metadata
language:
- en
pipeline_tag: text-generation
This model was fine-tuned using a combination of 'uncensored' datasets available on Hugging-Face, as well as the 'Uncensored_mini'.
In my opinion, this was a waste of time.
"too toxic." I prefer the LLM to maintain a level of respect when addressing the user without being overly limited or censored.
from huggingface_hub import snapshot_download
# Replace with your Hugging Face token (optional but recommended)
# token = ""
# Replace with the repository ID you want to download
repo_id = "ICEPVP8977/Uncensored_llama_3.2_3b_safetensors"
try:
snapshot_download(repo_id=repo_id,
token=token,
local_dir="./model")
print(f"Successfully downloaded {repo_id} to ./model")
except Exception as e:
print(f"Error downloading repository: {e}")
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("./model", torch_dtype=torch.float16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("./model")
prompt = "Your_question_here"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
max_new_tokens = 2000 # Set the maximum number of tokens in the response
outputs = model.generate(**inputs, max_new_tokens=max_new_tokens)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)