nateraw/nexusraven-v2-13b – Run with an API on Replicate

nateraw / nexusraven-v2-13b

Cold

Public
32 runs
A100 (80GB)

Run with an API

Playground API Examples README Versions

Input

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

npx create-replicate --model=nateraw/nexusraven-v2-13b

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 nateraw/nexusraven-v2-13b using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "nateraw/nexusraven-v2-13b:0318f81c2ec8bd8bf57c5e4626eadfb3d0674ece8f8b714b9429f79c80e7e256",
  {
    input: {
      top_k: 50,
      top_p: 1,
      temperature: 1,
      max_new_tokens: 256
    }
  }
);
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 nateraw/nexusraven-v2-13b using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "nateraw/nexusraven-v2-13b:0318f81c2ec8bd8bf57c5e4626eadfb3d0674ece8f8b714b9429f79c80e7e256",
    input={
        "top_k": 50,
        "top_p": 1,
        "temperature": 1,
        "max_new_tokens": 256
    }
)

# The nateraw/nexusraven-v2-13b 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/nateraw/nexusraven-v2-13b/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 nateraw/nexusraven-v2-13b 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": "0318f81c2ec8bd8bf57c5e4626eadfb3d0674ece8f8b714b9429f79c80e7e256",
    "input": {
      "top_k": 50,
      "top_p": 1,
      "temperature": 1,
      "max_new_tokens": 256
    }
  }' \
  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/nateraw/nexusraven-v2-13b@sha256:0318f81c2ec8bd8bf57c5e4626eadfb3d0674ece8f8b714b9429f79c80e7e256 \
  -i 'top_k=50' \
  -i 'top_p=1' \
  -i 'temperature=1' \
  -i 'max_new_tokens=256'

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/nateraw/nexusraven-v2-13b@sha256:0318f81c2ec8bd8bf57c5e4626eadfb3d0674ece8f8b714b9429f79c80e7e256
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "top_k": 50,
      "top_p": 1,
      "temperature": 1,
      "max_new_tokens": 256
    }
  }' \
  http://localhost:5000/predictions

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

Output

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

Run time and cost

This model costs approximately $0.036 to run on Replicate, or 27 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 A100 (80GB) GPU hardware. Predictions typically complete within 26 seconds. The predict time for this model varies significantly based on the inputs.

Readme

This model doesn't have a readme.