tanzir11/nibba – Run with an API on Replicate

tanzir11 / nibba

Cold

Public
1 run
L40S

Run with an API

Playground API Examples README Versions

Input

prompt

*string

Shift + Return to add a new line

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=tanzir11/nibba

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 tanzir11/nibba using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "tanzir11/nibba:818dde1d4d32541ad7fa0bf8838a5601b128ad2f980272f4d1b94d2dfb1d3349",
  {
    input: {
      debug: false,
      top_k: 50,
      top_p: 0.9,
      temperature: 0.75,
      max_new_tokens: 128,
      min_new_tokens: -1
    }
  }
);
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 tanzir11/nibba using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "tanzir11/nibba:818dde1d4d32541ad7fa0bf8838a5601b128ad2f980272f4d1b94d2dfb1d3349",
    input={
        "debug": False,
        "top_k": 50,
        "top_p": 0.9,
        "temperature": 0.75,
        "max_new_tokens": 128,
        "min_new_tokens": -1
    }
)

# The tanzir11/nibba 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/tanzir11/nibba/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 tanzir11/nibba 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": "818dde1d4d32541ad7fa0bf8838a5601b128ad2f980272f4d1b94d2dfb1d3349",
    "input": {
      "debug": false,
      "top_k": 50,
      "top_p": 0.9,
      "temperature": 0.75,
      "max_new_tokens": 128,
      "min_new_tokens": -1
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

No output yet! Press "Submit" to start a prediction.

Run time and cost

This model runs on Nvidia L40S GPU hardware. We don't yet have enough runs of this model to provide performance information.

Readme

This model doesn't have a readme.