Default Title

Llama-Guard is a 7B parameter Llama 2-based input-output safeguard model. It can be used for classifying content in both LLM inputs (prompt classification) and in LLM responses (response classification). It acts as an LLM: it generates text in its output that indicates whether a given prompt or response is safe/unsafe, and if unsafe based on a policy, it also lists the violating subcategories.

import requests
import json

url = "https://api.cyfuture.ai/aiapi/inferencing/response"
payload = {
  "model": "Model Name",
  "max_tokens": 16384,
  "top_p": 1,
  "top_k": 40,
  "presence_penalty": 0,
  "frequency_penalty": 0,
  "temperature": 0.6,
  "messages": [
    {
      "role": "user",
      "content": "Hello, how are you?"
    }
  ]
}
headers = {
  "Accept": "application/json",
  "Content-Type": "application/json",
  "Authorization": "Bearer <API_KEY>"
}
requests.request("POST", url, headers=headers, data=json.dumps(payload))

await fetch("https://api.cyfuture.ai/aiapi/inferencing/response", {
  method: "POST",
  headers: {
    "Accept": "application/json",
    "Content-Type": "application/json",
    "Authorization": "Bearer <API_KEY>"
  },
  body: JSON.stringify({
    model: ""Model Name"",
    max_tokens: 16384,
    top_p: 1,
    top_k: 40,
    presence_penalty: 0,
    frequency_penalty: 0,
    temperature: 0.6,
    messages: [
        {
            role: "user",
            content: "Hello, how are you?"
        }
    ]
  })
});

URI uri = URI.create("https://api.cyfuture.ai/aiapi/inferencing/response");
HttpClient client = HttpClient.newHttpClient();
HttpRequest request = HttpRequest.newBuilder()
  .uri(uri)
  .header("Accept", "application/json")
  .header("Content-Type", "application/json")
  .header("Authorization", "Bearer <API_KEY>")
  .POST(HttpRequest.BodyPublishers.ofString("""{
    "model": ""Model Name"",
    "max_tokens": 16384,
    "top_p": 1,
    "top_k": 40,
    "presence_penalty": 0,
    "frequency_penalty": 0,
    "temperature": 0.6,
    "messages": [
        {
            "role": "user",
            "content": "Hello, how are you?"
        }
    ]
  }"""))
  .build();
HttpResponse<String> response = client.send(request, HttpResponse.BodyHandlers.ofString());

package main

import (
  "bytes"
  "net/http"
  "fmt"
)

apiUrl := "https://api.cyfuture.ai/aiapi/inferencing/response"
var jsonData = []byte(`{
 "model": "Model Name",
  "max_tokens": 16384,
  "top_p": 1,
  "top_k": 40,
  "presence_penalty": 0,
  "frequency_penalty": 0,
  "temperature": 0.6,
  "messages": [
    {
      "role": "user",
      "content": "Hello, how are you?"
    }
  ]
}`)

req, err := http.NewRequest(POST, apiUrl, bytes.NewBuffer(jsonData))
req.Header.Set("Accept", "application/json")
req.Header.Set("Content-Type", "application/json")
req.Header.Set("Authorization", "Bearer <API_KEY>")

client := &http.Client{}
resp, err := client.Do(req)
if err != nil {
  panic(err)
}
defer resp.Body.Close()

fmt.Println("response Status:", resp.Status)

curl --request POST \
  --url https://api.cyfuture.ai/aiapi/inferencing/response \
  -H 'Accept: application/json' \
  -H 'Content-Type: application/json' \
  -H 'Authorization: Bearer <API_KEY>' \
  --data '{
   "model": "Model Name",
    "max_tokens": 16384,
    "top_p": 1,
    "top_k": 40,
    "presence_penalty": 0,
    "frequency_penalty": 0,
    "temperature": 0.6,
    "messages": [
        {
            "role": "user",
            "content": "Hello, how are you?"
        }
    ]
  }'

On-demand deployments

On-demand deployments allow you to use Llama Guard 7B on dedicated GPUs with Cyfuture AI' high-performance serving stack with high reliability and no rate limits.

See the On-demand deployments guide for details.

Deploy this base model

Model Details

Created by

dev@cyfuture.ai

Created

12/30/2024

Visibility

Public

Kind

Base model

Model size

14B parameters

Meta Llama / Llama Guard 7B

On-demand deployments

Model Details