Using Nous Hermes 2 Mixtral 8x7B on Together AI
Implementation guide · Hermes 2 · Nous Research
Serverless
Quick Start
- 1
- 2Use the Together AI SDK or REST API to call
nous-hermes2-mixtral-8x7b— see the documentation for request format. - 3
Code Examples
Install
pip install togetherAPI key
TOGETHER_API_KEYModel ID
nous-hermes2-mixtral-8x7bTogether 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="nous-hermes2-mixtral-8x7b",
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
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.60 |
| Output tokens | $0.60 |
Capabilities
No model capability flags are currently sourced.
About Nous Hermes 2 Mixtral 8x7B
Mixtral MoE variant of Hermes trained on 1M+ GPT-4 entries for content generation and customer service. Available in quantized formats (GGUF, GPTQ, AWQ) for flexible deployment.
Model Specs
Released2023-12-12
Parameters8x7B
Context32K
ArchitectureMixture of Experts
Knowledge cutoff2023-12