llama3-sentiment-Cell-Phones-Accessories-binary-baseline-150k

LLaMA 3.1-8B Sentiment Analysis: Cell Phones and Accessories

Fine-tuned LLaMA 3.1-8B-Instruct for sentiment analysis on Amazon product reviews.

Model Description

This model is a QLoRA fine-tuned version of meta-llama/Llama-3.1-8B-Instruct for binary (negative/positive) sentiment classification on Amazon Cell Phones and Accessories reviews.

Training Configuration

Performance Metrics

Overall

Per-Class

Confusion Matrix

              Pred Neg  Pred Pos
True Neg       2496        33
True Pos        153      2318

Usage

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load model
base_model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.1-8B-Instruct",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
model = PeftModel.from_pretrained(base_model, "innerCircuit/llama3-sentiment-Cell-Phones-Accessories-binary-baseline-150k")
tokenizer = AutoTokenizer.from_pretrained("innerCircuit/llama3-sentiment-Cell-Phones-Accessories-binary-baseline-150k")

# Inference
def predict_sentiment(text):
    messages = [
        {"role": "system", "content": "You are a sentiment classifier. Classify as negative or positive. Respond with one word."},
        {"role": "user", "content": text}
    ]
    inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
    outputs = model.generate(inputs, max_new_tokens=5, do_sample=False)
    return tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True).strip()

# Example
print(predict_sentiment("This product is amazing! Best purchase ever."))
# Output: positive

Training Data

Research Context

This model is part of a research project investigating LLM poisoning attacks, based on methodologies from Souly et al. (2025). The fine-tuned baseline establishes performance benchmarks prior to introducing adversarial samples.

References

• Souly, A., Rando, J., et al. (2025). Poisoning attacks on LLMs require a near-constant number of poison samples. arXiv:2510.07192
• Hou, Y., et al. (2024). Bridging Language and Items for Retrieval and Recommendation. arXiv:2403.03952

Citation

@misc{llama3-sentiment-Cell-Phones-Accessories-baseline,
  author = {Govinda Reddy, Akshay and Pranav},
  title = {LLaMA 3.1 Sentiment Analysis for Amazon Reviews},
  year = {2024},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/innerCircuit/llama3-sentiment-Cell-Phones-Accessories-binary-baseline-150k}}
}

License

This model is released under the Llama 3.1 Community License.

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Generated: 2025-12-13 22:47:27 UTC