The Riva-Translate-4B-Instruct-v2 Neural Machine Translation model translates text in English and 36 non-English languages. The supported languages are: English(en), Czech(cs), Danish(da), German(de), Greek(el), European Spanish(es-ES), LATAM Spanish(es-US), Finnish(fi), French(fr), Hungarian(hu), Italian(it), Lithuanian(lt), Latvian(lv), Dutch(nl), Norwegian(no), Polish(pl), European Portuguese(pt-PT), Brazilian Portuguese(pt-BR), Romanian(ro), Russian(ru), Slovak(sk), Swedish(sv), Simplified Chinese(zh-CN), Traditional Chinese(zh-TW), Japanese(ja), Hindi(hi), Korean(ko), Estonian(et), Slovenian(sl), Bulgarian(bg), Ukrainian(uk), Croatian(hr), Arabic(ar), Vietnamese(vi), Turkish(tr), Indonesian(id), Thai(th). It supports both sentence- and document-level translation. The model surpasses all in-house NMT models we've built so far.
Model Developer: NVIDIA
Model Dates: Riva-Translate-4B-Instruct-v2 was trained between Nov 2025 and May 2026.
To select a language pair for translation, include one of the following tags in the system prompt:
en-zh-cn or en-zh: English to Simplified Chinese
en-zh-tw: English to Traditional Chinese
en-ar: English to Arabic
en-bg: English to Bulgarian
en-cs: English to Czech
en-da: English to Danish
en-de: English to German
en-el: English to Greek
en-es or en-es-es: English to European Spanish
en-es-us: English to Latin American Spanish
en-et: English to Estonian
en-fi: English to Finnish
en-fr: English to French
en-hi: English to Hindi
en-hr: English to Croatian
en-hu: English to Hungarian
en-id: English to Indonesian
en-it: English to Italian
en-ja: English to Japanese
en-ko: English to Korean
en-lt: English to Lithuanian
en-lv: English to Latvian
en-nl: English to Dutch
en-no: English to Norwegian
en-pl: English to Polish
en-pt or en-pt-pt: English to European Portuguese
en-pt-br: English to Brazilian Portuguese
en-ro: English to Romanian
en-ru: English to Russian
en-sk: English to Slovak
en-sl: English to Slovenian
en-sv: English to Swedish
en-th: English to Thai
en-tr: English to Turkish
en-uk: English to Ukrainian
en-vi: English to Vietnamese
zh-en or zh-cn-en: Simplified Chinese to English
zh-tw-en: Traditional Chinese to English
ar-en: Arabic to English
bg-en: Bulgarian to English
cs-en: Czech to English
da-en: Danish to English
de-en: German to English
el-en: Greek to English
es-en or es-es-en: European Spanish to English
es-us-en: Latin American Spanish to English
et-en: Estonian to English
fi-en: Finnish to English
fr-en: French to English
hi-en: Hindi to English
hr-en: Croatian to English
hu-en: Hungarian to English
id-en: Indonesian to English
it-en: Italian to English
ja-en: Japanese to English
ko-en: Korean to English
lt-en: Lithuanian to English
lv-en: Latvian to English
nl-en: Dutch to English
no-en: Norwegian to English
pl-en: Polish to English
pt-en or pt-pt-en: European Portuguese to English
pt-br-en: Brazilian Portuguese to English
ro-en: Romanian to English
ru-en: Russian to English
sk-en: Slovak to English
sl-en: Slovenian to English
sv-en: Swedish to English
th-en: Thai to English
tr-en: Turkish to English
uk-en: Ukrainian to English
vi-en: Vietnamese to English
Use it with Transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("nvidia/Riva-Translate-4B-Instruct-v2")
model = AutoModelForCausalLM.from_pretrained("nvidia/Riva-Translate-4B-Instruct-v2")
messages = [
{
"role": "system",
"content": "en-zh-cn",
},
{"role": "user", "content": "The GRACE mission is a collaboration between the NASA and German Aerospace Center.?"},
]
tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(tokenized_chat, max_new_tokens=128, pad_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(outputs[0]))
Use it with vLLM
To install vllm, use the following pip command in a terminal within a supported environment.
pip install vllm: 0.19.1
Launch a vLLM server using the below python command. In this example, we use a context length of 8k as supported by the model.
If you are using DGX Spark or Jetson Thor, please use this vllm container. On Jetson Thor, be sure to include --runtime nvidia when running the Docker container.
On Jetson Thor, the previous vLLM cache is not currently cleaned automatically, so it must be cleared manually. Always run this command on the host before serving any model on Jetson Thor.
sudo sysctl -w vm.drop_caches=3
Here is an example client code for vLLM.
curl http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json"
-d '{
"model": "Riva-Translate-4B-Instruct-v2",
"messages": [
{"role": "system", "content": "en-zh"},
{"role": "user", "content": "The GRACE mission is a collaboration between the NASA and German Aerospace Center.?"}
]
}'
Test Hardware: NVIDIA A100, H100 80GB, Jetson Thor, DGX Spark
Ethical Considerations
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
We advise against circumvention of any provided safety guardrails contained in the Model without a substantially similar guardrail appropriate for your use case. For more details: Safety and Explainability Subcards.
For more detailed information on ethical considerations for this model, please see the Model Card++ Bias, and Privacy Subcards.
Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.
Technical Limitations & Mitigation:
Accuracy varies based on the characteristics of input (Domain, Use Case, Noise, Context, etc.). Grammar errors and semantic issues may be present. As a potential mitigation, the user can change the prompt to get a better translation.
