初始化项目，由ModelHub XC社区提供模型
Model: Fralet/DDeduPModelv7 Source: Original Platform
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README.md
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---
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base_model: unsloth/llama-3-8b-bnb-4bit
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- llama
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- educational
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- DDeduP
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license: apache-2.0
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language:
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- en
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- ru
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- kk
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datasets:
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- Fralet/DDeduPDatasetv2
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---
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## Overview
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**DDeduPModelv7** is a fine-tuned version of the Meta-Llama-3-8B model, specifically optimized for educational tasks for Kazakhstan and the world. This model excels in educational text generation, multi-label classification and mapping courses to relevant learning outcome codes across various academic disciplines, such as environmental science, pedagogy, pharmacology, ecology, IT, psychology, geodesy, art, linguistics, agriculture, geology, land management, and mathematics.
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## Background & Motivation
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The development of DDeduPModelv7 stems from the need to automate and streamline curriculum alignment in higher education. Traditional methods of mapping courses to learning outcomes are time-intensive and prone to inconsistencies, especially across multidisciplinary programs. By leveraging a large dataset of pre-mapped educational examples, this model addresses challenges in fields like environmental science and pedagogy, where ensuring that course content contributes to broader program competencies is crucial for accreditation, assessment, and quality assurance. The motivation is to provide educators, curriculum developers, and institutions with an AI-assisted tool that promotes accurate, efficient alignment, ultimately enhancing educational planning and student outcome tracking in diverse academic contexts.
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## Training & Methodology
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- **Base Model:** [unsloth/llama-3-8b-bnb-4bit](https://huggingface.co/unsloth/llama-3-8b-bnb-4bit)
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- **Fine-Tuning Framework:** The model was fine-tuned using Huggingface's TRL library in conjunction with [Unsloth](https://github.com/unslothai/unsloth), resulting in a training process that is approximately 2x faster.
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- **Dataset & Techniques:** Trained on Fralet/DDeduPDatasetv2, a comprehensive collection of 137,226 examples with Kazakh, Russian and English languages.
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## Intended Use Cases
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- **Curriculum Alignment:** Automatically map university courses to program learning outcomes, aiding in syllabus development and accreditation processes.
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- **Tool Integration:** Embed in software for educators to quickly assess program gaps.
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- **Research Support:** Assist researchers in studying interdisciplinary education by providing structured data on course-outcome relationships.
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- **Job Market Alignment:** Serve as a foundational tool in educational settings for aligning job market and educational program development.
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## Ethical Considerations & Limitations
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This model should be used to support, not replace, human expertise in education. Ensure outputs are reviewed for institutional compliance, as automated mappings may overlook nuanced contextual factors. Promote fair access to educational tools, and consider biases in the dataset's focus on specific disciplines or regions (e.g., Kazakhstani contexts).
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It is tailored for English, Russian and Kazakh, and educational tasks; adaptation for other languages or domains may require further fine-tuning. Potential biases from dataset sources (e.g., over-representation of certain fields) could affect fairness in diverse applications.
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config.json
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"LlamaForCausalLM"
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23
special_tokens_map.json
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23
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Normal file
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3
tokenizer.json
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3
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|
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2064
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2064
tokenizer_config.json
Normal file
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