cuuupid/glm-4v-9b:a75c9193 | Run with an API on Replicate

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cuuupid /glm-4v-9b:a75c9193

Playground API

Input

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

npx create-replicate --model=cuuupid/glm-4v-9b

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 cuuupid/glm-4v-9b using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "cuuupid/glm-4v-9b:a75c919339f65bf00afa96511af653fdbd0ec3cb0f5e6f4350809445eee0e14f",
  {
    input: {
      image: "https://replicate.delivery/pbxt/L4yoSgMNJ5AZ7NbzR4RfcAmofQqEfzVKDyI3MB6lnxeWn7dF/image.png",
      prompt: "请详细总结图片中文章的要点"
    }
  }
);

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 cuuupid/glm-4v-9b using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "cuuupid/glm-4v-9b:a75c919339f65bf00afa96511af653fdbd0ec3cb0f5e6f4350809445eee0e14f",
    input={
        "image": "https://replicate.delivery/pbxt/L4yoSgMNJ5AZ7NbzR4RfcAmofQqEfzVKDyI3MB6lnxeWn7dF/image.png",
        "prompt": "请详细总结图片中文章的要点"
    }
)
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 cuuupid/glm-4v-9b 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": "cuuupid/glm-4v-9b:a75c919339f65bf00afa96511af653fdbd0ec3cb0f5e6f4350809445eee0e14f",
    "input": {
      "image": "https://replicate.delivery/pbxt/L4yoSgMNJ5AZ7NbzR4RfcAmofQqEfzVKDyI3MB6lnxeWn7dF/image.png",
      "prompt": "请详细总结图片中文章的要点"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

这篇文章主要介绍了面壁Ultra对齐技术在大模型上的应用。面壁Ultra是一种高效的大模型训练技术，它通过自监督预训练和迁移学习等技术，在大规模数据集上进行训练，从而提高模型的性能和泛化能力。文章还提到了面壁Ultra的一些特点和优势，例如数据质量、模型结构等。此外，文章还提到了面壁Ultra在实际应用中的效果，如SFT和KTO等任务的性能提升。 <|endoftext|>

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