Instructions to use TensaZangetsu/condensed-bert-vulnerable with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TensaZangetsu/condensed-bert-vulnerable with Transformers:

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

pipe = pipeline("text-generation", model="TensaZangetsu/condensed-bert-vulnerable")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TensaZangetsu/condensed-bert-vulnerable")
model = AutoModelForCausalLM.from_pretrained("TensaZangetsu/condensed-bert-vulnerable", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TensaZangetsu/condensed-bert-vulnerable with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/TensaZangetsu/condensed-bert-vulnerable

SGLang

How to use TensaZangetsu/condensed-bert-vulnerable 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 "TensaZangetsu/condensed-bert-vulnerable" \
    --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": "TensaZangetsu/condensed-bert-vulnerable",
		"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 "TensaZangetsu/condensed-bert-vulnerable" \
        --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": "TensaZangetsu/condensed-bert-vulnerable",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TensaZangetsu/condensed-bert-vulnerable with Docker Model Runner:
```
docker model run hf.co/TensaZangetsu/condensed-bert-vulnerable
```

condensed-bert-vulnerable / training_args.bin

TensaZangetsu

Add initial files

b7318af verified over 2 years ago

History Blame Contribute Delete

4.28 kB

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:: 38e584de46a8d4c964265f0a1f3d3216b9b332a623847d8561651dfd402585ca
Size of remote file:: 4.28 kB
SHA256:: 7f88470d507395b53d0175a0485face0a74bbc3f3215859a2cc9f5cb8dbd94ad

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

TensaZangetsu
/

condensed-bert-vulnerable

Detected Pickle imports (8)

Xet Pointer Details