README.md

---
license: apache-2.0
language:
- en
tags:
- code
- cybersecurity
- penetration testing
- hacking
- code
- uncensored
datasets:
- mlabonne/orpo-dpo-mix-40k
---
# Prox-Llama-3-8B-abliterated-orpo

By [OpenVoid](https://openvoid.ai)

<img src="https://cdn.openvoid.ai/images/prox-llama3.png" width="400" />

## Model Description

Prox-Llama-3-8B is a uncensored fine-tune of Meta-Llama-3-8B-Instruct, tailored for specialized applications in code generation and cybersecurity.

## Intended Uses & Limitations

Designed for tasks related to hacking and coding:

- Code generation
- Code explanation and documentation
- Answering questions on hacking techniques and cybersecurity
- Providing coding project insights

Review and verify outputs carefully, especially for critical applications. Expert validation is recommended to avoid biased or inconsistent content. Use responsibly and ethically, complying with applicable laws and regulations to prevent misuse for malicious purposes.

## Training Data

The model was fine-tuned on a proprietary dataset from OpenVoid, featuring high-quality text data related to coding, cybersecurity, and hacking. Extensive filtering and preprocessing ensured data quality and relevance.

## Evaluation

- **HumanEval v1.0**: pass@1: 0.537
- **EvalPlus v1.1**: pass@1: 0.482

## How to Use the Model

### Using Transformers

Example of using Prox-Llama-3-8B with the Transformers library:

```python
import transformers
import torch

model_id = "openvoid/Prox-Llama-3-8B-abliterated-orpo"

pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are Prox."},
    {"role": "user", "content": "Who are you?"},
]

terminators = [
    pipeline.tokenizer.eos_token_id,
    pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

outputs = pipeline(
    messages,
    max_new_tokens=256,
    eos_token_id=terminators,
    do_sample=True,
    temperature=0.6,
    top_p=0.9,
)
print(outputs[0]["generated_text"][-1])
```