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Together AI

Using StripedHyena Nous 7B on Together AI

Implementation guide · Striped Hyena · Together.ai

Serverless

Quick Start

  1. 1
    Create an account at Together AI and generate an API key.
  2. 2
    Use the Together AI SDK or REST API to call stripedhyena-nous-7b — see the documentation for request format.
  3. 3
    You'll be billed $0.20/1M input, $0.20/1M output tokens. See full pricing.

Code Examples

Install
pip install together
API key
TOGETHER_API_KEY
Model ID
stripedhyena-nous-7b

Together 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="stripedhyena-nous-7b",
    messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)

About Together AI

Platform for running open-source and proprietary LLMs

Together AI is a platform for running open-source and proprietary LLMs with fast serverless and dedicated endpoints at competitive inference pricing.

Pricing on Together AI

TypePrice (per 1M)
Input tokens$0.20
Output tokens$0.20

Capabilities

Structured Outputs

About StripedHyena Nous 7B

StripedHyena-Nous-7B (SH-N 7B) is a state-of-the-art large language AI model from Together Computer, developed alongside Nous Research. Diverging from the traditional Transformer-based architecture, SH-N 7B employs a unique design integrating multi-head, grouped-query attention with gated convolutions in structured Hyena blocks. This hybrid architecture enhances its capacity for long-context processing and offers superior training efficiency and decoding speeds. The model is adept in chat applications, capable of engaging in coherent long-form dialogues, answering questions, and performing various language tasks. Despite requiring specific hardware configurations, SH-N 7B presents competitive performance comparable to leading open-source Transformer models. It’s trained on extensive datasets, including RedPajama, optimized for both short and long-context sequences up to 32k tokens.

Model Specs

Released2023-12-08
Parameters7B
Context32K
ArchitectureDecoder Only

Provider

Together AI
Together AI

San Francisco, California, United States