Prediction

meta/codellama-7b:6880b103613a9cd23950c5fd6c140197e519905bd0dd00e448c4858bdd06090a

Model

meta/codellama-7b:6880b103

c3pw7b3bb74us2tpt4yanp6li4

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

0.5s

Created

over 1 year ago by @replicate

Input

debug
top_k: 250
top_p: 0.95
prompt: # function to compute square root def s
temperature: 0.95
max_new_tokens: 80
min_new_tokens: -1
repetition_penalty: 1.15
repetition_penalty_sustain: 256
token_repetition_penalty_decay: 128

{
  "debug": false,
  "top_k": 250,
  "top_p": 0.95,
  "prompt": "# function to compute square root\ndef s",
  "temperature": 0.95,
  "max_new_tokens": 80,
  "min_new_tokens": -1,
  "repetition_penalty": 1.15,
  "repetition_penalty_sustain": 256,
  "token_repetition_penalty_decay": 128
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run meta/codellama-7b using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "meta/codellama-7b:6880b103613a9cd23950c5fd6c140197e519905bd0dd00e448c4858bdd06090a",
  {
    input: {
      debug: false,
      top_k: 250,
      top_p: 0.95,
      prompt: "# function to compute square root\ndef s",
      temperature: 0.95,
      max_new_tokens: 80,
      min_new_tokens: -1,
      repetition_penalty: 1.15,
      repetition_penalty_sustain: 256,
      token_repetition_penalty_decay: 128
    }
  }
);

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

Import the client:

import replicate

Run meta/codellama-7b using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "meta/codellama-7b:6880b103613a9cd23950c5fd6c140197e519905bd0dd00e448c4858bdd06090a",
    input={
        "debug": False,
        "top_k": 250,
        "top_p": 0.95,
        "prompt": "# function to compute square root\ndef s",
        "temperature": 0.95,
        "max_new_tokens": 80,
        "min_new_tokens": -1,
        "repetition_penalty": 1.15,
        "repetition_penalty_sustain": 256,
        "token_repetition_penalty_decay": 128
    }
)

# The meta/codellama-7b 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/meta/codellama-7b/api#output-schema
    print(item, end="")

To learn more, take a look at the guide on getting started with Python.

Run meta/codellama-7b 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": "meta/codellama-7b:6880b103613a9cd23950c5fd6c140197e519905bd0dd00e448c4858bdd06090a",
    "input": {
      "debug": false,
      "top_k": 250,
      "top_p": 0.95,
      "prompt": "# function to compute square root\\ndef s",
      "temperature": 0.95,
      "max_new_tokens": 80,
      "min_new_tokens": -1,
      "repetition_penalty": 1.15,
      "repetition_penalty_sustain": 256,
      "token_repetition_penalty_decay": 128
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

root(num): return math.sqrt(num) # main() section # Printing the result in new line print("sroot of

Generated in

0.5 seconds

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