chigozienri/xwin-mlewd-13b-v0-2 | Run with an API on Replicate

Input

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

npx create-replicate --model=chigozienri/xwin-mlewd-13b-v0-2

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 chigozienri/xwin-mlewd-13b-v0-2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "chigozienri/xwin-mlewd-13b-v0-2:ab6e9cd06c1deb7b200aa6c4792ef8c2503870283bfa82e1f9759e6068f409f2",
  {
    input: {
      top_k: 10,
      top_p: 0.95,
      max_tokens: 500,
      temperature: 0.8,
      mirostat_mode: "Disabled",
      repeat_penalty: 1.1,
      mirostat_entropy: 5,
      presence_penalty: 0,
      frequency_penalty: 0,
      mirostat_learning_rate: 0.1
    }
  }
);

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 chigozienri/xwin-mlewd-13b-v0-2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "chigozienri/xwin-mlewd-13b-v0-2:ab6e9cd06c1deb7b200aa6c4792ef8c2503870283bfa82e1f9759e6068f409f2",
    input={
        "top_k": 10,
        "top_p": 0.95,
        "max_tokens": 500,
        "temperature": 0.8,
        "mirostat_mode": "Disabled",
        "repeat_penalty": 1.1,
        "mirostat_entropy": 5,
        "presence_penalty": 0,
        "frequency_penalty": 0,
        "mirostat_learning_rate": 0.1
    }
)

# The chigozienri/xwin-mlewd-13b-v0-2 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/chigozienri/xwin-mlewd-13b-v0-2/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 chigozienri/xwin-mlewd-13b-v0-2 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": "chigozienri/xwin-mlewd-13b-v0-2:ab6e9cd06c1deb7b200aa6c4792ef8c2503870283bfa82e1f9759e6068f409f2",
    "input": {
      "top_k": 10,
      "top_p": 0.95,
      "max_tokens": 500,
      "temperature": 0.8,
      "mirostat_mode": "Disabled",
      "repeat_penalty": 1.1,
      "mirostat_entropy": 5,
      "presence_penalty": 0,
      "frequency_penalty": 0,
      "mirostat_learning_rate": 0.1
    }
  }' \
  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/chigozienri/xwin-mlewd-13b-v0-2@sha256:ab6e9cd06c1deb7b200aa6c4792ef8c2503870283bfa82e1f9759e6068f409f2 \
  -i 'top_k=10' \
  -i 'top_p=0.95' \
  -i 'max_tokens=500' \
  -i 'temperature=0.8' \
  -i 'mirostat_mode="Disabled"' \
  -i 'repeat_penalty=1.1' \
  -i 'mirostat_entropy=5' \
  -i 'presence_penalty=0' \
  -i 'frequency_penalty=0' \
  -i 'mirostat_learning_rate=0.1'

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/chigozienri/xwin-mlewd-13b-v0-2@sha256:ab6e9cd06c1deb7b200aa6c4792ef8c2503870283bfa82e1f9759e6068f409f2
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "top_k": 10,
      "top_p": 0.95,
      "max_tokens": 500,
      "temperature": 0.8,
      "mirostat_mode": "Disabled",
      "repeat_penalty": 1.1,
      "mirostat_entropy": 5,
      "presence_penalty": 0,
      "frequency_penalty": 0,
      "mirostat_learning_rate": 0.1
    }
  }' \
  http://localhost:5000/predictions

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

Output

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

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

https://huggingface.co/Undi95/Xwin-MLewd-13B-V0.2-GGUF