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

File size: 1,067 Bytes

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{
  "total_samples": 35,
  "parsed_samples": 35,
  "unparsed_samples": 0,
  "accuracy": 0.8571428571428571,
  "micro_f1": 0.8571428571428571,
  "precision_yes": 0.7333333333333333,
  "recall_yes": 0.9166666666666666,
  "f1_yes": 0.8148148148148148,
  "precision_no": 0.95,
  "recall_no": 0.8260869565217391,
  "f1_no": 0.8837209302325583,
  "macro_f1": 0.8492678725236865,
  "f1_avg": 0.8492678725236865,
  "weighted_f1": 0.8600959763750463,
  "tp": 11,
  "tn": 19,
  "fp": 4,
  "fn": 1,
  "dataset_jsonl": "/public/home/202420164005/code/woz/data/step_audio2_depression_lora_answer_first/dev.jsonl",
  "adapter_path": "/public/home/202420164005/code/woz/outputs/step_audio2_depression_lora_answer_first_dev25aug_max8000/answer100_from_ckpt650_plus2epochs/checkpoint-130",
  "model": "/public/home/202420164005/model/stepfun-ai/Step-Audio2-mini-Think",
  "result_path": "/public/home/202420164005/code/woz/outputs/step_audio2_depression_lora_answer_first_dev25aug_max8000/answer100_from_ckpt650_plus2epochs/eval_on_orig35_max10/checkpoint-130/dev_predictions.jsonl"
}