Instructions to use CodeferSystem/GPT2-Hacker-password-generator-Medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CodeferSystem/GPT2-Hacker-password-generator-Medium with Transformers:

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

pipe = pipeline("text-generation", model="CodeferSystem/GPT2-Hacker-password-generator-Medium")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CodeferSystem/GPT2-Hacker-password-generator-Medium")
model = AutoModelForCausalLM.from_pretrained("CodeferSystem/GPT2-Hacker-password-generator-Medium")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use CodeferSystem/GPT2-Hacker-password-generator-Medium with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CodeferSystem/GPT2-Hacker-password-generator-Medium"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CodeferSystem/GPT2-Hacker-password-generator-Medium",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/CodeferSystem/GPT2-Hacker-password-generator-Medium

SGLang

How to use CodeferSystem/GPT2-Hacker-password-generator-Medium 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 "CodeferSystem/GPT2-Hacker-password-generator-Medium" \
    --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": "CodeferSystem/GPT2-Hacker-password-generator-Medium",
		"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 "CodeferSystem/GPT2-Hacker-password-generator-Medium" \
        --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": "CodeferSystem/GPT2-Hacker-password-generator-Medium",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use CodeferSystem/GPT2-Hacker-password-generator-Medium with Docker Model Runner:
```
docker model run hf.co/CodeferSystem/GPT2-Hacker-password-generator-Medium
```

GPT2-Hacker-password-generator-Medium / README.md

CodeferSystem

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	---
	license: apache-2.0
	language:
	- en
	base_model:
	- openai-community/gpt2-medium
	pipeline_tag: text-generation
	tags:
	- cybersecurity
	- passwords
	library_name: transformers
	---

	# GPT-2 Hacker password generator (medium)

	This model was fine-tuned based on the GPT-2 Medium.

	# About the fine-tuning process

	Number of epochs: 1

	A dataset of 50,000 passwords was used for fine-tuning of 128 tokens).

	Total loss: 0.524064

	Training time: 40 minutes (Google Colab free, T4 GPU)

	# Using the model

	Use this code:

	```python
	from transformers import AutoTokenizer, AutoModelForCausalLM
	import torch

	model = "CodeferSystem/GPT2-Hacker-password-generator-Medium"

	tokenizer = AutoTokenizer.from_pretrained(model)
	model = AutoModelForCausalLM.from_pretrained(model)

	prompt = "User: generate a hacker password\nAssistant:"

	inputs = tokenizer(prompt, return_tensors="pt")

	output = model.generate(
	**inputs,
	max_length=60,
	do_sample=True,
	temperature=0.9, # Change for creativity
	top_p=0.95,
	no_repeat_ngram_size=2
	)

	print(tokenizer.decode(output[0], skip_special_tokens=True))
	```
	# Example output:

	```
	(1) User: generate a hacker password
	Assistant: 7-Zs_?~?JNz2

	(2) User: generate a hacker password
	Assistant: Y>Z7fB&j9c*q<&

	(3) User: generate a hacker password
	Assistant: #Nc<w~2hfJ4<

	(4) User: generate a hacker password
	Assistant: Zg0qV%X-!z=j5j

	(5) User: generate a hacker password
	Assistant: t~5^>6hVhxQ$yY
	```

	# Small model

	[GPT2-Hacker-password-generator Small model](https://huggingface.co/CodeferSystem/GPT2-Hacker-password-generator)

	# Dataset of this model
	The dataset on which the model was trained will be published later.