Using Zephyr 7B Alpha on Replicate API
Implementation guide · Zephyr · Hugging Face H4
Replicate API exposes Zephyr 7B Alpha through model ID joehoover/zephyr-7b-alpha. 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 Replicate API SDK or REST API to call
joehoover/zephyr-7b-alpha— see the documentation for request format. - 3
Code Examples
pip install replicateREPLICATE_API_TOKENjoehoover/zephyr-7b-alphaReplicate uses "owner/model-name" format (e.g. "meta/meta-llama-3-8b-instruct") for the latest version, or "owner/model-name:version-sha" to pin to a specific version. The REST endpoint splits owner and model-name into the path: /v1/models/{owner}/{model-name}/predictions.
import replicate
# reads REPLICATE_API_TOKEN from env
# joehoover/zephyr-7b-alpha format: "owner/model-name" (latest version) or "owner/model-name:version-hash"
output = replicate.run(
"joehoover/zephyr-7b-alpha",
input={"prompt": "Hello"}
)
# Output is a list or generator depending on the model
print("".join(output))Pricing on Replicate API
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.05 |
| Output tokens | $0.25 |
Capabilities
No model capability flags are currently sourced.
About Zephyr 7B Alpha
The Zephyr 7B Alpha is a 7-billion parameter language model fine-tuned from the Mistral-7B-v0.1 framework. It serves as an AI assistant, primarily optimizing its performance using Direct Preference Optimization. Although it excels in English text generation and conversational tasks, its training with a mix of public and synthetic datasets—like UltraChat and UltraFeedback—brings a higher risk of generating problematic content due to lesser alignment with human safety standards compared to models like ChatGPT. The model's architecture is GPT-like, offering several quantized versions such as GPTQ and GGUF, which trade-off model size for performance, but may affect accuracy.