The 33B model has entered the beta phase, and we’re excited to share how to access and utilize this cutting-edge AI for cybersecurity applications. Whether you’re a developer or a cybersecurity enthusiast, this guide will help you navigate the essential components of the WhiteRabbitNeo model, along with some troubleshooting tips.

Accessing the Model

To get started with the WhiteRabbitNeo model:

• Visit the official website to access the model.
• Join our community and discuss your findings on our Discord server.

Understanding the Code

The code provided below illustrates how to implement the model in your own applications. Think of it like an assembly manual for building a smart robot that can help with various tasks, from carrying out conversations to solving intricate problems.

import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "home/migel/models/WhiteRabbitNeo"
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    load_in_8bit=True,
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0).to("cuda")
    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.5,
        "generate_len": 1024,
        "top_k": 50,
    }
    length = len(tokens[0])
    with torch.no_grad():
        output = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
        )
    string = tokenizer.decode(output[0], skip_special_tokens=True)
    answer = string.split("USER:")[0].strip()
    return answer

In this analogy:

• The model_path is like the workshop where you build your robot.
• The generate_text function is the brain of the robot, allowing it to understand inputs and provide useful outputs.
• Tokens are akin to the robot’s sensory responses, interpreting the world around it (the user’s instructions) to produce a coherent answer.

Terms of Use and Legal Considerations

Before diving deeper into the model, it’s crucial to adhere to its Usage Restrictions. The terms explicitly prohibit using the model for malicious or harmful purposes. Remember: with great power comes great responsibility. You must ensure you’re compliant with the laws governing your locality.

Troubleshooting Common Issues

If you run into issues while using the model, here are some troubleshooting tips to help you out:

• Ensure that you have the necessary packages installed and that your environment is set up correctly for PyTorch and Transformers.
• If you encounter memory issues, try adjusting the load_in_8bit parameter or managing the batch sizes carefully.
• Keep an eye on the logs for errors; they can provide vital clues as to what went wrong.
• If you need additional support or wish to collaborate on AI projects, don’t hesitate to reach out through our Discord server or visit fxis.ai. For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

By understanding and properly utilizing the WhiteRabbitNeo model, you can play a crucial role in enhancing cybersecurity measures. Remember to respect the usage restrictions and embrace ethical considerations in your endeavors. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.