Platypus Models by garage-bAInd
Last refreshed 2026-05-19. Next refresh: weekly.
Details
About
The Platypus family is a series of cutting-edge large language models (LLMs) developed by researchers at Boston University, achieving top performance on Hugging Face's Open LLM Leaderboard. These models are characterized by their impactful use of the Open-Platypus dataset, a curated collection focusing on STEM and logic, which enables high performance with minimal fine-tuning. By integrating Low-Rank Adaptation (LoRA) modules, Platypus models effectively combine pre-trained strengths with domain-specific insights. This innovative approach facilitates significant reductions in training time and resource usage, as a 13B Platypus model can be trained in just 5 hours using a single A100 GPU. Furthermore, the research team has addressed data contamination issues during training to ensure reliable outcomes. 123.
Decision facts
- Best fit
- General model comparison
- Capability starting point
- Platypus 30B with 2k context
- Lowest tracked input
- Not tracked
- Closest related family
- Platypus2
Current Variants
Use-when guidance is based on each model's tracked capabilities, context window, release date, and replacement status.
Use when the workload needs 2k context and 30B parameters.
| Model | Use when | Released | Signals | Status |
|---|---|---|---|---|
| Platypus 30B | Use when the workload needs 2k context and 30B parameters. | 2023-07 | 2k context30B parameters | Current |
Release Timeline
1 release groupSpecifications(1 models)
| Model | Released | Context | Parameters |
|---|---|---|---|
| Platypus 30B | 2023-07 | 2k | 30B |
