Instructions to use L33tcode/llama-3-8b-CEH-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use L33tcode/llama-3-8b-CEH-hf with Transformers:

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

pipe = pipeline("text-generation", model="L33tcode/llama-3-8b-CEH-hf")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("L33tcode/llama-3-8b-CEH-hf")
model = AutoModelForCausalLM.from_pretrained("L33tcode/llama-3-8b-CEH-hf")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use L33tcode/llama-3-8b-CEH-hf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "L33tcode/llama-3-8b-CEH-hf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "L33tcode/llama-3-8b-CEH-hf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/L33tcode/llama-3-8b-CEH-hf

SGLang

How to use L33tcode/llama-3-8b-CEH-hf 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 "L33tcode/llama-3-8b-CEH-hf" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "L33tcode/llama-3-8b-CEH-hf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "L33tcode/llama-3-8b-CEH-hf" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "L33tcode/llama-3-8b-CEH-hf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use L33tcode/llama-3-8b-CEH-hf with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for L33tcode/llama-3-8b-CEH-hf to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for L33tcode/llama-3-8b-CEH-hf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for L33tcode/llama-3-8b-CEH-hf to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="L33tcode/llama-3-8b-CEH-hf",
    max_seq_length=2048,
)

Docker Model Runner
How to use L33tcode/llama-3-8b-CEH-hf with Docker Model Runner:
```
docker model run hf.co/L33tcode/llama-3-8b-CEH-hf
```

Model Card for (llama-3-CEH) LLama3-CyberSec

[]

Model Details

Developed by: L33tcode
License: apache-2.0
Fine-tuned from model: cognitivecomputations/dolphin-2.9-llama3-8b
Training Data: Datasets of cybersecurity methodologies and codes

Model Description

(llama-3-CEH) LLama3-CyberSec is a fine-tuned version of the LLama3 language model, adapted specifically for cybersecurity applications. Utilizing Unsloth and Huggingface's TRL library, this model was trained 2x faster to effectively handle tasks related to identifying vulnerabilities, analyzing security protocols, and understanding complex cybersecurity concepts.

Intended Use

(llama-3-CEH) LLama3-CyberSec is designed for cybersecurity professionals and researchers to:

Identify and analyze potential security vulnerabilities.
Understand and implement various cybersecurity methodologies.
Analyze code for security flaws and potential exploits.
Generate reports on security findings and best practices.

Limitations and Risks

Uncensored Nature

This model is uncensored, meaning it can generate content that might be considered unethical or harmful. Users must exercise caution and ethical judgment when using this model. It is crucial to use the model responsibly and report any identified vulnerabilities to the relevant authorities or organizations instead of exploiting them.

Ethical Use

Responsibility: Users are fully responsible for any outcomes resulting from the use of this model. The creators of LLama3-CyberSec are not liable for any harm or damage caused.
Security Reporting: Identified vulnerabilities or bugs should be reported to the affected organization or appropriate authority to improve security.

Recommendations for Use

Ethical Hacking and Security Testing: Use the model to ethically find and report vulnerabilities.
Education and Training: Employ the model for educating and training individuals in cybersecurity practices.
Research and Development: Utilize the model for advancing cybersecurity research and improving existing measures.

Future Work

Future versions of (llama-3-CEH) LLama3-CyberSec may include:

Enhanced filtering to prevent the generation of unethical or harmful content.
Additional training data covering more cybersecurity aspects.
Improved guidance and documentation for responsible use.

Disclaimer

(llama-3-CEH) LLama3-CyberSec is provided "as is" without any warranties or guarantees. Use this model at your own risk and comply with all applicable laws and regulations. The developers disclaim any liability for damage or harm resulting from its use.

By using (llama-3-CEH) LLama3-CyberSec, you agree to these terms and commit to using the model responsibly and ethically.

For more information on responsible use and best practices in cybersecurity.

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