Instructions to use yigagilbert/stepaudio2-mini-luganda-english-bidirectional-s2st-full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yigagilbert/stepaudio2-mini-luganda-english-bidirectional-s2st-full with Transformers:

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

pipe = pipeline("text-generation", model="yigagilbert/stepaudio2-mini-luganda-english-bidirectional-s2st-full", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("yigagilbert/stepaudio2-mini-luganda-english-bidirectional-s2st-full", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yigagilbert/stepaudio2-mini-luganda-english-bidirectional-s2st-full with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yigagilbert/stepaudio2-mini-luganda-english-bidirectional-s2st-full"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yigagilbert/stepaudio2-mini-luganda-english-bidirectional-s2st-full",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/yigagilbert/stepaudio2-mini-luganda-english-bidirectional-s2st-full

SGLang

How to use yigagilbert/stepaudio2-mini-luganda-english-bidirectional-s2st-full 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 "yigagilbert/stepaudio2-mini-luganda-english-bidirectional-s2st-full" \
    --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": "yigagilbert/stepaudio2-mini-luganda-english-bidirectional-s2st-full",
		"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 "yigagilbert/stepaudio2-mini-luganda-english-bidirectional-s2st-full" \
        --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": "yigagilbert/stepaudio2-mini-luganda-english-bidirectional-s2st-full",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use yigagilbert/stepaudio2-mini-luganda-english-bidirectional-s2st-full with Docker Model Runner:
```
docker model run hf.co/yigagilbert/stepaudio2-mini-luganda-english-bidirectional-s2st-full
```

stepaudio2-mini-luganda-english-bidirectional-s2st-full / special_tokens_map.json

yigagilbert

Upload merged bidirectional Step-Audio-2-mini Luganda-English model

8eb4145 verified 4 months ago

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	{
	"additional_special_tokens": [
	"<\|EOT\|>",
	"<\|BOT\|>",
	"<\|CALL_START\|>",
	"<\|CALL_END\|>",
	"<\|THINK_START\|>",
	"<\|THINK_END\|>",
	"<\|IMG_START\|>",
	"<\|IMG_END\|>",
	"<\|META_START\|>",
	"<\|META_END\|>",
	"<im_patch>",
	"<im_start>",
	"<im_end>",
	"<dream>",
	"<dream_start>",
	"<dream_end>",
	"<\|MASK_1e69f\|>",
	"<\|UNMASK_1e69f\|>",
	"<video_start>",
	"<video_end>",
	"<patch_start>",
	"<patch_end>",
	"<patch_newline>",
	"<audio_start>",
	"<audio_end>",
	"<audio_patch>",
	"<audio_patch_pad>",
	"<\|SC\|>",
	"<tts_start>",
	"<tts_end>",
	"<tts_pad>"
	],
	"eos_token": {
	"content": "<\|EOT\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false
	},
	"pad_token": {
	"content": "<\|endoftext\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false
	}
	}