lucataco/deepseek-r1-70b | Run with an API on Replicate

lucataco / deepseek-r1-70b

DeepSeek's first generation reasoning models with comparable performance to OpenAI-o1 (Updated 4 months, 4 weeks ago)

Cold

Public
1.9K runs
L40S
GitHub
Weights
License

Iterate in playground

Run with an API

Playground API Examples README Versions

Input

prompt

*string

Shift + Return to add a new line

Input text for the model

max_tokens

integer

(minimum: 1, maximum: 8192)

Maximum number of tokens it is allowed to generate

Default: 512

seed

integer

0 for Random seed

Default: 42

Including top_p and 2 more...

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

npx create-replicate --model=lucataco/deepseek-r1-70b

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 lucataco/deepseek-r1-70b using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lucataco/deepseek-r1-70b:656b5ca19031ecadf6de648f81c7af84cba66eb7a1328fa863d0282a6fa7da7b",
  {
    input: {
      seed: 42,
      top_p: 1,
      prompt: "solve the equation x^2 - 3x + 2 = 0",
      max_tokens: 512,
      temperature: 1,
      repeat_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 lucataco/deepseek-r1-70b using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lucataco/deepseek-r1-70b:656b5ca19031ecadf6de648f81c7af84cba66eb7a1328fa863d0282a6fa7da7b",
    input={
        "seed": 42,
        "top_p": 1,
        "prompt": "solve the equation x^2 - 3x + 2 = 0",
        "max_tokens": 512,
        "temperature": 1,
        "repeat_penalty": 1
    }
)

# The lucataco/deepseek-r1-70b 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/lucataco/deepseek-r1-70b/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 lucataco/deepseek-r1-70b 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": "lucataco/deepseek-r1-70b:656b5ca19031ecadf6de648f81c7af84cba66eb7a1328fa863d0282a6fa7da7b",
    "input": {
      "seed": 42,
      "top_p": 1,
      "prompt": "solve the equation x^2 - 3x + 2 = 0",
      "max_tokens": 512,
      "temperature": 1,
      "repeat_penalty": 1
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

<think> To solve the quadratic equation $ x^2 - 3x + 2 = 0 $, I'll start by recognizing that it fits the standard quadratic form $ ax^2 + bx + c = 0 $. Next, I'll use the factoring method to factorize the quadratic expression. I'll look for two numbers that multiply to give 2 (the constant term) and add up to -3 (the coefficient of the x-term). The numbers -1 and -2 satisfy these conditions. After factoring, the equation becomes $ (x - 1)(x - 2) = 0 $. Setting each factor equal to zero gives the potential solutions for x. Finally, I'll solve each simple equation to find the roots of the quadratic equation. This will provide the complete solution set. </think> **Solution:** We are given the quadratic equation: \[ x^2 - 3x + 2 = 0 \] To solve for $ x $, we can factor the quadratic equation. 1. **Factor the Quadratic:** We look for two numbers that multiply to $ 2 $ (the constant term) and add up to $ -3 $ (the coefficient of the $ x $-term). The numbers $ -1 $ and $ -2 $ satisfy these conditions: \[ (-1) \times (-2) = 2 \] \[ (-1) + (-2) = -3 \] Therefore, the equation can be factored as: \[ (x - 1)(x - 2) = 0 \] 2. **Find the Roots:** Set each factor equal to zero: \[ x - 1 = 0 \quad \text{or} \quad x - 2 = 0 \] \[ x = 1 \quad \text{or} \quad x = 2 \] 3. **Final Answer:** The solutions to the equation $ x^2 - 3x + 2 = 0 $ are: \[ \boxed{x = 1 \quad \text{and} \quad x = 2} \]

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  "created_at": "2025-01-21T23:49:59.512000Z",
  "data_removed": false,
  "error": null,
  "id": "e5a0byyyb1rm80cmhd2vg2t89c",
  "input": {
    "seed": 42,
    "top_p": 1,
    "prompt": "solve the equation x^2 - 3x + 2 = 0",
    "max_tokens": 512,
    "temperature": 1,
    "repeat_penalty": 1
  },
  "logs": "Using seed: 42",
  "metrics": {
    "predict_time": 30.990079895,
    "total_time": 104.749507
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  ],
  "started_at": "2025-01-21T23:51:13.271427Z",
  "status": "succeeded",
  "urls": {
    "stream": "https://stream-b.svc.ric1.c.replicate.net/v1/streams/2acfkygt2vzet62cwfw2uf6k6fgvjk6texbt44q6orquuyt4yf5q",
    "get": "https://api.replicate.com/v1/predictions/e5a0byyyb1rm80cmhd2vg2t89c",
    "cancel": "https://api.replicate.com/v1/predictions/e5a0byyyb1rm80cmhd2vg2t89c/cancel"
  },
  "version": "656b5ca19031ecadf6de648f81c7af84cba66eb7a1328fa863d0282a6fa7da7b"
}

Generated in

31.0 seconds

Tweak it Share Report View full prediction

Run time and cost

This model costs approximately $0.041 to run on Replicate, or 24 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 L40S GPU hardware. Predictions typically complete within 43 seconds. The predict time for this model varies significantly based on the inputs.