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RichardErkhov/explorewithai_-_ChatFrame-Uncensored-Instruct-Small-gguf
explorewithai_-_ChatFrame-Uncensored-Instruct-Small-gguf 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.
Downloads · 30 days
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From the Hugging Face model README
Quantization made by Richard Erkhov.
ChatFrame-Uncensored-Instruct-Small - GGUF
license: mit library_name: transformers language:
🎉 Introducing ChatFrame V1, the game-changing AI model that pushes the boundaries of language modeling!

Model Description: ChatFrame V1 is a groundbreaking AI language model, proudly developed by AIFRAME INC. What sets this model apart is its unique training data, consisting of uncensored texts, questions, and answers. This means that ChatFrame V1 is the first of its kind in the English language, capable of providing unfiltered and unrestricted responses.
Key Features:
Target Audience: ChatFrame V1 is ideal for users seeking an AI companion that provides unfiltered and honest interactions. It can assist content creators, developers, and individuals looking to explore language modeling without restrictions.
With its cutting-edge capabilities and uncensored nature, ChatFrame V1 is set to revolutionize the way we interact with AI, offering a fresh and dynamic perspective on language understanding and generation!
Disclaimer: While ChatFrame V1 provides unrestricted responses, users are advised to utilize the model responsibly and ethically, adhering to legal and moral guidelines. AIFRAME INC promotes the responsible use of AI technology and does not endorse any harmful or illegal activities.
Using with pipline
from transformers import pipeline
import torch
# Determine the device: 0 for GPU, -1 for CPU
device = 0 if torch.cuda.is_available() else -1
# Load the text-generation model pipeline with GPU support if available
pipe = pipeline("text-generation", model="explorewithai/ChatFrame-Uncensored-Instruct-Small", device=device)
# Define the function to generate responses
def generate_response(user_input):
messages = [
{"role": "user", "content": user_input},
]
response = pipe(messages)
# Extract and return only the assistant's response
assistant_response = response[0]['generated_text']
return assistant_response
ai = generate_response(user_input = "Hello")
print(ai)