Update Model Card

README.md

@@ -53,27 +53,211 @@ You are an expert at translating text from English to Simplified Chinese.</s>
 What is the Simplified Chinese translation of the sentence: The GRACE mission is a collaboration between the NASA and German Aerospace Center.?</s>
 <s>Assistant
 ```
-## Usage
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
 
+## Quick Start Guide
+
+### How to Choose the Language Pair
+
+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-de`: English to German  
+* `en-es` or `en-es-es`: English to European Spanish  
+* `en-es-us`: English to Latin American Spanish  
+* `en-fr`: English to French  
+* `en-ja`: English to Japanese  
+* `en-ko`: English to Korean  
+* `en-ru`: English to Russian  
+* `en-pt`: English to Brazilian Portuguese  
+* `en-pt-br`: English to Brazilian Portuguese  
+* `zh-en` or `zh-cn-en`: Simplified Chinese to English  
+* `zh-tw-en`: Traditional Chinese to English  
+* `ar-en`: Arabic to English  
+* `de-en`: German to English  
+* `es-en` or `es-es-en`: European Spanish to English  
+* `es-us-en`: Latin American Spanish to English  
+* `fr-en`: French to English  
+* `ja-en`: Japanese to English  
+* `ko-en`: Korean to English  
+* `ru-en`: Russian to English  
+* `pt-en` or `pt-br-en`: Brazilian Portuguese to English
+
+### Use it with Transformers
+
+```
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
 tokenizer = AutoTokenizer.from_pretrained("nvidia/Riva-Translate-4B-Instruct-v1.1")
 model = AutoModelForCausalLM.from_pretrained("nvidia/Riva-Translate-4B-Instruct-v1.1").cuda()
 
-
-# Use the prompt template
+# Use the prompt template (along with chat template)
 messages = [
     {
         "role": "system",
-        "content": "You are an expert at translating text from English to Simplified Chinese.",
+        "content": "en-zh",
     },
-    {"role": "user", "content": "What is the Simplified Chinese translation of the sentence: The GRACE mission is a collaboration between the NASA and German Aerospace Center.?"},
+    {"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 -U "vllm>=0.12.0"
+```
+
+Launch a vLLM server using the below python command. In this example, we use a context length of 8k as supported by the model.
+
+```
+python3 -m vllm.entrypoints.openai.api_server \
+     --model nvidia/Riva-Translate-4B-Instruct-v1.1 \
+     --dtype bfloat16 \
+     --gpu-memory-utilization 0.95 \
+     --max-model-len 8192 \
+     --host 0.0.0.0 \
+     --port 8000 \
+     --tensor-parallel-size 1 \
+     --served-model-name Riva-Translate-4B-Instruct-v1.1
+```
+
+Alternatively, you can use Docker to launch a vLLM server.
+
+```
+docker run --runtime nvidia --gpus all \
+           -v ~/.cache/huggingface:/root/.cache/huggingface \
+           -p 8000:8000 \
+           --ipc=host \
+           vllm/vllm-openai:v0.12.0 \
+           --model nvidia/Riva-Translate-4B-Instruct-v1.1 \
+           --dtype bfloat16 \
+           --gpu-memory-utilization 0.95 \
+           --max-model-len 8192 \
+           --host 0.0.0.0 \
+           --port 8000 \
+           --tensor-parallel-size 1 \
+           --served-model-name Riva-Translate-4B-Instruct-v1.1
+```
+
+If you are using DGX Spark or Jetson Thor, please use [this vllm container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/vllm?version=25.12.post1-py3). On Jetson Thor, be sure to include `--runtime nvidia` when running the Docker container.
