BIG TIGER GEMMA 27B V1

Introduction

Big Tiger Gemma 27B V1 is a text generation model that has been decensored, showing no refusals except in rare instances. The model is designed to operate without apparent cognitive limitations, producing coherent text across various contexts.

Architecture

The model is built on the Transformers library and supports safetensors for secure model storage. It is compatible with text generation and conversational applications, utilizing advanced inference endpoints for deployment.

Training

The model has undergone extensive training to ensure high-quality text generation capabilities. It is optimized for performance with specific configurations available, such as the GGUF and iMatrix for improved perplexity (PPL).

Guide: Running Locally

Set Up Environment:
- Ensure Python and necessary libraries like Transformers are installed.
- Download the model from its Hugging Face repository.

Load the Model:

from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("TheDrummer/Big-Tiger-Gemma-27B-v1")
tokenizer = AutoTokenizer.from_pretrained("TheDrummer/Big-Tiger-Gemma-27B-v1")

Generate Text:

input_text = "Your input here"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Consider Cloud GPUs:
- For optimal performance, especially with large models like this, using cloud GPU services such as AWS, Google Cloud, or Azure is recommended.

License

The model is available under a specific license provided on its Hugging Face page. Users should review the license terms to ensure compliance with usage guidelines.