Platypus
Open-source LLM fine-tuned on STEM and logic questions — top benchmark scorer with minimal training data.
Overview
Platypus is an open-source large language model fine-tuned from Llama 2 by researchers at Boston University and MIT using only 25,000 carefully curated STEM and logic problems. Released in August 2023, Platypus-30B achieved the highest average score on the HuggingFace Open LLM Leaderboard at the time, demonstrating that careful data curation beats brute-force data scaling.
The training dataset (Open-Platypus) was assembled by filtering and deduplicating STEM problems from multiple open-source datasets, removing any examples that overlapped with benchmark test sets to ensure clean evaluation. This contamination-free approach made the benchmark results more trustworthy than many competing models.
Platypus demonstrated a key principle: a highly curated 25,000-example dataset of domain-specific problems can produce better benchmark performance than a 500,000-example general-purpose dataset. This insight influenced many subsequent fine-tuning projects to focus on quality and domain specificity over raw data volume.
Pricing
Pricing shown for reference only. These figures reflect RECATOOLS research as of 8 May 2026 and may be out of date or incomplete. This is not financial or purchasing advice — always confirm the current price on the provider’s official website before making any decision.
Use cases
ASEAN Perspective
Platypus in Southeast Asia
ASEAN-region availability and pricing notes coming soon. Drop the editorial team a note via /contact/ if you can supply local context (Singapore/Malaysia/Indonesia/Thailand/Vietnam).
Platypus is a research project from Boston University: a family of LLaMA-based models fine-tuned on the curated Open-Platypus dataset using LoRA and PEFT, which briefly topped the Hugging Face Open LLM Leaderboard while using a tiny fraction of the data and compute of rival fine-tunes (a 13B model trained in about 5 hours on one A100). Its lasting value is the dataset and the methodology demonstrating cheap, fast refinement of base models.
It suits ML researchers and practitioners studying efficient fine-tuning, not end users looking for a chatbot or product. Caveats: it is an academic artefact from 2023 built on now-superseded LLaMA bases, with no product, support, SLA or commercial backing, and leaderboard relevance has long since moved on. ASEAN readiness is moot in a product sense, the weights and dataset are openly available globally on GitHub/Hugging Face, but there is no hosted API or commercial offering.
Notable facts
- Platypus achieved #1 on the HuggingFace leaderboard using only 25,000 training examples — 20x fewer than competing models that used 500k+ examples.
- The researchers carefully removed any training examples that appeared in benchmark test sets, making Platypus one of the cleanest-evaluated open models.
- The paper was accepted to NeurIPS 2023, validating the scientific contribution of data curation quality over quantity.
Frequently asked questions
About this listing
This entry was compiled from publicly available data including Platypus's official website, press releases, documentation, and reputable third-party publications. RECATOOLS is not affiliated with Platypus unless explicitly stated.
Third-party AI tools update their pricing, features, availability, and policies frequently. Information here may be outdated by the time you read this — we make reasonable efforts to keep listings current, but cannot guarantee absolute accuracy.
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