Vedant3907/Llama-3.2-1B-PersonaClassifier

Model Description

This model is a fine-tuned version of meta-llama/Llama-3.2-1B optimized for Persona Classifier tasks when given a Detailed Persona. The training was done on argilla/FinePersonas-v0.1 dataset with the 10k records.

• Developed by: Vedant Rajpurohit
• Model type: Causal Language Model
• Language(s): English
• Fine-tuned from model: meta-llama/Llama-3.2-1B

Direct Use

model_id_new = "Vedant3907/Llama-3.2-1B-PersonaClassifier"

tokenzier = AutoTokenizer.from_pretrained(model_id_new)
model_pretrained = AutoModelForCausalLM.from_pretrained(
    model_id_new,
    device_map="auto",
    torch_dtype="float16")

prompt = """Given the persona give the associated labels:
### Persona:
      A social justice activist and blogger focused on anti-colonialism, anti-racism, and media representation, particularly within the context of intersectional people of color experiences.

### Labels:  
"""

pipe = pipeline(task="text-generation",
                        model=model_pretrained,
                        tokenizer=tokenizer,
                        max_new_tokens=50,
                        temperature=0.1,
                        pad_token_id = tokenizer.eos_token_id)

result = pipe(testing_prompt)

print(extract_labels(result[0]['generated_text']))


#The extract_labels function is to print just the lsit of persona generated by model if sometime it generates random things.

'''
import re

def extract_labels(output_text):
    """
    Extracts the list of labels from the generated text.
    Args:
        output_text (str): The raw output text from the model.
    Returns:
        list: A list of labels if found, otherwise an empty list.
    """
    try:
        # Find the content after "Labels:" and extract the list
        match = re.search(r"### Labels:\s*(\[.*?\])", output_text)
        if match:
            labels = eval(match.group(1))  # Convert string representation of list to Python list
            if isinstance(labels, list):
                return labels
    except Exception as e:
        print(f"Error extracting labels: {e}")
    
    # Return an empty list if extraction fails
    return []
'''

Training Details

Training Procedure

The model was fine-tuned using with LoRA adapters, enabling efficient training. Below are the hyperparameters used:

training_arguments = TrainingArguments(
    output_dir=output_dir,                    
    num_train_epochs=3,                       
    per_device_train_batch_size=1,            
    gradient_accumulation_steps=8,            
    optim="paged_adamw_32bit",
    logging_steps=10,
    learning_rate=2e-4,                       
    fp16=True,
    bf16=False,
    max_grad_norm=0.3,                      
    # max_steps=-1,
    warmup_steps=7,                  
    group_by_length=False,
    lr_scheduler_type="cosine",             
    report_to="wandb",
    eval_strategy="steps",
    eval_steps = 0.2
)

Hardware

• Trained on google colab with its T4 GPU