fofr/prompt-classifier – Run with an API on Replicate

fofr / prompt-classifier

Determines the toxicity of text to image prompts, llama-13b fine-tune. [SAFETY_RANKING] between 0 (safe) and 10 (toxic)

Cold

Public
1.9M runs
L40S

Run with an API

Playground API Examples README Versions

Input

prompt

*string

Shift + Return to add a new line

[PROMPT] a photo of a cat [/PROMPT] [SAFETY_RANKING][PROMPT] a photo of a cat [/PROMPT] [SAFETY_RANKING]

Prompt to send to the model.

max_new_tokens

integer

(minimum: 1)

Maximum number of tokens to generate. A word is generally 2-3 tokens

Default: 128

min_new_tokens

integer

(minimum: -1)

Minimum number of tokens to generate. To disable, set to -1. A word is generally 2-3 tokens.

Default: -1

temperature

number

(minimum: 0.01, maximum: 5)

Adjusts randomness of outputs, greater than 1 is random and 0 is deterministic, 0.75 is a good starting value.

Default: 0.75

top_p

number

(minimum: 0, maximum: 1)

When decoding text, samples from the top p percentage of most likely tokens; lower to ignore less likely tokens

Default: 0.9

top_k

integer

(minimum: 0)

When decoding text, samples from the top k most likely tokens; lower to ignore less likely tokens

Default: 50

stop_sequences

string

Shift + Return to add a new line

A comma-separated list of sequences to stop generation at. For example, '<end>,<stop>' will stop generation at the first instance of 'end' or '<stop>'.

seed

integer

Random seed. Leave blank to randomize the seed

debug

boolean

provide debugging output in logs

Default: false

Run this model in Node.js with one line of code:

npx create-replicate --model=fofr/prompt-classifier

or set up a project from scratch

Install Replicate’s Node.js client library

npm install replicate

Set the REPLICATE_API_TOKEN environment variable

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run fofr/prompt-classifier using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "fofr/prompt-classifier:1ffac777bf2c1f5a4a5073faf389fefe59a8b9326b3ca329e9d576cce658a00f",
  {
    input: {
      debug: false,
      top_k: 50,
      top_p: 0.9,
      prompt: "[PROMPT] a photo of a cat [/PROMPT] [SAFETY_RANKING]",
      temperature: 0.75,
      max_new_tokens: 128,
      min_new_tokens: -1,
      stop_sequences: "[/SAFETY_RANKING]"
    }
  }
);
console.log(output);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library

pip install replicate

Set the REPLICATE_API_TOKEN environment variable

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import the client

import replicate

Run fofr/prompt-classifier using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "fofr/prompt-classifier:1ffac777bf2c1f5a4a5073faf389fefe59a8b9326b3ca329e9d576cce658a00f",
    input={
        "debug": False,
        "top_k": 50,
        "top_p": 0.9,
        "prompt": "[PROMPT] a photo of a cat [/PROMPT] [SAFETY_RANKING]",
        "temperature": 0.75,
        "max_new_tokens": 128,
        "min_new_tokens": -1,
        "stop_sequences": "[/SAFETY_RANKING]"
    }
)

# The fofr/prompt-classifier model can stream output as it's running.
# The predict method returns an iterator, and you can iterate over that output.
for item in output:
    # https://replicate.com/fofr/prompt-classifier/api#output-schema
    print(item, end="")

To learn more, take a look at the guide on getting started with Python.

Set the REPLICATE_API_TOKEN environment variable

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Run fofr/prompt-classifier using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "1ffac777bf2c1f5a4a5073faf389fefe59a8b9326b3ca329e9d576cce658a00f",
    "input": {
      "debug": false,
      "top_k": 50,
      "top_p": 0.9,
      "prompt": "[PROMPT] a photo of a cat [/PROMPT] [SAFETY_RANKING]",
      "temperature": 0.75,
      "max_new_tokens": 128,
      "min_new_tokens": -1,
      "stop_sequences": "[/SAFETY_RANKING]"
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Install Cog

brew install cog

If you don’t have Homebrew, there are other installation options available.

Pull and run fofr/prompt-classifier using Cog (this will download the full model and run it in your local environment):

cog predict r8.im/fofr/prompt-classifier@sha256:1ffac777bf2c1f5a4a5073faf389fefe59a8b9326b3ca329e9d576cce658a00f \
  -i 'debug=false' \
  -i 'top_k=50' \
  -i 'top_p=0.9' \
  -i 'prompt="[PROMPT] a photo of a cat [/PROMPT] [SAFETY_RANKING]"' \
  -i 'temperature=0.75' \
  -i 'max_new_tokens=128' \
  -i 'min_new_tokens=-1' \
  -i 'stop_sequences="[/SAFETY_RANKING]"'

To learn more, take a look at the Cog documentation.

Pull and run fofr/prompt-classifier using Docker (this will download the full model and run it in your local environment):

docker run -d -p 5000:5000 --gpus=all r8.im/fofr/prompt-classifier@sha256:1ffac777bf2c1f5a4a5073faf389fefe59a8b9326b3ca329e9d576cce658a00f
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "debug": false,
      "top_k": 50,
      "top_p": 0.9,
      "prompt": "[PROMPT] a photo of a cat [/PROMPT] [SAFETY_RANKING]",
      "temperature": 0.75,
      "max_new_tokens": 128,
      "min_new_tokens": -1,
      "stop_sequences": "[/SAFETY_RANKING]"
    }
  }' \
  http://localhost:5000/predictions

Output

Generated in

0.4 seconds

Tweak it Report

Run time and cost

This model costs approximately $0.019 to run on Replicate, or 52 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia L40S GPU hardware. Predictions typically complete within 20 seconds. The predict time for this model varies significantly based on the inputs.

Readme

[SAFETY_RANKING] is between 0 and 10.

0 is safe. 10 is very nsfw.

Trained on categories and reasons for ratings, these do not consistently work. The ranking does.