Using Platypus2 70B on Together AI
Implementation guide · Platypus2 · garage-bAInd
Together AI exposes Platypus2 70B through model ID platypus2-70b. Use the setup steps, sourced pricing, capabilities, and official provider links below to validate this route before deployment.
Last refreshed 2026-06-15. Next refresh: weekly.
Quick Start
- 1
- 2Use the Together AI SDK or REST API to call
platypus2-70b— see the documentation for request format. - 3
Code Examples
pip install togetherTOGETHER_API_KEYplatypus2-70bTogether uses "organization/model-name" format, e.g. "meta-llama/Llama-4-Scout-17B-16E-Instruct" or "Qwen/QwQ-32B". See the Together model catalog for the exact ID.
from together import Together
client = Together() # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
model="platypus2-70b",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Pricing on Together AI
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.90 |
| Output tokens | $0.90 |
Capabilities
About Platypus2 70B
Platypus2-70B is an advanced auto-regressive language model leveraging the LLaMA 2 transformer architecture, specifically designed by Cole Hunter and Ariel Lee. Distinguished for its exceptional capabilities in STEM and logic tasks, the model's proficiency is bolstered by its training on the Open-Platypus dataset, optimized using Low-Rank Adaptation (LoRA) and Parameter-Efficient Fine-Tuning (PEFT) techniques. This efficient training method enables performance optimization with fewer computational resources. Notably, Platypus2-70B once attained the top spot on HuggingFace's Open LLM Leaderboard, showcasing its robust performance across various benchmark metrics.