andreasjansson / codegen

An open-source model for program synthesis. Competitive with OpenAI Codex.

Cold

Public
1.1K runs
A100 (80GB)
GitHub
Paper
License

Run with an API

Playground API Examples README Versions

Prediction

andreasjansson/codegen:211bde4f

Model

andreasjansson/codegen:211bde4f

6xkvlcbn3fgmxioh5adxuo5iiu

Status

Succeeded

Source

Web

Hardware

–

Total duration

1.9s

Created

almost 3 years ago

Input

seed: -1
top_p: 0.95
context: # Implement a function that computes the square of an integer argument.
max_length: 128
temperature: 0.2
prepend_imports
num_return_sequences: "1"
prepend_context_to_output

{
  "seed": -1,
  "top_p": 0.95,
  "context": "# Implement a function that computes the square of an integer argument.\n",
  "max_length": 128,
  "temperature": 0.2,
  "prepend_imports": true,
  "num_return_sequences": "1",
  "prepend_context_to_output": true
}

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

const output = await replicate.run(
  "andreasjansson/codegen:211bde4f1da227ce567f2db0fe3811d27435f69b3ac1c37237f3272fa1e79820",
  {
    input: {
      seed: -1,
      top_p: 0.95,
      context: "# Implement a function that computes the square of an integer argument.\n",
      max_length: 128,
      temperature: 0.2,
      prepend_imports: true,
      num_return_sequences: "1",
      prepend_context_to_output: true
    }
  }
);
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 andreasjansson/codegen using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "andreasjansson/codegen:211bde4f1da227ce567f2db0fe3811d27435f69b3ac1c37237f3272fa1e79820",
    input={
        "seed": -1,
        "top_p": 0.95,
        "context": "# Implement a function that computes the square of an integer argument.\n",
        "max_length": 128,
        "temperature": 0.2,
        "prepend_imports": True,
        "num_return_sequences": "1",
        "prepend_context_to_output": True
    }
)
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 andreasjansson/codegen 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": "211bde4f1da227ce567f2db0fe3811d27435f69b3ac1c37237f3272fa1e79820",
    "input": {
      "seed": -1,
      "top_p": 0.95,
      "context": "# Implement a function that computes the square of an integer argument.\\n",
      "max_length": 128,
      "temperature": 0.2,
      "prepend_imports": true,
      "num_return_sequences": "1",
      "prepend_context_to_output": true
    }
  }' \
  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/andreasjansson/codegen@sha256:211bde4f1da227ce567f2db0fe3811d27435f69b3ac1c37237f3272fa1e79820 \
  -i 'seed=-1' \
  -i 'top_p=0.95' \
  -i $'context="# Implement a function that computes the square of an integer argument.\\n"' \
  -i 'max_length=128' \
  -i 'temperature=0.2' \
  -i 'prepend_imports=true' \
  -i 'num_return_sequences="1"' \
  -i 'prepend_context_to_output=true'

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/andreasjansson/codegen@sha256:211bde4f1da227ce567f2db0fe3811d27435f69b3ac1c37237f3272fa1e79820
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "seed": -1,
      "top_p": 0.95,
      "context": "# Implement a function that computes the square of an integer argument.\\n",
      "max_length": 128,
      "temperature": 0.2,
      "prepend_imports": true,
      "num_return_sequences": "1",
      "prepend_context_to_output": true
    }
  }' \
  http://localhost:5000/predictions

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

Output

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Generated in

1.7 seconds

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