Instructions to use LanguaMan/vieneu-tts-v2-exp-tuning-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LanguaMan/vieneu-tts-v2-exp-tuning-model with Transformers:

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

pipe = pipeline("text-generation", model="LanguaMan/vieneu-tts-v2-exp-tuning-model")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LanguaMan/vieneu-tts-v2-exp-tuning-model")
model = AutoModelForCausalLM.from_pretrained("LanguaMan/vieneu-tts-v2-exp-tuning-model")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LanguaMan/vieneu-tts-v2-exp-tuning-model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LanguaMan/vieneu-tts-v2-exp-tuning-model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LanguaMan/vieneu-tts-v2-exp-tuning-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LanguaMan/vieneu-tts-v2-exp-tuning-model

SGLang

How to use LanguaMan/vieneu-tts-v2-exp-tuning-model 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 "LanguaMan/vieneu-tts-v2-exp-tuning-model" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LanguaMan/vieneu-tts-v2-exp-tuning-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "LanguaMan/vieneu-tts-v2-exp-tuning-model" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LanguaMan/vieneu-tts-v2-exp-tuning-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LanguaMan/vieneu-tts-v2-exp-tuning-model with Docker Model Runner:
```
docker model run hf.co/LanguaMan/vieneu-tts-v2-exp-tuning-model
```

Configuration Parsing Warning:Config file tokenizer_config.json cannot be fetched (too big)

VieNeu-TTS-v2-exp-tuning

This model is a fine-tuned version of pnnbao-ump/VieNeu-TTS-0.3B on an unknown dataset. It achieves the following results on the evaluation set:

eval_loss: 1.4197
eval_runtime: 161.4634
eval_samples_per_second: 85.103
eval_steps_per_second: 5.32
epoch: 0.4705
step: 40000

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 16
eval_batch_size: 16
seed: 1337
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
training_steps: 500000

Framework versions

Transformers 4.56.0
Pytorch 2.8.0+cu129
Datasets 4.8.5
Tokenizers 0.22.0

Downloads last month: 6

Model tree for LanguaMan/vieneu-tts-v2-exp-tuning-model

Base model

pnnbao-ump/VieNeu-TTS-0.3B

Finetuned

(3)

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