Bloom 560M Finetuned Fraud is an open-source language model by jslin09. Features: 560m LLM, VRAM: 2.2GB, License: bigscience-bloom-rail-1.0, Fine-Tuned, LLM Explorer Score: 0.06.
Bloom 560M Finetuned Fraud Parameters and Internals
Model Type Use Cases
Areas: Legal research, Drafting legal arguments, Training AI on Chinese-language legal corpus
Applications: Generating legal document drafts in Chinese, Fraud case text augmentation
Primary Use Cases: Automated drafting of criminal fact paragraphs in fraud and theft cases
Limitations: Limited non-generalized application outside of specified legal context, Performance on broader legal text tasks not guaranteed
Considerations: Utilize draft text responsibly in conjunction with human legal expertise. Careful validation required.
Additional Notes Designed to handle legality texts focused on Chinese verbiage and legal systems.
Supported Languages Training Details
Data Sources: JsLin09/Fraud_Case_Verdicts
Data Volume: 74823 judgments (ๅคๆฑบใ่ฃๅฎ)
Methodology: Fine-tuned the BLOOM 560m model using public fraud case verdicts
Model Architecture: BLOOM-based architecture, fine-tuned for specific legal text generation tasks
Responsible Ai Considerations
Fairness: The model is trained on unbiased public legal document datasets.
Transparency: Open model with source and research details provided.
Accountability: Developers provide disclaimer due to legal consequences of any generated text.
Mitigation Strategies: Model is designed and documented with aware usage instructions, but no specific risk mitigation strategies implemented.
Input Output
Input Format: Legal incident summary or case fact text in Mandarin
Accepted Modalities: Output Format: Continued legal case drafts and crime facts
Performance Tips: Ensure the prompts are clear to adhere to legal-jargon context for improved output quality.
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Instruction Following and Task Automation
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Data Analysis and Insight Generation
Text Generation
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