Instructions to use wago5090/mixstyle_multi-s with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wago5090/mixstyle_multi-s with Transformers:

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

pipe = pipeline("text-generation", model="wago5090/mixstyle_multi-s")

# Load model directly
from transformers import AutoProcessor, AutoModelForSeq2SeqLM

processor = AutoProcessor.from_pretrained("wago5090/mixstyle_multi-s")
model = AutoModelForSeq2SeqLM.from_pretrained("wago5090/mixstyle_multi-s")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use wago5090/mixstyle_multi-s with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wago5090/mixstyle_multi-s"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wago5090/mixstyle_multi-s",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/wago5090/mixstyle_multi-s

SGLang

How to use wago5090/mixstyle_multi-s 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 "wago5090/mixstyle_multi-s" \
    --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": "wago5090/mixstyle_multi-s",
		"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 "wago5090/mixstyle_multi-s" \
        --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": "wago5090/mixstyle_multi-s",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use wago5090/mixstyle_multi-s with Docker Model Runner:
```
docker model run hf.co/wago5090/mixstyle_multi-s
```

mixstyle_multi-s

This model is a fine-tuned version of openai/whisper-small on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.4159
Cer: 105.1323

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: 32
eval_batch_size: 32
seed: 42
optimizer: Use OptimizerNames.ADAFACTOR and the args are: No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 912
training_steps: 9129
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Cer
0.4009	0.9997	3043	0.5068	102.8680
0.292	1.9993	6086	0.4280	108.6212
0.2075	2.9990	9129	0.4159	105.1323

Framework versions

Transformers 4.47.1
Pytorch 2.8.0+cu128
Datasets 2.21.0
Tokenizers 0.21.4

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