Instructions to use ModalityDance/latent-tts-colar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ModalityDance/latent-tts-colar with Transformers:

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

pipe = pipeline("text-generation", model="ModalityDance/latent-tts-colar")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, ColarLlama

tokenizer = AutoTokenizer.from_pretrained("ModalityDance/latent-tts-colar")
model = ColarLlama.from_pretrained("ModalityDance/latent-tts-colar")
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

vLLM

How to use ModalityDance/latent-tts-colar with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ModalityDance/latent-tts-colar

SGLang

How to use ModalityDance/latent-tts-colar 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 "ModalityDance/latent-tts-colar" \
    --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": "ModalityDance/latent-tts-colar",
		"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 "ModalityDance/latent-tts-colar" \
        --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": "ModalityDance/latent-tts-colar",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ModalityDance/latent-tts-colar with Docker Model Runner:
```
docker model run hf.co/ModalityDance/latent-tts-colar
```

Add pipeline tag and paper/code links

by nielsr HF Staff - opened Jan 12

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+10

-5

nielsr

Jan 12

Hi,

This PR improves the model card by:

Adding the pipeline_tag: text-generation to the metadata for better discoverability.
Adding direct links to the associated paper "Parallel Test-Time Scaling for Latent Reasoning Models" and the official GitHub repository.

The existing technical documentation and sample usage provided by the authors have been preserved.

Best,
Niels

Add pipeline tag and paper/code links49189628

dd101bb changed pull request status to merged Jan 14

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