Using Zephyr ORPO 141B on DeepInfra
Implementation guide · Zephyr · Hugging Face H4
DeepInfra exposes Zephyr ORPO 141B through model ID zephyr-orpo-141b. 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 DeepInfra SDK or REST API to call
zephyr-orpo-141b— see the documentation for request format. - 3
Code Examples
pip install openaiDEEPINFRA_API_KEYzephyr-orpo-141bDeepInfra uses "organization/model-name" format, e.g. "meta-llama/Meta-Llama-3-8B-Instruct" or "mistralai/Mistral-7B-Instruct-v0.3". See the DeepInfra model catalog for exact IDs.
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["DEEPINFRA_API_KEY"],
base_url="https://api.deepinfra.com/v1/openai"
)
response = client.chat.completions.create(
model="zephyr-orpo-141b",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Pricing on DeepInfra
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.65 |
| Output tokens | $0.65 |
Capabilities
About Zephyr ORPO 141B
The Zephyr ORPO 141B is a cutting-edge large language model by Hugging Face, developed in partnership with Argilla and KAIST. It employs a Mixture of Experts (MoE) architecture, consisting of 141 billion parameters, with 39 billion active during operation. The model is derived from the Mixtral-8x22B framework and fine-tuned using the innovative Odds Ratio Preference Optimization (ORPO) method, which improves computational efficiency by removing the need for a separate supervised fine-tuning phase.