metadata

license: gemma

🚀 Nidum Gemma-3-27B Instruct Uncensored

Welcome to Nidum's Gemma-3-27B Instruct Uncensored, a powerful and versatile model optimized for unrestricted interactions. Designed for creators, researchers, and AI enthusiasts seeking innovative and boundary-pushing capabilities.

✨ Why Nidum Gemma-3-27B Instruct Uncensored?

Uncensored Interaction: Generate content freely without artificial restrictions.
High Intelligence: Exceptional reasoning and comprehensive conversational capabilities.
Versatile Applications: Perfect for creative writing, educational interactions, research projects, virtual assistance, and more.
Open and Innovative: Tailored for users who appreciate limitless creativity.

🚀 Available GGUF Quantized Models

Quantization	Bits per Weight	Ideal For	Link
Q8_0	8-bit	Best accuracy and performance	model-Q8_0.gguf
Q6_K	6-bit	Strong accuracy and fast inference	model-Q6_K.gguf
Q5_K_M	5-bit	Balance between accuracy and speed	model-Q5_K_M.gguf
Q3_K_M	3-bit	Low memory usage, good performance	model-Q3_K_M.gguf
TQ2_0	2-bit (Tiny)	Maximum speed and minimal resources	model-TQ2_0.gguf
TQ1_0	1-bit (Tiny)	Minimal footprint and fastest inference	model-TQ1_0.gguf

🎯 Recommended Quantization

Best accuracy: Use Q8_0 or Q6_K.
Balanced performance: Use Q5_K_M.
Small footprint (mobile/edge): Choose Q3_K_M, TQ2_0, or TQ1_0.

🚀 Example Usage (Original Model)

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "nidum/nidum-Gemma-3-27B-Instruct-Uncensored"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = "Tell me a futuristic story about space travel."
inputs = tokenizer(prompt, return_tensors="pt")
output = model.generate(**inputs, max_length=200)
print(tokenizer.decode(output[0], skip_special_tokens=True))

🚀 Your AI, Your Way

Unlock your creativity and innovation potential with Nidum Gemma-3-27B Instruct Uncensored. Experience the freedom to create, explore, and innovate without limits.