Instructions to use Kavi11/sentiment-analyzer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kavi11/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, "Kavi11/sentiment-analyzer")

Transformers

How to use Kavi11/sentiment-analyzer with Transformers:

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

pipe = pipeline("text-generation", model="Kavi11/sentiment-analyzer")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Kavi11/sentiment-analyzer", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Kavi11/sentiment-analyzer with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kavi11/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": "Kavi11/sentiment-analyzer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Kavi11/sentiment-analyzer

SGLang

How to use Kavi11/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 "Kavi11/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": "Kavi11/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 "Kavi11/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": "Kavi11/sentiment-analyzer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Kavi11/sentiment-analyzer with Docker Model Runner:
```
docker model run hf.co/Kavi11/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.

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.

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

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

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

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)

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