Prediction

Model

nateraw/mistral-7b-openorca:7afe2184

73glludbc6knkvihbaeunzovym

Status

Succeeded

Source

Web

Hardware

A40

Total duration

5.0s

Created

about 2 years ago by nateraw

Webhook

–

Input

prompt: Explain what's so cool about Python
max_new_tokens: 512
temperature: 0.2
top_p: 0.95
top_k: 50
presence_penalty: 0
frequency_penalty: 0
prompt_template: <|im_start|>system You are MistralOrca, a large language model trained by Alignment Lab AI. Write out your reasoning step-by-step to be sure you get the right answers! <|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant

{
  "frequency_penalty": 0,
  "max_new_tokens": 512,
  "presence_penalty": 0,
  "prompt": "Explain what's so cool about Python",
  "prompt_template": "<|im_start|>system\nYou are MistralOrca, a large language model trained by Alignment Lab AI. Write out your reasoning step-by-step to be sure you get the right answers!\n<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n",
  "temperature": 0.2,
  "top_k": 50,
  "top_p": 0.95
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_1W7**********************************

This is your API token. Keep it to yourself.

Import and set up the client:

import Replicate from "replicate";

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

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

const output = await replicate.run(
  "nateraw/mistral-7b-openorca:7afe21847d582f7811327c903433e29334c31fe861a7cf23c62882b181bacb88",
  {
    input: {
      frequency_penalty: 0,
      max_new_tokens: 512,
      presence_penalty: 0,
      prompt: "Explain what's so cool about Python",
      prompt_template: "<|im_start|>system\nYou are MistralOrca, a large language model trained by Alignment Lab AI. Write out your reasoning step-by-step to be sure you get the right answers!\n<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n",
      temperature: 0.2,
      top_k: 50,
      top_p: 0.95
    }
  }
);

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=r8_1W7**********************************

This is your API token. Keep it to yourself.

Import the client:

import replicate

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

output = replicate.run(
    "nateraw/mistral-7b-openorca:7afe21847d582f7811327c903433e29334c31fe861a7cf23c62882b181bacb88",
    input={
        "frequency_penalty": 0,
        "max_new_tokens": 512,
        "presence_penalty": 0,
        "prompt": "Explain what's so cool about Python",
        "prompt_template": "<|im_start|>system\nYou are MistralOrca, a large language model trained by Alignment Lab AI. Write out your reasoning step-by-step to be sure you get the right answers!\n<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n",
        "temperature": 0.2,
        "top_k": 50,
        "top_p": 0.95
    }
)

# The nateraw/mistral-7b-openorca 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/mistral-7b-openorca/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=r8_1W7**********************************

This is your API token. Keep it to yourself.

Run nateraw/mistral-7b-openorca 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": "nateraw/mistral-7b-openorca:7afe21847d582f7811327c903433e29334c31fe861a7cf23c62882b181bacb88",
    "input": {
      "frequency_penalty": 0,
      "max_new_tokens": 512,
      "presence_penalty": 0,
      "prompt": "Explain what\'s so cool about Python",
      "prompt_template": "<|im_start|>system\\nYou are MistralOrca, a large language model trained by Alignment Lab AI. Write out your reasoning step-by-step to be sure you get the right answers!\\n<|im_end|>\\n<|im_start|>user\\n{prompt}<|im_end|>\\n<|im_start|>assistant\\n",
      "temperature": 0.2,
      "top_k": 50,
      "top_p": 0.95
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

To explain what's so cool about Python, let's break it down into several key aspects:

1. Versatility: Python is a versatile programming language that can be used for a wide range of tasks, including web development, data analysis, machine learning, and more. This makes it a popular choice for developers and data scientists alike.

2. Ease of use: Python has a simple and easy-to-read syntax, which makes it beginner-friendly. This means that even if you're new to programming, you can quickly pick up the language and start writing code.

3. Open-source libraries: Python has a vast ecosystem of open-source libraries and frameworks, such as NumPy, Pandas, and TensorFlow, which can be used to accelerate development and make complex tasks easier.

4. Community support: The Python community is large and active, which means there's plenty of help available if you run into any issues or have questions. Additionally, the community contributes to the development and improvement of the language.

5. Portability: Python code can be run on various platforms, including Windows, macOS, and Linux, making it a great choice for cross-platform development.

6. Scalability: Python's code can be easily scaled up or down depending on the requirements of the project, making it suitable for both small and large-scale applications.

7. Interpreted language: Python is an interpreted language, which means that the code is executed directly by the Python interpreter, without the need for compiling. This can save time during development and deployment.

In summary, Python is a cool programming language because it is versatile, easy to learn, has a large community and library support, is portable, and is scalable. These factors make it a popular choice for a wide range of applications and developers.

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  "input": {
    "frequency_penalty": 0,
    "max_new_tokens": 512,
    "presence_penalty": 0,
    "prompt": "Explain what's so cool about Python",
    "prompt_template": "<|im_start|>system\nYou are MistralOrca, a large language model trained by Alignment Lab AI. Write out your reasoning step-by-step to be sure you get the right answers!\n<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n",
    "temperature": 0.2,
    "top_k": 50,
    "top_p": 0.95
  },
  "logs": "Generated 398 tokens in 4.958377122879028 seconds.",
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  },
  "metrics": {
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  }
}

Generated in

5.0 seconds

Tweak it Report