Last refreshed 2026-06-15. Next refresh: weekly.
Why use Nous Hermes Llama 2 7B on Together AI?
Together AI offers Nous Hermes Llama 2 7B with pay-as-you-go pricing at $0.20/1M input tokens. Together AI is a platform for running open-source and proprietary LLMs with fast serverless and dedicated endpoints at competitive inference pricing.
Compare Nous Hermes Llama 2 7B across 2 providers to find the best fit for your use caseSetup recipe
Python + curlpip install togetherexport TOGETHER_API_KEY=...from together import Together
client = Together() # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
model="nous-hermes-llama2-7b",nous-hermes-llama2-7bRequest example
from together import Together
client = Together() # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
model="nous-hermes-llama2-7b",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Gotchas
- 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.
- The examples expect TOGETHER_API_KEY; rename it only if your application config maps the new variable.
Compare Nous Hermes Llama 2 7B Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Together AI | $0.20 | $0.20 |
| Fireworks AI | $0.20 | $0.20 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.20 |
| Output tokens | $0.20 |
Capabilities
About Nous Hermes Llama 2 7B
The Nous Hermes Llama 2 7B is a state-of-the-art large language model built on the efficient Llama 2 transformer architecture. Fine-tuned on over 300,000 instructions, it exhibits several impressive features, such as generating long, detailed responses with a low hallucination rate. Notably, it lacks OpenAI's censorship, enabling more open discussions. The model excels in knowledge retention and task completion through extensive training on synthetic GPT-4 outputs and supports prompts in the versatile Alpaca format. Its benchmark performance varies across tasks like GPT4All and BigBench, and quantized versions are available, providing flexible deployment across various platforms.