - summarization - lora - qlora - rag datasets: - abisee/cnn_dailymail language: en --- # DocuBrief — Document Summarization Assistant (fine-tuned Qwen/Qwen2.5-1.5B-Instruct) Fine-tuned with QLoRA on 4000 examples derived from [CNN/DailyMail](https://huggingface.co/datasets/abisee/cnn_dailymail). ## Intended use Designed to be paired with a RAG/chunking layer that supplies the actual document content at inference time (see the DocuBrief AI project guide). This model provides the *summarization behavior* - clear, well-structured, faithful summaries - not memorized knowledge of any particular document. ## Training - Base model: `Qwen/Qwen2.5-1.5B-Instruct` - Method: QLoRA (4-bit NF4), r=16, alpha=32 - Steps: 200, LR: 2e-4, max seq len: 1024 - Dataset: CNN/DailyMail 3.0.0, articles truncated to <= 500 words ## Limitations - English only. Small model - pair with the map-reduce/RAG layer described in the DocuBrief AI project guide for accurate summarization of large or arbitrary documents. - Trained on news-style text; may need a larger base model or more training for highly technical or domain-specific documents (legal, medical, scientific).