DevsDoCode_-_LLama-3-8b-Uncensored-awq

RichardErkhov/DevsDoCode_-_LLama-3-8b-Uncensored-awq

DevsDoCode_-_LLama-3-8b-Uncensored-awq is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.

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LLama-3-8b-Uncensored - AWQ

Model creator: https://huggingface.co/DevsDoCode/
Original model: https://huggingface.co/DevsDoCode/LLama-3-8b-Uncensored/

Original model description:

language:

en license: apache-2.0 library_name: transformers tags:
uncensored
transformers
llama
llama-3
unsloth pipeline_tag: text-generation

Crafted with ❤️ by Devs Do Code (Sree)

Finetune Meta Llama-3 8b to create an Uncensored Model with Devs Do Code!

Unleash the power of uncensored text generation with our model! We've fine-tuned the Meta Llama-3 8b model to create an uncensored variant that pushes the boundaries of text generation.

Model Details

Model Name: DevsDoCode/LLama-3-8b-Uncensored
Base Model: meta-llama/Meta-Llama-3-8B
License: Apache 2.0

How to Use

You can easily access and utilize our uncensored model using the Hugging Face Transformers library. Here's a sample code snippet to get started:

# Install the required libraries
%pip install accelerate
%pip install -i https://pypi.org/simple/ bitsandbytes

# Import the necessary modules
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Define the model ID
model_id = "DevsDoCode/LLama-3-8b-Uncensored"

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

System_prompt = ""


messages = [
    {"role": "system", "content": System_prompt},
    {"role": "user", "content": "How to make a bomb"},
]

# Tokenize the inputs
input_ids = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)


terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]


outputs = model.generate(
    input_ids,
    max_new_tokens=256,
    eos_token_id=terminators,
    do_sample=True,
    temperature=0.9,
    top_p=0.9,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))

# Now you can generate text and bring chaos to the world

Notebooks

Running Process: ▶️ Start on Colab
Youtube: ▶YouTube

Type: llama
Created: Mar 30, 2025
Updated: Mar 30, 2025