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lemuralabs/Gemma-3-27B-it-Uncensored
Gemma-3-27B-it-Uncensored is a machine learning model from lemuralabs. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as gemma.
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Downloads · 30 days
40
0% of all-time downloads
All-time downloads
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.safetensors54.9 GB · 100%
From the Hugging Face model README
Welcome to Lemura Labs'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.
| Quantization | Bits per Weight | Ideal For | Link |
|---|---|---|---|
| Q8_0 | 8-bit | Best accuracy and performance | [model-Q8_0.gguf](https://huggingface.co/lemuralabs/Lemura Labs-Gemma-3-27B-Instruct-Uncensored-GGUF/blob/main/model-Q8_0.gguf) |
| Q6_K | 6-bit | Strong accuracy and fast inference | [model-Q6_K.gguf](https://huggingface.co/lemuralabs/Lemura Labs-Gemma-3-27B-Instruct-Uncensored-GGUF/resolve/main/model-Q6_K.gguf) |
| Q5_K_M | 5-bit | Balance between accuracy and speed | [model-Q5_K_M.gguf](https://huggingface.co/lemuralabs/Lemura Labs-Gemma-3-27B-Instruct-Uncensored-GGUF/resolve/main/model-Q5_K_M.gguf) |
| Q3_K_M | 3-bit | Low memory usage, good performance | [model-Q3_K_M.gguf](https://huggingface.co/lemuralabs/Lemura Labs-Gemma-3-27B-Instruct-Uncensored-GGUF/resolve/main/model-Q3_K_M.gguf) |
| TQ2_0 | 2-bit (Tiny) | Maximum speed and minimal resources | [model-TQ2_0.gguf](https://huggingface.co/lemuralabs/Lemura Labs-Gemma-3-27B-Instruct-Uncensored-GGUF/resolve/main/model-TQ2_0.gguf) |
| TQ1_0 | 1-bit (Tiny) | Minimal footprint and fastest inference | [model-TQ1_0.gguf](https://huggingface.co/lemuralabs/Lemura Labs-Gemma-3-27B-Instruct-Uncensored-GGUF/resolve/main/model-TQ1_0.gguf) |
Q8_0 or Q6_K.Q5_K_M.Q3_K_M, TQ2_0, or TQ1_0.from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_name = "lemuralabs/Lemura Labs-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))
Unlock your creativity and innovation potential with Lemura Labs Gemma-3-27B Instruct Uncensored. Experience the freedom to create, explore, and innovate without limits.