madlad400 3b mt

An open translation model that turns English text into the language you name. google/madlad400-3b-mt

Get started Run in your cloud Hugging Face

Accuracy

44.6

WMT24++, 1,488 segments, chrF++
Time per request

Not timed one request at a time
Serverless price

$0.999 in · $5.00 out

per 1M tokens

Call it

from sie_sdk import SIEClient
client = SIEClient(api_key="API keysk-sie-…")
text = "textThe train to Munich leaves at half past seven tomorrow morning."
result = client.generate(
    "modelgoogle/madlad400-3b-mt",
    f"<2de> {text}",
    max_new_tokens=64,
    temperature=0,
)
print(result["text"])

import { SIEClient } from '@superlinked/sie-sdk';

const client = new SIEClient(undefined, { apiKey: 'sk-sie-…' });

const text = "The train to Munich leaves at half past seven tomorrow morning.";
const result = await client.generate(
  'google/madlad400-3b-mt',
  `<2de> ${text}`,
  { maxNewTokens: 64, temperature: 0 },
);
console.log(result.text);

curl https://api.superlinked.com/v1/generate/google__madlad400-3b-mt \
  -H "Authorization: Bearer sk-sie-…" \
  -H "Content-Type: application/json" \
  -d "{\"prompt\":\"<2de> The train to Munich leaves at half past seven tomorrow morning.\",\"max_new_tokens\":64,\"temperature\":0}"

Build the "Translation" capability into my app using the Superlinked Inference Engine (SIE).

Context
- SIE is an OpenAI-style inference API. Python SDK: `from sie_sdk import SIEClient`; TypeScript: `@superlinked/sie-sdk`.
- Base URL: https://api.superlinked.com (or my regional endpoint). Auth: Bearer key from env `SIE_API_KEY` (never hard-code it).
- Model: google/madlad400-3b-mt (SIE primitive: /generate). Keep the model id configurable.

Task
- Input: a block of English text plus the target locale.
- Behaviour: return the text translated into the selected target language
- Call the selected SIE primitive once per request and map the response into your domain type.

Deliverables
- A typed client wrapper, an application-level function for this task, error handling for timeouts/empty input, and unit tests with a stubbed client.
- Wire it into my existing stack (ask me which framework if unclear) and add a short usage example.

Run

Next to the other translation models

Next to the other translation models
Model	WMT24++, 1,488 segments, chrF++	Time per request	Serverless price	Size
madlad400 3b mt google	44.6	Not timed	$0.999 in · $5.00 out per 1M tokens	2.9B
Hy-MT2 1.8B tencent	48.1	Not timed	$1.13 in · $5.63 out per 1M tokens	2.0B

Both translation models

Facts

Size: 2.9B parameters
License: Apache-2.0
Reads: Text, up to 512 tokens
Endpoint: generate

Evaluation

Accuracy: Run on SIE against the public dataset named in each figure
Prices: Serverless list prices

Run madlad400 3b mt on Serverless or in your own cloud

SIE Serverless

We host the models; you call one API.

No idle GPUs, pay for what you use
Fits your stack: SDK, API, CLI, MCP
Self-host the same open-source engine
SOC 2 Type 2, US or EU data residency
Create an account to try Serverless

Get started

SIE Cluster

Kubernetes in your own cloud, backed by our engineers.

Terraform modules for your cloud
Apache-2.0, same engine as Serverless
GPU workers scale to zero between jobs
Separate worker pools for each tenant

Agent prompt Deploy SIE to our AWS account with the superlinked/sie/aws Terraform module. Docs: superlinked.com/docs/deployment.

Deploy SIE to our Google Cloud project with the superlinked/sie/google Terraform module. Docs: superlinked.com/docs/deployment.

Deploy SIE to our Azure subscription with the superlinked/sie/azure Terraform module. Docs: superlinked.com/docs/deployment.

Deploy SIE to our Alibaba Cloud account with the superlinked/sie/alicloud Terraform module. Docs: superlinked.com/docs/deployment.

Talk to us Deploy guide

SIE Local

The open-source server on your own machine.

NVIDIA GPU
One container, no cluster
Process your data fully offline
Same SDK and model IDs as Serverless

docker run --gpus all -p 8080:8080 ghcr.io/superlinked/sie-server:latest-cuda12-ctranslate2

Quickstart