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eai-distill-0.5b

EssentialAI

EAI-Distill-0.5b is a fine-tuned version of Qwen2.5-0.5B-Instruct designed for document classification across 12 taxonomic categories. This model is optimized for high-throughput classification of web documents and produces structured metadata for large-scale dataset curation. The model classifies documents across the following dimensions: - **📚 Free Decimal Correspondence (FDC)**: Subject matter classification based on the Dewey Decimal System - **🧠 Bloom's Taxonomy**: Cognitive process (Remember/Understand/Apply/Analyze/Evaluate/Create) and knowledge domain (Factual/Conceptual/Procedural/Metacognitive) - **📄 Document Type**: Web page categorization (News, Academic, Reference, Code, Social, etc.) - **🔍 Content Quality**: Extraction artifacts, missing content detection - **🎓 Educational Metadata**: Reasoning depth, technical correctness, educational level - **🤖 Base Model**: Qwen2.5-0.5B-Instruct - **📊 Training Data**: 82B synthetic tokens generated by Qwen2.5-32B-Instruct (teacher model) on 104M Common Crawl documents - **⚙️ Optimizer**: AdamW (β₁=0.9, β₂=0.95, weight_decay=0.1) - **📈 Learning Rate**: 1×10⁻⁴ with linear warmup (2B tokens), cosine decay to 1×10⁻⁵, then linear anneal to 0 - **📦 Batch Size**: 2M tokens - **📏 Sequence Length**: 16,384 tokens - **💻 Hardware**: Trained on AMD MI300x GPUs

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rnj-1

EssentialAI

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Text generation

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rnj-1.5-instruct

EssentialAI

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Text generation

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rnj-1-instruct

EssentialAI

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

Text generation

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Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
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Explore AI models 4

eai-distill-0.5b

rnj-1

rnj-1.5-instruct

rnj-1-instruct

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