Welcome to an exciting journey into the world of the 33B model now in beta with WhiteRabbitNeo! In this article, we will guide you through accessing and utilizing this powerful artificial intelligence model while keeping in mind its usage restrictions and the responsibilities that come with it.

Getting Started: Accessing the 33B Model

To access the 33B model, simply visit the following link:

• Access the 33B model here

Make sure to join our lively Discord community for real-time insights and support:

• Join our Discord Server

Understanding Usage Restrictions

While the 33B model offers tremendous potential, it’s crucial to adhere to its usage guidelines. Here are some important restrictions:

• No military use or harm towards minors.
• Do not disseminate false information or inappropriate content.
• Respect the privacy of others; avoid generating personal identifiable information without authorization.
• No automated decision-making that negatively impacts individuals.
• Avoid exploitation of vulnerable groups based on age or social characteristics.

Terms of Use

By using the model, you acknowledge and accept the responsibility for its outcomes. It’s vital to understand that:

• You indemnify the creators against any potential claims or damages arising from your use.
• The model is provided “as is,” with no guarantees on performance or security.
• Using the model is at your own risk; there is a possibility of system damage or data loss.

Code Walkthrough: How It Works

Now, let’s imagine leveraging the 33B model’s capabilities through a hands-on coding analogy. Think of the process as baking a cake – you have ingredients (data), a recipe (code), and an oven (the model) that brings everything together to create a delicious outcome (results).

Here’s a simplified version of the code you will use:

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")
    ...

In this analogy:

• Ingredients: The model_path specifies where your baking ingredients are stored.
• Recipe: You mix your tokens like mixing ingredients, preparing them for the oven.
• Oven: The model processes the tokens and bakes them into outputs (the final cake!).

Troubleshooting Common Issues

While using the 33B model, you may encounter challenges. Here are some troubleshooting tips:

• Ensure correct installation of all required libraries, such as PyTorch and Transformers.
• If you face any hardware compatibility issues, make sure your GPU drivers are up to date.
• For inference speed concerns, ensure the model is loaded with the correct configurations (like load_in_8bit=True).
• If you experience unexpected outputs, revisit your input syntax and ensure that the instruction is clear.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

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.

Now you are ready to explore the depths of the 33B model while adhering to its essential guidelines. Happy coding!