Prediction

Model

ibm-granite/granite-embedding-278m-multilingual:f7fa31b0

nv2z109h8hrm80cpx2k9zpyngw

Status

Succeeded

Source

Web

Hardware

L40S

Total duration

2m 14s

Created

7 months ago

Webhook

–

Input

texts: Dogs are mammals , Fish are not mammals

{
  "texts": [
    "Dogs are mammals ",
    "Fish are not mammals "
  ]
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_JHk**********************************

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 ibm-granite/granite-embedding-278m-multilingual using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "ibm-granite/granite-embedding-278m-multilingual:f7fa31b092ccacba82485168a2ee42f6ceeb62829706f63947b105715533ae63",
  {
    input: {
      texts: ["Dogs are mammals ","Fish are not mammals "]
    }
  }
);

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_JHk**********************************

This is your API token. Keep it to yourself.

Import the client:

import replicate

Run ibm-granite/granite-embedding-278m-multilingual using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "ibm-granite/granite-embedding-278m-multilingual:f7fa31b092ccacba82485168a2ee42f6ceeb62829706f63947b105715533ae63",
    input={
        "texts": ["Dogs are mammals ","Fish are not mammals "]
    }
)

# The ibm-granite/granite-embedding-278m-multilingual 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/ibm-granite/granite-embedding-278m-multilingual/api#output-schema
    print(item)

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_JHk**********************************

This is your API token. Keep it to yourself.

Run ibm-granite/granite-embedding-278m-multilingual 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": "ibm-granite/granite-embedding-278m-multilingual:f7fa31b092ccacba82485168a2ee42f6ceeb62829706f63947b105715533ae63",
    "input": {
      "texts": ["Dogs are mammals ","Fish are not mammals "]
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "outputType": "array",
  "arrayType": "number",
  "items": {
    "type": "number"
  },
  "count": 768,
  "inferred": false,
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2 / 2

{
  "id": "nv2z109h8hrm80cpx2k9zpyngw",
  "model": "ibm-granite/granite-embedding-278m-multilingual",
  "version": "f7fa31b092ccacba82485168a2ee42f6ceeb62829706f63947b105715533ae63",
  "input": {
    "texts": [
      "Dogs are mammals ",
      "Fish are not mammals "
    ]
  },
  "logs": "2025-05-19 13:07:42 [info     ] predict() commencing           input_count=2 request_id=c63f5dda884e442b9e3fde3e83f16258\n2025-05-19 13:07:42 [debug    ] PoolingParams                  input_count=2 pooling_params=PoolingParams(dimensions=None, additional_metadata=None) request_id=c63f5dda884e442b9e3fde3e83f16258\nINFO 05-19 13:07:42 [async_llm_engine.py:211] Added request c63f5dda884e442b9e3fde3e83f16258/0.\n2025-05-19 13:07:42 [debug    ] Embedding result               embedding_length=768 finished=True input_count=2 input_tokens=7 request_id=c63f5dda884e442b9e3fde3e83f16258/0 text=Dogs are mammals\nINFO 05-19 13:07:42 [async_llm_engine.py:211] Added request c63f5dda884e442b9e3fde3e83f16258/1.\n2025-05-19 13:07:42 [debug    ] Embedding result               embedding_length=768 finished=True input_count=2 input_tokens=7 request_id=c63f5dda884e442b9e3fde3e83f16258/1 text=Fish are not mammals\n2025-05-19 13:07:42 [info     ] Embedding took 0.01s           input_count=2 request_id=c63f5dda884e442b9e3fde3e83f16258\n/root/.pyenv/versions/3.12.10/lib/python3.12/site-packages/cog/server/scope.py:21: ExperimentalFeatureWarning: current_scope is an experimental internal function. It may change or be removed without warning.\n  warnings.warn(\n2025-05-19 13:07:42 [info     ] predict() complete             input_count=2 request_id=c63f5dda884e442b9e3fde3e83f16258",
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