sentiment-analyzer

Gemma-2B LoRA Fine-Tuned Model

This repository contains LoRA adapter weights fine-tuned on top of the google/gemma-2b base model.
The model is optimized for lightweight deployment and efficient fine-tuning using PEFT / QLoRA, making it suitable for low-resource environments.

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Model Details

Model Description

• Base model: google/gemma-2b
• Fine-tuning method: LoRA (Parameter-Efficient Fine-Tuning)
• Library: PEFT + Hugging Face Transformers
• Pipeline type: Text Generation
• Language: English
• Model format: LoRA adapter (not merged)

This repository does not contain full Gemma weights. It provides adapter layers that must be loaded on top of the base Gemma-2B model.

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Model Type

• Causal Language Model (Decoder-only Transformer)

License

• Same as base model: Gemma License
  (Users must comply with Google Gemma usage terms)

Finetuned From

• google/gemma-2b

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Intended Uses

Direct Use

This model can be used for:

• Domain-specific text generation
• Instruction-following tasks (depending on dataset)
• Prototyping GenAI applications with limited compute

Downstream Use

• Can be further fine-tuned with additional LoRA adapters
• Can be merged with base model for inference if required
• Suitable for chatbots, assistants, or internal tools

Out-of-Scope Use

• High-risk domains (medical, legal, financial advice)
• Fully autonomous decision-making systems
• Tasks requiring multilingual or long-context reasoning beyond base model limits

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Bias, Risks, and Limitations

• Inherits biases from the base Gemma-2B model and training data
• Performance is highly dependent on the fine-tuning dataset
• Not evaluated for safety-critical applications
• LoRA adapters may underperform compared to full fine-tuning

Recommendations

• Validate outputs before production use
• Add guardrails for sensitive or user-facing deployments
• Perform task-specific evaluation before downstream use

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How to Get Started

Loading the Model

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = "google/gemma-2b"
adapter_repo = "Kavi11/gemma-2b-lora"  # update if needed

tokenizer = AutoTokenizer.from_pretrained(base_model)

model = AutoModelForCausalLM.from_pretrained(
    base_model,
    device_map="auto"
)

model = PeftModel.from_pretrained(model, adapter_repo)