Instructions to use archanaachu776/sentiment-analyzer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use archanaachu776/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, "archanaachu776/sentiment-analyzer")

Transformers

How to use archanaachu776/sentiment-analyzer with Transformers:

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

pipe = pipeline("text-generation", model="archanaachu776/sentiment-analyzer")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("archanaachu776/sentiment-analyzer", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use archanaachu776/sentiment-analyzer with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "archanaachu776/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": "archanaachu776/sentiment-analyzer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/archanaachu776/sentiment-analyzer

SGLang

How to use archanaachu776/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 "archanaachu776/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": "archanaachu776/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 "archanaachu776/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": "archanaachu776/sentiment-analyzer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use archanaachu776/sentiment-analyzer with Docker Model Runner:
```
docker model run hf.co/archanaachu776/sentiment-analyzer
```

Sentiment Analyzer Model

Overview

This repository contains a Sentiment Analysis model fine-tuned using LoRA (Low-Rank Adaptation) on top of the Google Gemma-2B base model.
The model is designed to analyze text input and determine the sentiment polarity (positive, negative, or neutral).

The model is hosted on Hugging Face and uploaded using the huggingface_hub Python API.

Model Details

Model Description

Developed by: Archana S
Hugging Face Username: archanaachu776
Model type: Text Generation / Sentiment Analysis
Language(s): English
Base Model: google/gemma-2b
Fine-tuning Method: PEFT (LoRA)
Library: Transformers + PEFT
License: Apache 2.0
Finetuned from: google/gemma-2b

Model Sources

Repository: https://huggingface.co/archanaachu776/sentiment-analyzer
Base Model: https://huggingface.co/google/gemma-2b

Intended Uses

Direct Use

Analyze sentiment of user reviews
Customer feedback analysis
Social media sentiment monitoring
Text classification tasks requiring sentiment polarity

Downstream Use

Can be integrated into chatbots
Can be used in recommendation systems
Can be extended for domain-specific sentiment analysis (e.g., product, finance, healthcare)

Out-of-Scope Use

Not suitable for generating medical, legal, or financial advice
Not trained for multilingual sentiment analysis
Not designed for toxicity or hate-speech detection

Bias, Risks, and Limitations

The model may reflect biases present in the training data
Performance may vary for informal, slang-heavy, or sarcastic text
Accuracy depends on the domain similarity to training data

Recommendations

Users should validate predictions before using them in critical decision-making systems and consider further fine-tuning for domain-specific applications.

How to Get Started

Installation

pip install transformers peft torch


from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "archanaachu776/sentiment-analyzer"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

text = "The product quality is excellent and I am very happy."

inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)

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

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google/gemma-2b

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