Using Zephyr 7B Beta on Fireworks AI
Implementation guide · Zephyr · Hugging Face H4
Fireworks AI exposes Zephyr 7B Beta through model ID accounts/fireworks/models/zephyr-7b-beta. 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
accounts/fireworks/models/zephyr-7b-beta— see the documentation for request format. - 3
Code Examples
pip install openaiFIREWORKS_API_KEYaccounts/fireworks/models/zephyr-7b-betaFireworks 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="accounts/fireworks/models/zephyr-7b-beta",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Pricing on Fireworks AI
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.20 |
| Output tokens | $0.20 |
Capabilities
No model capability flags are currently sourced.
About Zephyr 7B Beta
Zephyr 7B Beta is a 7-billion parameter large language model, fine-tuned from the Mistral-7B-v0.1 model. It is tailored to serve as an effective virtual assistant, performing well in generating human-like responses. The model's training involved Direct Preference Optimization (DPO) on a combination of publicly available and synthetic datasets, achieving strong performance on benchmarks like MT-Bench and AlpacaEval, especially for conversational tasks. However, its complexity falls short when compared to proprietary models, especially in tasks involving coding and mathematics. A notable limitation is its insufficient alignment to human safety preferences and the absence of in-the-loop filtering to prevent problematic outputs.