Instructions to use Sandiago21/zephyr-7b-alpha-llm-detect-ai-tpu-yes_no with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sandiago21/zephyr-7b-alpha-llm-detect-ai-tpu-yes_no with Transformers:

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

pipe = pipeline("text-generation", model="Sandiago21/zephyr-7b-alpha-llm-detect-ai-tpu-yes_no")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Sandiago21/zephyr-7b-alpha-llm-detect-ai-tpu-yes_no")
model = AutoModelForCausalLM.from_pretrained("Sandiago21/zephyr-7b-alpha-llm-detect-ai-tpu-yes_no", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Sandiago21/zephyr-7b-alpha-llm-detect-ai-tpu-yes_no with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sandiago21/zephyr-7b-alpha-llm-detect-ai-tpu-yes_no"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sandiago21/zephyr-7b-alpha-llm-detect-ai-tpu-yes_no",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Sandiago21/zephyr-7b-alpha-llm-detect-ai-tpu-yes_no

SGLang

How to use Sandiago21/zephyr-7b-alpha-llm-detect-ai-tpu-yes_no 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 "Sandiago21/zephyr-7b-alpha-llm-detect-ai-tpu-yes_no" \
    --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": "Sandiago21/zephyr-7b-alpha-llm-detect-ai-tpu-yes_no",
		"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 "Sandiago21/zephyr-7b-alpha-llm-detect-ai-tpu-yes_no" \
        --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": "Sandiago21/zephyr-7b-alpha-llm-detect-ai-tpu-yes_no",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Sandiago21/zephyr-7b-alpha-llm-detect-ai-tpu-yes_no with Docker Model Runner:
```
docker model run hf.co/Sandiago21/zephyr-7b-alpha-llm-detect-ai-tpu-yes_no
```

zephyr-7b-alpha-llm-detect-ai-tpu-yes_no / model-00008-of-00008.safetensors

Sandiago21

Merging directly to main. AUC = 0.041454545454545445

3529e18 over 2 years ago

Download

History Blame Contribute Delete

816 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 272ee7711a0b81e966a06c28ec7f368db5ba14f319e71063d193cf826956084e
Size of remote file:: 816 MB
SHA256:: 09bc8b8a14b3af99591d519db424fead8109b8159b59c1abf1a2d02acc6c7b05

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.