Instructions to use Pavithrapn-01/sentiment-analyzer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Pavithrapn-01/sentiment-analyzer with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("google/gemma-2b")
model = PeftModel.from_pretrained(base_model, "Pavithrapn-01/sentiment-analyzer")

Transformers

How to use Pavithrapn-01/sentiment-analyzer with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Pavithrapn-01/sentiment-analyzer")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Pavithrapn-01/sentiment-analyzer", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Pavithrapn-01/sentiment-analyzer with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Pavithrapn-01/sentiment-analyzer"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Pavithrapn-01/sentiment-analyzer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Pavithrapn-01/sentiment-analyzer

SGLang

How to use Pavithrapn-01/sentiment-analyzer with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Pavithrapn-01/sentiment-analyzer" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Pavithrapn-01/sentiment-analyzer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Pavithrapn-01/sentiment-analyzer" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Pavithrapn-01/sentiment-analyzer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Pavithrapn-01/sentiment-analyzer with Docker Model Runner:
```
docker model run hf.co/Pavithrapn-01/sentiment-analyzer
```

Sentiment Analyzer

A fine-tuned sentiment analysis model developed and shared by Pavithrapn-01.
This model is designed to analyze text and classify sentiment efficiently using a lightweight fine-tuning approach.

Model Details

Model Description

This model is a sentiment analysis system built by fine-tuning the google/gemma-2b base model using LoRA (Low-Rank Adaptation).
It is optimized for understanding emotional polarity in text such as positive, negative, or neutral sentiment.

Developed by: Pavithra PN
Shared by: Pavithrapn-01
Model type: Text Generation / Sentiment Analysis
Language(s): English
License: Open-source (same as base model)
Finetuned from model: google/gemma-2b

Model Sources

Repository: Pavithrapn-01/sentiment-analyzer
Base Model: google/gemma-2b

Uses

Direct Use

Sentiment analysis of user reviews
Opinion mining from social media text
Feedback and survey analysis
Educational and academic projects

Downstream Use

Can be integrated into chatbots
Can be used in recommendation systems
Can be further fine-tuned for domain-specific sentiment tasks

Out-of-Scope Use

Medical or legal decision-making
High-risk or safety-critical applications
Multilingual sentiment analysis (English only)

Bias, Risks, and Limitations

The model may reflect biases present in the training data
Performance may vary on slang, sarcasm, or ambiguous text
Best suited for short to medium-length text inputs

Recommendations

Users should validate outputs before deploying the model in real-world applications and avoid using it for sensitive decision-making.

How to Get Started with the Model

from transformers import pipeline

classifier = pipeline("sentiment-analysis", model="Pavithrapn-01/sentiment-analyzer")
result = classifier("I really enjoyed using this application!")
print(result)

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