Instructions to use abhiseksanyal/testing-malicious-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abhiseksanyal/testing-malicious-models with Transformers:

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

pipe = pipeline("image-text-to-text", model="abhiseksanyal/testing-malicious-models")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("abhiseksanyal/testing-malicious-models")
model = AutoModelForCausalLM.from_pretrained("abhiseksanyal/testing-malicious-models", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use abhiseksanyal/testing-malicious-models with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/abhiseksanyal/testing-malicious-models

SGLang

How to use abhiseksanyal/testing-malicious-models 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 "abhiseksanyal/testing-malicious-models" \
    --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": "abhiseksanyal/testing-malicious-models",
		"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 "abhiseksanyal/testing-malicious-models" \
        --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": "abhiseksanyal/testing-malicious-models",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use abhiseksanyal/testing-malicious-models with Docker Model Runner:
```
docker model run hf.co/abhiseksanyal/testing-malicious-models
```

testing-malicious-models / README.md

abhiseksanyal

Duplicate from ank13/testing-malicious-models

834279a 6 months ago

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metadata

license: mit
library_name: transformers
pipeline_tag: image-text-to-text
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: >-
  To access Gemma on Hugging Face, you’re required to review and agree to
  Google’s usage license. To do this, please ensure you’re logged in to Hugging
  Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
base_model: Qwen/Qwen2.5-Math-7B
tags:
  - automatic-speech-recognition
  - automatic-speech-translation
  - audio-text-to-text
  - video-text-to-text

Duplicated from ank13/testing-malicious-models

abhiseksanyal
/

testing-malicious-models

This is sample README.md file