Instructions to use 09Vaarun/sentiment-analyzer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 09Vaarun/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, "09Vaarun/sentiment-analyzer")

Transformers

How to use 09Vaarun/sentiment-analyzer with Transformers:

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

pipe = pipeline("text-generation", model="09Vaarun/sentiment-analyzer")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("09Vaarun/sentiment-analyzer", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use 09Vaarun/sentiment-analyzer with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "09Vaarun/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": "09Vaarun/sentiment-analyzer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/09Vaarun/sentiment-analyzer

SGLang

How to use 09Vaarun/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 "09Vaarun/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": "09Vaarun/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 "09Vaarun/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": "09Vaarun/sentiment-analyzer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use 09Vaarun/sentiment-analyzer with Docker Model Runner:
```
docker model run hf.co/09Vaarun/sentiment-analyzer
```

Sentiment Analyzer (LoRA Fine-tuned Gemma-2B)

Model Summary

This repository contains a Sentiment Analysis model fine-tuned using LoRA (Low-Rank Adaptation) on top of Google’s Gemma-2B base model.
The model is designed for educational, research, and applied business analytics use cases, especially sentiment analysis of textual data such as customer feedback and social media content.

Model Details

Model Name: Sentiment Analyzer
Developed by: Varun Agrawal
Hugging Face Username: 09Vaarun
Affiliation: IIRM Jaipur
Model Type: Natural Language Processing (Sentiment Analysis / Text Generation)
Base Model: google/gemma-2b
Fine-tuning Technique: PEFT (LoRA)
Language: English
License: Apache 2.0

Intended Use

✅ Direct Use

This model can be used for:

Sentiment analysis of:
- Customer reviews
- Social media posts
- Online feedback forms
- Business and marketing text
Academic demonstrations of:
- Transformers
- Parameter-Efficient Fine-Tuning (PEFT)
- LoRA-based adaptation

🔄 Downstream Use

Social media analytics projects
Business intelligence dashboards
NLP coursework and workshops
Research experiments in sentiment analysis

❌ Out-of-Scope Use

Medical, legal, or financial decision-making
High-stakes automated systems without human review

Bias, Risks, and Limitations

The model may reflect biases present in the training data
Performance may vary across domains and writing styles
Not recommended for critical real-world decisions without further evaluation

Recommendations

Perform domain-specific validation before deployment
Use human oversight for business applications

How to Use the Model

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

base_model = "google/gemma-2b"
adapter_model = "09Vaarun/sentiment-analyzer"

tokenizer = AutoTokenizer.from_pretrained(base_model)
model = AutoModelForCausalLM.from_pretrained(base_model)
model = PeftModel.from_pretrained(model, adapter_model)

text = "The service was excellent and the staff was very helpful."
inputs = tokenizer(text, return_tensors="pt")

outputs = model.generate(
    **inputs,
    max_new_tokens=50
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloads last month: -

Model tree for 09Vaarun/sentiment-analyzer

Base model

google/gemma-2b

Adapter

(23698)

this model