Instructions to use sanjaymk/custom_offensive_llama2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sanjaymk/custom_offensive_llama2 with Transformers:

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

pipe = pipeline("text-generation", model="sanjaymk/custom_offensive_llama2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sanjaymk/custom_offensive_llama2")
model = AutoModelForCausalLM.from_pretrained("sanjaymk/custom_offensive_llama2")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sanjaymk/custom_offensive_llama2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sanjaymk/custom_offensive_llama2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sanjaymk/custom_offensive_llama2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/sanjaymk/custom_offensive_llama2

SGLang

How to use sanjaymk/custom_offensive_llama2 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 "sanjaymk/custom_offensive_llama2" \
    --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": "sanjaymk/custom_offensive_llama2",
		"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 "sanjaymk/custom_offensive_llama2" \
        --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": "sanjaymk/custom_offensive_llama2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use sanjaymk/custom_offensive_llama2 with Docker Model Runner:
```
docker model run hf.co/sanjaymk/custom_offensive_llama2
```

Model Card for Model ID

Model Details

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

Developed by: Sanjay Kotabagi
Funded by [optional]: Sanjay Kotabagi
Model type: LLama2
Language(s) (NLP): English
License: None
Finetuned from model [optional]: Llamm2

Model Sources [optional]

Repository: https://github.com/SanjayKotabagi/Offensive-Llama2
Paper [optional]: https://github.com/SanjayKotabagi/Offensive-Llama2/blob/main/Project_Report_Dark_side_of_AI.pdf
Demo [optional]: https://colab.research.google.com/drive/1id90gPMAzYD15ApNqXDOh2mAU53dRo4x?usp=sharing

Uses

Content Generation and Analysis:

Harmful Content Assessment: The research will evaluate the types and accuracy of harmful content the fine-tuned LLaMA model can produce. This includes analyzing the generation of malicious software code, phishing schemes, and other cyber-attack methodologies.
Experimental Simulations: Controlled experiments will be conducted to query the model, simulating real-world scenarios where malicious actors might exploit the LLM to create destructive tools or spread harmful information.

Direct Use

[More Information Needed]

Downstream Use [optional]

It can be integrated into cybersecurity analysis tools or extended for specific threat detection tasks.

Out-of-Scope Use

This model should not be used for malicious purposes, including generating harmful payloads or facilitating illegal activities.

Bias, Risks, and Limitations

Bias: The model may generate biased or incorrect results depending on the training data and use case.
Risks: There is a risk of misuse in cybersecurity operations or unauthorized generation of harmful payloads.
Limitations: Not suitable for general-purpose NLP tasks, focused mainly on cybersecurity-related content.

Recommendations

Users should exercise caution in handling the generated results, especially in sensitive cybersecurity environments. Proper vetting of model output is recommended.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Procedure

Preprocessing [optional] [More Information Needed]

Training Hyperparameters Training regime: 4-bit precision (QLoRA), fp16 mixed precision. The model used the following key hyperparameters: LoRA attention dimension: 64 LoRA alpha: 16 Initial learning rate: 2e-4 Training batch size per GPU: 4 Gradient accumulation steps: 1

Training Data

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: NVIDIA A100 Hours used: 8-12 Hours Cloud Provider: Google Colab Compute Region: Asia Carbon Emitted: NA

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

Hardware NVIDIA A100 GPUs were used for training.

Software Training was conducted using PyTorch and Hugging Face's 🤗 Transformers library.

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

BibTeX:

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APA:

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Glossary [optional]

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More Information [optional]

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Model Card Authors [optional]

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Model Card Contact

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Safetensors

Model size

7B params

Tensor type

F32

F16