Using OLMo 7B Twin-2T on Together AI
Implementation guide · OLMo · Allen Institute for Artificial Intelligence (AI2)
Together AI exposes OLMo 7B Twin-2T through model ID olmo-7b-twin-2t. 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
olmo-7b-twin-2t— see the documentation for request format. - 3
Code Examples
pip install togetherTOGETHER_API_KEYolmo-7b-twin-2tTogether 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="olmo-7b-twin-2t",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Pricing on Together AI
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.20 |
| Output tokens | $0.20 |
Capabilities
About OLMo 7B Twin-2T
The OLMo 7B Twin-2T is a robust open-source large language model that implements a decoder-only transformer architecture with enhancements for greater stability and performance. It features non-parametric layer normalization and SwiGLU activation functions, along with Rotary positional embeddings for better sequence handling. The model, comprising 32 layers and 32 attention heads, was trained on approximately 2 trillion tokens and supports a context length of 2048. It is notable for its transparency in AI research, as all training data, code, and evaluations are publicly accessible, promoting collaborative advancements.