Humanizer LoRA Adapter

This is a LoRA (Low-Rank Adaptation) adapter for Llama3 8B Instruct that converts formal text into more natural, human-like language.

Model Details

Base Model: meta-llama/Meta-Llama-3-8B-Instruct
Adapter Type: LoRA (Low-Rank Adaptation)
LoRA Rank: 32
LoRA Alpha: 64
Target Modules: {'k_proj', 'gate_proj', 'q_proj', 'v_proj', 'up_proj', 'down_proj', 'o_proj'}
Task: Text humanization - converting formal/academic text to conversational style

Files Included

This adapter includes all necessary files:

adapter_config.json - LoRA configuration
adapter_model.safetensors - LoRA weights
special_tokens_map.json - Special tokens mapping
tokenizer.json - Tokenizer vocabulary
tokenizer_config.json - Tokenizer configuration
training_args.bin - Training arguments

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

# Load base model and tokenizer
base_model = "meta-llama/Meta-Llama-3-8B-Instruct"
model = AutoModelForCausalLM.from_pretrained(
    base_model,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(base_model)

# Load LoRA adapter
adapter_name = "arda24/Humanizer"
model = PeftModel.from_pretrained(model, adapter_name)

# Prepare input
prompt = "### Instruction:
rewrite this text in a natural and human like way

### Input:
The system requires authentication before proceeding.

### Response:
"

# Generate humanized text
inputs = tokenizer(prompt, return_tensors="pt")
if torch.cuda.is_available():
    inputs = {k: v.cuda() for k, v in inputs.items()}

outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.3,
    do_sample=True,
    top_p=0.7,
    repetition_penalty=1.05,
    no_repeat_ngram_size=2
)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
humanized_text = response.split("### Response:")[1].strip()
print(humanized_text)

Example

Input: "The system requires authentication before proceeding."

Output: "You need to log in first before you can access the system."

Training Configuration

LoRA Rank: 32
LoRA Alpha: 64
Learning Rate: 1e-5
Batch Size: 1
Gradient Accumulation Steps: 16
Training Steps: ~4000

Advantages of LoRA

Smaller size: Only ~50MB vs several GB for full model
Faster loading: Loads quickly on top of base model
Flexible: Can be combined with other adapters
Efficient: Uses minimal additional parameters

Limitations

Works best with formal/academic text
May occasionally add citations if not properly controlled
Conservative settings recommended for minimal changes
Not suitable for creative writing or fiction

License

This adapter is based on Llama3 8B Instruct and follows the same license terms.