pthinc/BCE-Prettybird-Micro-Standard-v0.0.5
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AI & ML interests
Behavior-driven AI architectures, mathematical reasoning models,
controlled decision systems, brain–body AI separation,
safe and auditable AI design, and optimization-oriented language models.
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updated a dataset 1 day ago
pthinc/BCE-Prettybird-Micro-Standard-v0.0.5 published a dataset 1 day ago
pthinc/BCE-Prettybird-Micro-Standard-v0.0.5Organizations
🚀 The Future Standard pthinc/BCE-Prettybird-Micro-Standard-v0.0.5 HERE!
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pthinc/pofuduk_cicikus_v4_5B
#9195 opened 3 days ago
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pthinc/Cicikus_v4_0.3B_Pitircik
#9194 opened 3 days ago
by prometechinc
Cicikus-v4-0.3B-PITIRCIK (Prettybird Cutiee) Edition
#1 opened 3 days ago
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129
BCE-Prettybird-Nano-Science-v0.1 - 500 Science Q&A Dataset for Instruction-Based Learning
We are excited to introduce a comprehensive math-physics-chemistry-biology dataset containing 500 instruction-based question-answer pairs, designed to support research in science reasoning, problem-solving, and AI training. Generated using Python’s math libraries (e.g., math, numpy, sympy), the dataset covers a diverse range of difficulty levels—from basic arithmetic and algebra to advanced calculus, probability, and number theory.
Each entry follows a structured instruction-input-output format, ensuring clarity and usability for fine-tuning language models, benchmarking AI systems, or educational applications. The problems include word problems, symbolic computations, and real-world scenarios, making it ideal for developing models that require logical reasoning and numerical precision.
Whether for LLM fine-tuning, automated tutoring, or math-focused AI research, this dataset provides a balanced mix of complexity and accessibility, helping bridge the gap between theoretical math and practical problem-solving.
We are excited to introduce a comprehensive math-physics-chemistry-biology dataset containing 500 instruction-based question-answer pairs, designed to support research in science reasoning, problem-solving, and AI training. Generated using Python’s math libraries (e.g., math, numpy, sympy), the dataset covers a diverse range of difficulty levels—from basic arithmetic and algebra to advanced calculus, probability, and number theory.
Each entry follows a structured instruction-input-output format, ensuring clarity and usability for fine-tuning language models, benchmarking AI systems, or educational applications. The problems include word problems, symbolic computations, and real-world scenarios, making it ideal for developing models that require logical reasoning and numerical precision.
Whether for LLM fine-tuning, automated tutoring, or math-focused AI research, this dataset provides a balanced mix of complexity and accessibility, helping bridge the gap between theoretical math and practical problem-solving.
updated a
dataset 6 days ago
published a
dataset 6 days ago
posted an update 8 days ago
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145
🚀 New Advanced Math Datasets Released: BCE-Prettybird-Micro-Math & Nano-Math v0.1!
Designed for researchers, educators, and AI developers, these datasets feature challenging, competition-level math problems covering calculus, linear algebra, number theory, combinatorics, and more in a clean instruction-input-output format.
✅ Why these datasets?
✔ Rigorously curated – Problems range from Olympiad-style to university-level difficulty.
✔ Diverse topics – Ideal for fine-tuning LLMs, benchmarking reasoning models, or automated tutoring.
✔ Structured for AI training – Perfect for supervised fine-tuning (SFT) or reinforcement learning (RL).
🔗 Check them out, download, and start building!
💡 Feedback & contributions welcome—let’s push the boundaries of AI math reasoning together!
#MathAI #DatasetRelease #LLM #MachineLearning #HuggingFace
Designed for researchers, educators, and AI developers, these datasets feature challenging, competition-level math problems covering calculus, linear algebra, number theory, combinatorics, and more in a clean instruction-input-output format.
✅ Why these datasets?
✔ Rigorously curated – Problems range from Olympiad-style to university-level difficulty.
✔ Diverse topics – Ideal for fine-tuning LLMs, benchmarking reasoning models, or automated tutoring.
✔ Structured for AI training – Perfect for supervised fine-tuning (SFT) or reinforcement learning (RL).
🔗 Check them out, download, and start building!
💡 Feedback & contributions welcome—let’s push the boundaries of AI math reasoning together!
#MathAI #DatasetRelease #LLM #MachineLearning #HuggingFace
updated a
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published a
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