Instructions to use juno-labs/gemma-text-vad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use juno-labs/gemma-text-vad with Transformers:

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

pipe = pipeline("text-generation", model="juno-labs/gemma-text-vad")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("juno-labs/gemma-text-vad")
model = AutoModelForCausalLM.from_pretrained("juno-labs/gemma-text-vad")
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 juno-labs/gemma-text-vad with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/juno-labs/gemma-text-vad

SGLang

How to use juno-labs/gemma-text-vad 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 "juno-labs/gemma-text-vad" \
    --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": "juno-labs/gemma-text-vad",
		"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 "juno-labs/gemma-text-vad" \
        --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": "juno-labs/gemma-text-vad",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use juno-labs/gemma-text-vad with Docker Model Runner:
```
docker model run hf.co/juno-labs/gemma-text-vad
```

This is a text-based Voice Activity Detection model that determines if a given speech fragment is complete enough for processing by a smart speaker assistant. This allows smart speakers to move from using time based pauses (300ms - 1000ms) to detect the end of voice input to using this model to determine if the voice input is complete.

Example:

"Hey" -> no
"Hey Juno" -> no
"Hey Juno can you" -> no
"Hey Juno can you set" -> no
"Hey Juno can you set the" -> no
"Hey Juno can you set the temperature" -> no
"Hey Juno can you set the temperature to" -> no
"Hey Juno can you set the temperature to 65" -> yes

Model prompting requirements:

Required system prompt: "You are a Voice Activity Detection system. Determine if the given speech fragment is complete enough for processing. Answer with only 'yes' if complete or 'no' if incomplete."
Required user prompt: "Is this sentence fragment complete for processing: '{fragment}'"

To use with pipeline from transformers:

from transformers import pipeline

pipe = pipeline("text-generation", model="juno-labs/gemma-text-vad")

SYSTEM_PROMPT = (
  "You are a Voice Activity Detection system. "
  "Determine if the given speech fragment is complete enough for processing. Answer with only 'yes' if complete or 'no' if incomplete."
)

SENTENCE = "Hey Juno can you set the temperature to"

messages = [
    {'content': SYSTEM_PROMPT, 'role': 'system'},
    {'content': f"Is this sentence fragment complete for processing: '{SENTENCE}'", 'role': 'user'}
]

generated = pipe(messages)

classification = generated[0]["generated_text"][2]["content"]

print(f"Classification: {classification}") # "yes" or "no"

To use with transformers:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_ID = "juno-labs/gemma-text-vad"

# Load model + tokenizer
model = AutoModelForCausalLM.from_pretrained(MODEL_ID, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)

SYSTEM_PROMPT = (
    "You are a Voice Activity Detection system. "
    "Determine if the given speech fragment is complete enough for processing. Answer with only 'yes' if complete or 'no' if incomplete."
)

SENTENCE = "Set the temperature to 68"

messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {"role": "user", "content": f"Is this sentence fragment complete for processing: '{SENTENCE}'"},
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=1,        # only 1 token
        do_sample=False,         # greedy decoding
        pad_token_id=tokenizer.eos_token_id,
    )

decoded = tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)

print(f"Classification: {decoded}") # "yes" or "no"