Instructions to use GIGAParviz/V-TTS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GIGAParviz/V-TTS with Transformers:

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

pipe = pipeline("text-generation", model="GIGAParviz/V-TTS")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GIGAParviz/V-TTS")
model = AutoModelForCausalLM.from_pretrained("GIGAParviz/V-TTS")
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 GIGAParviz/V-TTS with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/GIGAParviz/V-TTS

SGLang

How to use GIGAParviz/V-TTS 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 "GIGAParviz/V-TTS" \
    --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": "GIGAParviz/V-TTS",
		"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 "GIGAParviz/V-TTS" \
        --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": "GIGAParviz/V-TTS",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use GIGAParviz/V-TTS with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for GIGAParviz/V-TTS to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for GIGAParviz/V-TTS to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for GIGAParviz/V-TTS to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="GIGAParviz/V-TTS",
    max_seq_length=2048,
)

Docker Model Runner
How to use GIGAParviz/V-TTS with Docker Model Runner:
```
docker model run hf.co/GIGAParviz/V-TTS
```

GIGAParviz commited on 18 days ago

Commit

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1 Parent(s): 90a84f2

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Files changed (4) hide show

chat_template.jinja +1 -45
special_tokens_map.json +0 -0
tokenizer.json +2 -2
tokenizer_config.json +0 -0

chat_template.jinja CHANGED Viewed

@@ -1,45 +1 @@
-{%- for message in messages %}
-    {#-- Validate role is a stringified integer --#}
-    {%- if not message['role'] is string or not message['role'].isdigit() %}
-        {{- raise_exception("The role must be an integer or a stringified integer (e.g. '0') designating the speaker id") }}
-    {%- endif %}
-    {#-- Validate content is a list --#}
-    {%- set content = message['content'] %}
-    {%- if content is not iterable or content is string %}
-        {{- raise_exception("The content must be a list") }}
-    {%- endif %}
-    {#-- Collect content types --#}
-    {%- set content_types = content | map(attribute='type') | list %}
-    {%- set is_last = loop.last %}
-    {#-- Last message validation --#}
-    {%- if is_last %}
-        {%- if 'text' not in content_types %}
-            {{- raise_exception("The last message must include one item of type 'text'") }}
-        {%- elif (content_types | select('equalto', 'text') | list | length > 1) or (content_types | select('equalto', 'audio') | list | length > 1) %}
-            {{- raise_exception("At most two items are allowed in the last message: one 'text' and one 'audio'") }}
-        {%- endif %}
-    {#-- All other messages validation --#}
-    {%- else %}
-        {%- if content_types | select('equalto', 'text') | list | length != 1
-              or content_types | select('equalto', 'audio') | list | length != 1 %}
-            {{- raise_exception("Each message (except the last) must contain exactly one 'text' and one 'audio' item") }}
-        {%- elif content_types | reject('in', ['text', 'audio']) | list | length > 0 %}
-            {{- raise_exception("Only 'text' and 'audio' types are allowed in content") }}
-        {%- endif %}
-    {%- endif %}
-{%- endfor %}
-{%- for message in messages %}
-    {{- bos_token }}
-    {{- '[' + message['role'] + ']' }}
-    {{- message['content'][0]['text'] }}
-    {{- eos_token }}
-    {%- if message['content']|length > 1 %}
-        {{- '<|AUDIO|><|audio_eos|>' }}
-    {%- endif %}
-{%- endfor %}


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+size 18366636

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