Phi 3 Mini 128K Instruct LLaMAfied is an open-source language model by ThomasComics. Features: 3.8b LLM, VRAM: 7.6GB, Context: 128K, License: mit, Instruction-Based, LLM Explorer Score: 0.12.
Phi 3 Mini 128K Instruct LLaMAfied Parameters and Internals
Model Type Use Cases
Areas: Applications: Memory/compute constrained environments, Latency bound scenarios, Strong reasoning applications (especially code, math, and logic)
Primary Use Cases: Building block for generative AI, Acceleration of research on language and multimodal models
Limitations: Developers should consider common limitations and evaluate for accuracy, safety, and fairness before applying to specific use cases.
Considerations: The model is not designed for all downstream purposes; adherence to applicable laws is recommended.
Additional Notes Phi-3 Mini-4K-Instruct is optimized for GPU, CPU, and Mobile with different configurations, including ONNX models.
Supported Languages en (Primary language for use; model performance is optimized for English.)
Training Details
Data Sources: Publicly available documents, newly created synthetic "textbook-like" data, supervised data
Data Volume: Methodology: Supervised fine-tuning and Direct Preference Optimization
Context Length: Training Time: Hardware Used: Model Architecture: Dense decoder-only Transformer model with alignment to human preferences and safety guidelines.
Responsible Ai Considerations
Fairness: The model's quality of service may vary across different English varieties and non-English languages.
Transparency: Developers should follow transparency best practices and inform end-users they are interacting with an AI system.
Accountability: Developers are responsible for ensuring compliance with relevant laws and regulations; assessments for high-risk scenarios recommended.
Mitigation Strategies: Implement feedback mechanisms and pipelines to ground responses in use-case specific, contextual information.
Input Output
Input Format: Best suited for chat format with structured prompts and questions.
Accepted Modalities: Output Format: Generated text in response to input
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Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation
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