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  # Model Card: Minimalist Assistant
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  ## Model Details
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- - **Architecture**: 32k tokens, 32 layers
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- - **Quantization**: 4-bit
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- - **Base Model**: Mistral Instruct
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- - **Tokenizer**: Custom (based on Mistral Instruct)
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  ## Intended Use
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  - As Editor Assistant for revision and paraphrasing
 
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  ## Training Data
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- - **Initial Training**: 14,000 conversations in minimalist style to ensure concise output
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- - **Further Training**: 8,000 revision conversations to enhance rewriting and paraphrasing capabilities
 
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  ## Performance and Limitations
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- - **Strengths**:
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- - Optimized for generating concise content
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- - Specialized in rewriting and paraphrasing tasks
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  - **Limitations**:
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- - May produce shorter outputs compared to standard models
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- - Potential biases from training data should be considered
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  ## Ethical Considerations
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  - Designed for daily use, potential biases from training data should be considered
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- - Users should be aware of the model's focus on brevity and rewriting
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  ## Additional Information
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  - Fine-tuned to address limitations in writing tasks observed in other models
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  - Personalized for everyday use cases
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  - Motivation for development was to create a model better suited for writing tasks, as existing models were found lacking in this area
 
 
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  # Model Card: Minimalist Assistant
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  ## Model Details
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+ - **Base Model**: Mistral Instruct v2
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+ - **Tokenizer**: based on Mistral Instruction following
 
 
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  ## Intended Use
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  - As Editor Assistant for revision and paraphrasing
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+ - Avoids technical jargon in favor of clear and accessible language
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  ## Training Data
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+ - **Initial Training**: 14,000 conversations in minimalist style and more accessible language
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+ - Dataset: kevin009/system-defined-sft-llama3-14k
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+ - **Further Training**: 8,000 revision conversations to enhance rewriting and paraphrasing tasks.
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  ## Performance and Limitations
 
 
 
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  - **Limitations**:
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+ - May produce shorter outputs compared to original version.
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+ - Potential biases
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  ## Ethical Considerations
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  - Designed for daily use, potential biases from training data should be considered
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+ - The model does not have implemented safety measures to prevent generation of potentially harmful or offensive content
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  ## Additional Information
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  - Fine-tuned to address limitations in writing tasks observed in other models
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  - Personalized for everyday use cases
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  - Motivation for development was to create a model better suited for writing tasks, as existing models were found lacking in this area
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+ - SFT fine-tuned model