Using Llama Guard 3 8B on Fireworks AI

Implementation guide · Llama Guard · AI at Meta

ServerlessOpen Weights

Fireworks AI exposes Llama Guard 3 8B through model ID accounts/fireworks/models/llama-guard-3-8b. Use the setup steps, sourced pricing, capabilities, and official provider links below to validate this route before deployment.

Last refreshed 2026-07-09. Next refresh: weekly.

Quick Start

  1. 1
    Create an account at Fireworks AI and generate an API key.
  2. 2
    Use the Fireworks AI SDK or REST API to call accounts/fireworks/models/llama-guard-3-8b — see the documentation for request format.
  3. 3
    You'll be billed $0.20/1M input, $0.20/1M output tokens. See full pricing.

Code Examples

Install
pip install openai
API key
FIREWORKS_API_KEY
Model ID
accounts/fireworks/models/llama-guard-3-8b

Fireworks 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/llama-guard-3-8b",
    messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)

Pricing on Fireworks AI

TypePrice (per 1M)
Input tokens$0.20
Output tokens$0.20

Capabilities

Structured Outputs

About Llama Guard 3 8B

Llama Guard 3 8B is a specialized large language model developed by Meta for content safety classification. Fine-tuned from Llama 3.1, this 8-billion parameter model excels in moderation tasks, classifying both inputs and outputs across 14 hazard categories based on the MLCommons taxonomy. It supports multiple languages, including English, French, German, Hindi, Italian, Portuguese, Spanish, and Thai. Designed for AI engineers focusing on safe and responsible AI systems, Llama Guard 3 offers improved accuracy and reduced false positive rates in identifying unsafe content, making it a valuable tool for developing robust content moderation systems in conversational AI applications .

Model Specs

Released2024-07-23
Parameters8B
Context8k
ArchitectureDecoder Only
Knowledge cutoff2023-12

Provider

Fireworks AI

San Mateo, California, United States