+
+```
+# On DGX SPark or Jetson Thor
+docker run \
+  --runtime nvidia \ # Remove this on DGX Spark
+  -v ~/.cache/huggingface:/root/.cache/huggingface \
+  -p 8000:8000 \
+  --ipc=host \
+  nvcr.io/nvidia/vllm:25.12.post1-py3 \
+  vllm serve nvidia/Riva-Translate-4B-Instruct-v1.1 \
+    --dtype bfloat16 \
+    --gpu-memory-utilization 0.95 \
+    --max-model-len 8192 \
+    --host 0.0.0.0 \
+    --port 8000 \
+    --tensor-parallel-size 1 \
+    --served-model-name Riva-Translate-4B-Instruct-v1.1
+```
+
+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-v1.1",
+"messages": [
+      {"role": "system", "content": "en-zh"},
+      {"role": "user", "content": "The GRACE mission is a collaboration between the NASA and German Aerospace Center.?"}
+    ]
+}'
+```
+
+### Chat Template Structure
+```
+{%- set language_pairs = {
+  'en-zh-cn': {'source': 'English', 'target': 'Simplified Chinese'},
+  'en-zh': {'source': 'English', 'target': 'Simplified Chinese'},
+  'en-zh-tw': {'source': 'English', 'target': 'Traditional Chinese'},
+  'en-ar': {'source': 'English', 'target': 'Arabic'},
+  'en-de': {'source': 'English', 'target': 'German'},
+  'en-es': {'source': 'English', 'target': 'European Spanish'},
+  'en-es-es': {'source': 'English', 'target': 'European Spanish'},
+  'en-es-us': {'source': 'English', 'target': 'Latin American Spanish'},
+  'en-fr': {'source': 'English', 'target': 'French'},
+  'en-ja': {'source': 'English', 'target': 'Japanese'},
+  'en-ko': {'source': 'English', 'target': 'Korean'},
+  'en-ru': {'source': 'English', 'target': 'Russian'},
+  'en-pt': {'source': 'English', 'target': 'Brazilian Portuguese'},
+  'en-pt-br': {'source': 'English', 'target': 'Brazilian Portuguese'},
+  'zh-en': {'source': 'Simplified Chinese', 'target': 'English'},
+  'zh-cn-en': {'source': 'Simplified Chinese', 'target': 'English'},
+  'zh-tw-en': {'source': 'Traditional Chinese', 'target': 'English'},
+  'ar-en': {'source': 'Arabic', 'target': 'English'},
+  'de-en': {'source': 'German', 'target': 'English'},
+  'es-en': {'source': 'European Spanish', 'target': 'English'},
+  'es-es-en': {'source': 'European Spanish', 'target': 'English'},
+  'es-us-en': {'source': 'Latin American Spanish', 'target': 'English'},
+  'fr-en': {'source': 'French', 'target': 'English'},
+  'ja-en': {'source': 'Japanese', 'target': 'English'},
+  'ko-en': {'source': 'Korean', 'target': 'English'},
+  'ru-en': {'source': 'Russian', 'target': 'English'},
+  'pt-en': {'source': 'Brazilian Portuguese', 'target': 'English'},
+  'pt-br-en': {'source': 'Brazilian Portuguese', 'target': 'English'},
+} -%}
+
+{%- set system_message = '' -%}
+{%- set source_lang = '' -%}
+{%- set target_lang = '' -%}
+
+{%- if messages[0]['role'] == 'system' -%}
+  {%- set lang_pair = messages[0]['content'] | trim -%}
+  {%- set messages = messages[1:] -%}
+  {%- if lang_pair in language_pairs -%}
+    {%- set source_lang = language_pairs[lang_pair]['source'] -%}
+    {%- set target_lang = language_pairs[lang_pair]['target'] -%}
+    {%- set system_message = 'You are an expert at translating text from ' + source_lang + ' to ' + target_lang + '.' -%}
+  {%- else -%}
+    {%- set system_message = 'You are a translation expert.' -%}
+  {%- endif -%}
+{%- endif -%}
+
+{{- '<s>System\n' + system_message + '</s>\n' -}}
+
+{%- for message in messages -%}
+  {%- if (message['role'] in ['user']) != (loop.index0 % 2 == 0) -%}
+    {{- raise_exception('Conversation roles must alternate between user and assistant') -}}
+  {%- elif message['role'] == 'user' -%}
+    {%- set user_content = (
+          target_lang
+          and 'What is the ' + target_lang + ' translation of the sentence: ' + message['content'] | trim
+          or message['content'] | trim
+        ) -%}
+    {{- '<s>User\n' + user_content + '</s>\n' -}}
+  {%- elif message['role'] == 'assistant' -%}
+    {{- '<s>Assistant\n' + message['content'] | trim + '</s>\n' -}}
+  {%- endif -%}
+{%- endfor -%}
+
+{%- if add_generation_prompt -%}
+  {{ '<s>Assistant\n' }}
+{%- endif -%}
+```
+
+## Inference
+* Engine: HF, vLLM  
+* 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.