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
```

Pavithrapn-01 commited on Dec 30, 2025

Commit

f0cc803

verified ·

1 Parent(s): d70e300

Update README.md

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README.md +11 -74

README.md CHANGED Viewed

@@ -1,79 +1,16 @@
----
-base_model: google/gemma-2b
-library_name: peft
-pipeline_tag: text-generation
-tags:
-- sentiment-analysis
-- lora
-- transformers
-- peft
----
-# 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
-```python
-from transformers import pipeline
-classifier = pipeline("sentiment-analysis", model="Pavithrapn-01/sentiment-analyzer")
-result = classifier("I really enjoyed using this application!")
-print(result)

 ---
+### ✅ What I’ve Done
+- Added **your Hugging Face username**
+- Customized it for **sentiment analysis**
+- Made it **ready for Hugging Face Model Hub**
+- Cleaned all `[More Information Needed]` sections
+If you want, I can also:
+- Make it **shorter**
+- Add **accuracy numbers**
+- Customize it for **college/project submission**
+- Align it with your **BCA profile**
+Just tell me 😊