Instructions to use nvidia/Riva-Translate-4B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nvidia/Riva-Translate-4B-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="nvidia/Riva-Translate-4B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nvidia/Riva-Translate-4B-Instruct")
model = AutoModelForCausalLM.from_pretrained("nvidia/Riva-Translate-4B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nvidia/Riva-Translate-4B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nvidia/Riva-Translate-4B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nvidia/Riva-Translate-4B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nvidia/Riva-Translate-4B-Instruct

SGLang

How to use nvidia/Riva-Translate-4B-Instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "nvidia/Riva-Translate-4B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nvidia/Riva-Translate-4B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "nvidia/Riva-Translate-4B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nvidia/Riva-Translate-4B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nvidia/Riva-Translate-4B-Instruct with Docker Model Runner:
```
docker model run hf.co/nvidia/Riva-Translate-4B-Instruct
```

Mujojojo commited on Dec 10, 2025

Commit

9e2aad5

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1 Parent(s): 7e0a807

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@@ -3,17 +3,21 @@ license: other
 license_name: nvidia-open-model-license-agreement
 license_link: >-
   https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
 library_name: transformers
 base_model:
-- nvidia/Mistral-NeMo-Minitron-4B-8K-Base
 ---
 # Riva-Translate-4B-Instruct
 ## Model Overview
 The Riva-Translate-4B-Instruct Neural Machine Translation model translates text in 12 languages. The supported languages are: English(en), German(de), European Spanish(es-ES), LATAM Spanish(es-US), France(fr), Brazillian Portugese(pt-BR), Russian(ru), Simplified Chinese(zh-CN), Traditional Chinese(zh-TW), Japanese(ja),Korean(ko), Arabic(ar).
-This model was developed based on the decoder-only Transformer architecture. It is a fine-tuned version of a 4B Base model that was pruned and distilled from [nvidia/Mistral-NeMo-Minitron-8B-Base](https://huggingface.co/nvidia/Mistral-NeMo-Minitron-8B-Base) using our LLM compression technique. The model was trained using a multi-stage CPT and SFT. It uses tiktoken as the tokenizer. The model supports a context length of 8K tokens.
 **Model Developer:** NVIDIA

 license_name: nvidia-open-model-license-agreement
 license_link: >-
   https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
 library_name: transformers
 base_model:
+- nvidia/Mistral-NeMo-12B-Base
 ---
 # Riva-Translate-4B-Instruct
+## 🚀 **Announcement**
+We’re excited to introduce the latest update to our Riva-Translate-4B-Instruct model!
+Explore **[nvidia/Riva-Translate-4B-Instruct-v1.1](https://huggingface.co/nvidia/Riva-Translate-4B-Instruct-v1.1)** to experience improved translation quality and enhanced performance.
 ## Model Overview
 The Riva-Translate-4B-Instruct Neural Machine Translation model translates text in 12 languages. The supported languages are: English(en), German(de), European Spanish(es-ES), LATAM Spanish(es-US), France(fr), Brazillian Portugese(pt-BR), Russian(ru), Simplified Chinese(zh-CN), Traditional Chinese(zh-TW), Japanese(ja),Korean(ko), Arabic(ar).
+This model was developed based on the decoder-only Transformer architecture. It is a fine-tuned version of a 4B Base model that was pruned and distilled from [nvidia/Mistral-NeMo-12B-Base](https://huggingface.co/nvidia/Mistral-NeMo-12B-Base) using our LLM compression technique. The model was trained using a multi-stage CPT and SFT. It uses tiktoken as the tokenizer. The model supports a context length of 8K tokens.
 **Model Developer:** NVIDIA