Instructions to use maimai11/woz with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use maimai11/woz with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("/public/home/202420164005/model/stepfun-ai/Step-Audio2-mini-Think")
model = PeftModel.from_pretrained(base_model, "maimai11/woz")

Transformers

How to use maimai11/woz with Transformers:

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

pipe = pipeline("text-generation", model="maimai11/woz")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("maimai11/woz", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use maimai11/woz with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "maimai11/woz"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maimai11/woz",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/maimai11/woz

SGLang

How to use maimai11/woz 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 "maimai11/woz" \
    --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": "maimai11/woz",
		"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 "maimai11/woz" \
        --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": "maimai11/woz",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use maimai11/woz with Docker Model Runner:
```
docker model run hf.co/maimai11/woz
```

woz / README.md

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	---
	library_name: peft
	pipeline_tag: text-generation
	tags:
	- lora
	- peft
	- transformers
	- audio-text
	- depression-detection
	---

	# woz checkpoint-130 LoRA adapter

	This repository contains the LoRA adapter exported from the best `checkpoint-130`
	used in the `Step-Audio2 DAIC-WOZ Depression` experiments.

	## Contents

	- `adapter_model.safetensors`
	- `adapter_config.json`
	- `additional_config.json`
	- `args.json`
	- `dev_metrics.json`

	## Intended base model

	This adapter is intended to be loaded on top of:

	- `Step-Audio2-mini-Think`

	The base model is not redistributed in this repository.

	## Evaluation snapshot

	The included `dev_metrics.json` reports the following result on the project dev
	split used during this experiment:

	- accuracy: `0.8571`
	- weighted_f1: `0.8601`
	- macro_f1: `0.8493`
	- f1_yes: `0.8148`
	- tp: `11`
	- tn: `19`
	- fp: `4`
	- fn: `1`

	## Notes

	- This repository only packages the inference-relevant adapter artifacts.
	- Optimizer state and other training-only checkpoint files are intentionally
	excluded.