tomasmcm/carl-llama-2-13b:87af0568 – Run with an API on Replicate

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tomasmcm /carl-llama-2-13b:87af0568

Playground API

Input

prompt

*string

Shift + Return to add a new line

Text prompt to send to the model.

max_new_tokens

integer

The maximum number of tokens the model should generate as output.

Default: 128

temperature

number

(minimum: 0.01, maximum: 5)

The value used to modulate the next token probabilities.

Default: 0.8

top_p

number

(minimum: 0.01, maximum: 1)

A probability threshold for generating the output. If < 1.0, only keep the top tokens with cumulative probability >= top_p (nucleus filtering). Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751).

Default: 0.95

top_k

integer

The number of highest probability tokens to consider for generating the output. If > 0, only keep the top k tokens with highest probability (top-k filtering).

Default: 50

presence_penalty

number

(minimum: 0.01, maximum: 5)

Presence penalty

Default: 1

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

npx create-replicate --model=tomasmcm/carl-llama-2-13b

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 tomasmcm/carl-llama-2-13b using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "tomasmcm/carl-llama-2-13b:87af0568c33b019eacc9323b08c5e0a5c05d0f7723c383ddfd4bba12a1a06e58",
  {
    input: {
      top_k: 50,
      top_p: 0.95,
      temperature: 0.8,
      max_new_tokens: 128,
      presence_penalty: 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 tomasmcm/carl-llama-2-13b using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "tomasmcm/carl-llama-2-13b:87af0568c33b019eacc9323b08c5e0a5c05d0f7723c383ddfd4bba12a1a06e58",
    input={
        "top_k": 50,
        "top_p": 0.95,
        "temperature": 0.8,
        "max_new_tokens": 128,
        "presence_penalty": 1
    }
)
print(output)

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 tomasmcm/carl-llama-2-13b 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": "tomasmcm/carl-llama-2-13b:87af0568c33b019eacc9323b08c5e0a5c05d0f7723c383ddfd4bba12a1a06e58",
    "input": {
      "top_k": 50,
      "top_p": 0.95,
      "temperature": 0.8,
      "max_new_tokens": 128,
      "presence_penalty": 1
    }
  }' \
  https://api.replicate.com/v1/predictions

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

You can run this model locally using Cog. First, install Cog:

brew install cog

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

Run this to download the model and run it in your local environment:

cog predict r8.im/tomasmcm/carl-llama-2-13b@sha256:87af0568c33b019eacc9323b08c5e0a5c05d0f7723c383ddfd4bba12a1a06e58 \
  -i 'top_k=50' \
  -i 'top_p=0.95' \
  -i 'temperature=0.8' \
  -i 'max_new_tokens=128' \
  -i 'presence_penalty=1'

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

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 --gpus=all r8.im/tomasmcm/carl-llama-2-13b@sha256:87af0568c33b019eacc9323b08c5e0a5c05d0f7723c383ddfd4bba12a1a06e58
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "top_k": 50,
      "top_p": 0.95,
      "temperature": 0.8,
      "max_new_tokens": 128,
      "presence_penalty": 1
    }
  }' \
  http://localhost:5000/predictions

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

Output

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