Using Nous Capybara 34B on Fireworks AI
Implementation guide · Capybara · Nous Research
Fireworks AI exposes Nous Capybara 34B through model ID nous-capybara-34b. Use the setup steps, sourced pricing, capabilities, and official provider links below to validate this route before deployment.
Last refreshed 2026-09-30. Next refresh: weekly.
Quick Start
- 1
- 2Use the Fireworks AI SDK or REST API to call
nous-capybara-34b— see the documentation for request format. - 3
Code Examples
pip install openaiFIREWORKS_API_KEYnous-capybara-34bFireworks model IDs use "accounts/fireworks/models/{model-name}" format, e.g. "accounts/fireworks/models/llama4-scout-instruct-basic" or "accounts/fireworks/models/deepseek-r1".
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["FIREWORKS_API_KEY"],
base_url="https://api.fireworks.ai/inference/v1"
)
response = client.chat.completions.create(
model="nous-capybara-34b",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Pricing on Fireworks AI
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.90 |
| Output tokens | $0.90 |
Capabilities
No model capability flags are currently sourced.
About Nous Capybara 34B
The Nous Capybara 34B, developed by NousResearch, is a cutting-edge large language model built on the Yi-34B architecture. It stands out with its remarkable 200K context length, enabling effective handling of vast input data. Excelling in tasks such as text generation, conversational AI, complex summarization, and information recall, the model is trained on a concise dataset of 20,000 examples, enhanced by the Amplify-Instruct synthesis technique. This transformer-based model provides multiple quantization formats for varied hardware capacities, though it faces challenges like small dataset size and potential accuracy trade-offs.