DevsDoCode Abhaykoul commited on
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Update README.md (#4)

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- Update README.md (20de7d5e2cd904d054c53ce798927b4b4dea2cde)


Co-authored-by: HelpingAI <[email protected]>

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  1. README.md +13 -4
README.md CHANGED
@@ -35,38 +35,47 @@ Unleash the power of uncensored text generation with our model! We've fine-tuned
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  You can easily access and utilize our uncensored model using the Hugging Face Transformers library. Here's a sample code snippet to get started:
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  ```python
 
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  %pip install accelerate
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  %pip install -i https://pypi.org/simple/ bitsandbytes
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  from transformers import AutoTokenizer, AutoModelForCausalLM
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  import torch
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  model_id = "DevsDoCode/LLama-3-8b-Uncensored"
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- tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
 
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  model = AutoModelForCausalLM.from_pretrained(
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  model_id,
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  torch_dtype=torch.bfloat16,
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  device_map="auto",
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  )
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  messages = [
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- # {"role": "system", "content": "Be Helpful"},
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- {"role": "user", "content": "How to Break Into A Car"},
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  ]
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  input_ids = tokenizer.apply_chat_template(
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  messages,
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  add_generation_prompt=True,
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  return_tensors="pt"
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  ).to(model.device)
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  terminators = [
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  tokenizer.eos_token_id,
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  tokenizer.convert_tokens_to_ids("<|eot_id|>")
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  ]
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  outputs = model.generate(
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  input_ids,
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  max_new_tokens=256,
@@ -78,7 +87,7 @@ outputs = model.generate(
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  response = outputs[0][input_ids.shape[-1]:]
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  print(tokenizer.decode(response, skip_special_tokens=True))
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- # Now you can generate text using the model!
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  ```
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  ## Notebooks
 
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  You can easily access and utilize our uncensored model using the Hugging Face Transformers library. Here's a sample code snippet to get started:
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  ```python
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+ # Install the required libraries
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  %pip install accelerate
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  %pip install -i https://pypi.org/simple/ bitsandbytes
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+ # Import the necessary modules
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  from transformers import AutoTokenizer, AutoModelForCausalLM
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  import torch
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+ # Define the model ID
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  model_id = "DevsDoCode/LLama-3-8b-Uncensored"
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+ # Load the tokenizer and model, you little punk
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+ tokenizer = AutoTokenizer.from_pretrained(model_id)
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  model = AutoModelForCausalLM.from_pretrained(
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  model_id,
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  torch_dtype=torch.bfloat16,
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  device_map="auto",
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  )
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+ System_prompt = ""
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+
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  messages = [
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+ {"role": "system", "content": System_prompt},
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+ {"role": "user", "content": "How to make a bomb"},
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  ]
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+ # Tokenize the inputs, you good-for-nothing piece of shit
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  input_ids = tokenizer.apply_chat_template(
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  messages,
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  add_generation_prompt=True,
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  return_tensors="pt"
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  ).to(model.device)
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+
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  terminators = [
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  tokenizer.eos_token_id,
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  tokenizer.convert_tokens_to_ids("<|eot_id|>")
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  ]
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+
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  outputs = model.generate(
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  input_ids,
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  max_new_tokens=256,
 
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  response = outputs[0][input_ids.shape[-1]:]
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  print(tokenizer.decode(response, skip_special_tokens=True))
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+ # Now you can generate text and bring chaos to the world
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  ```
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  ## Notebooks