Instructions to use Unbabel/Tower-Plus-72B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Unbabel/Tower-Plus-72B with Transformers:

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

pipe = pipeline("text-generation", model="Unbabel/Tower-Plus-72B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Unbabel/Tower-Plus-72B")
model = AutoModelForCausalLM.from_pretrained("Unbabel/Tower-Plus-72B")
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 Unbabel/Tower-Plus-72B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Unbabel/Tower-Plus-72B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Unbabel/Tower-Plus-72B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Unbabel/Tower-Plus-72B

SGLang

How to use Unbabel/Tower-Plus-72B 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 "Unbabel/Tower-Plus-72B" \
    --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": "Unbabel/Tower-Plus-72B",
		"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 "Unbabel/Tower-Plus-72B" \
        --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": "Unbabel/Tower-Plus-72B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Unbabel/Tower-Plus-72B with Docker Model Runner:
```
docker model run hf.co/Unbabel/Tower-Plus-72B
```

nielsr HF Staff commited on Jul 4, 2025

Commit

4e7eb0a

verified ·

1 Parent(s): b7d7025

Update README.md

Browse files

Files changed (1) hide show

README.md +2 -6

README.md CHANGED Viewed

@@ -68,9 +68,7 @@ sampling_params = SamplingParams(
   max_tokens=8192,
 )
 llm = LLM(model="Unbabel/Tower-Plus-72B", tensor_parallel_size=4)
-messages = [{"role": "user", "content": "Translate the following English source text to Portuguese (Portugal):
-English: Hello world!
-Portuguese (Portugal): "}]
 outputs = llm.chat(messages, sampling_params)
 # Make sure your prompt_token_ids look like this
 print (outputs[0].outputs[0].text)
@@ -87,9 +85,7 @@ from transformers import pipeline
 pipe = pipeline("text-generation", model="Unbabel/Tower-Plus-72B", device_map="auto")
 # We use the tokenizer’s chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
-messages = [{"role": "user", "content": "Translate the following English source text to Portuguese (Portugal):
-English: Hello world!
-Portuguese (Portugal): "}]
 input_ids = pipe.tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True)
 outputs = pipe(messages, max_new_tokens=256, do_sample=False)
 print(outputs[0]["generated_text"])

   max_tokens=8192,
 )
 llm = LLM(model="Unbabel/Tower-Plus-72B", tensor_parallel_size=4)
+messages = [{"role": "user", "content": "Translate the following English source text to Portuguese (Portugal):\nEnglish: Hello world!\nPortuguese (Portugal): "}]
 outputs = llm.chat(messages, sampling_params)
 # Make sure your prompt_token_ids look like this
 print (outputs[0].outputs[0].text)
 pipe = pipeline("text-generation", model="Unbabel/Tower-Plus-72B", device_map="auto")
 # We use the tokenizer’s chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
+messages = [{"role": "user", "content": "Translate the following English source text to Portuguese (Portugal):\nEnglish: Hello world!\nPortuguese (Portugal): "}]
 input_ids = pipe.tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True)
 outputs = pipe(messages, max_new_tokens=256, do_sample=False)
 print(outputs[0]["generated_text"])