Instructions to use Pankaj001/malicious-artifact with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Pankaj001/malicious-artifact with Diffusers:

pip install -U diffusers transformers accelerate

import torch
from diffusers import DiffusionPipeline

# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("Pankaj001/malicious-artifact", dtype=torch.bfloat16, device_map="cuda")

prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Pankaj001/malicious-artifact with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Pankaj001/malicious-artifact
# Run inference directly in the terminal:
llama cli -hf Pankaj001/malicious-artifact

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Pankaj001/malicious-artifact
# Run inference directly in the terminal:
llama cli -hf Pankaj001/malicious-artifact

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Pankaj001/malicious-artifact
# Run inference directly in the terminal:
./llama-cli -hf Pankaj001/malicious-artifact

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Pankaj001/malicious-artifact
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Pankaj001/malicious-artifact

Use Docker

docker model run hf.co/Pankaj001/malicious-artifact

LM Studio
Jan
Ollama
How to use Pankaj001/malicious-artifact with Ollama:
```
ollama run hf.co/Pankaj001/malicious-artifact
```
Unsloth Desktop
Docker Model Runner
How to use Pankaj001/malicious-artifact with Docker Model Runner:
```
docker model run hf.co/Pankaj001/malicious-artifact
```

Lemonade

How to use Pankaj001/malicious-artifact with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Pankaj001/malicious-artifact

Run and chat with the model

lemonade run user.malicious-artifact-{{QUANT_TAG}}

List all available models

lemonade list

Atomic Chat

malicious-artifact / tensorflow_model_test /malicious_model_lambda.h5

Pankaj001

Upload 15 files

b867cf8 verified 12 months ago

History Blame Contribute Delete

50.5 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:: 17f29fc82ca3ddfa6e7deb79a747ab2d4b2abdec615ada1f93e4d6f2f83c844e
Size of remote file:: 50.5 kB
SHA256:: 7229f01976b4f6d4c192982cf8c4284b0e939ddeab37009ed7d73504eaea977c

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