Instructions to use Sujith2121/gemma-ia3-alpaca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sujith2121/gemma-ia3-alpaca with Transformers:

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

pipe = pipeline("text-generation", model="Sujith2121/gemma-ia3-alpaca")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Sujith2121/gemma-ia3-alpaca", device_map="auto")

PEFT
How to use Sujith2121/gemma-ia3-alpaca with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Sujith2121/gemma-ia3-alpaca with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sujith2121/gemma-ia3-alpaca"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sujith2121/gemma-ia3-alpaca",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Sujith2121/gemma-ia3-alpaca

SGLang

How to use Sujith2121/gemma-ia3-alpaca 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 "Sujith2121/gemma-ia3-alpaca" \
    --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": "Sujith2121/gemma-ia3-alpaca",
		"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 "Sujith2121/gemma-ia3-alpaca" \
        --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": "Sujith2121/gemma-ia3-alpaca",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Sujith2121/gemma-ia3-alpaca with Docker Model Runner:
```
docker model run hf.co/Sujith2121/gemma-ia3-alpaca
```

Gemma 2B IA3 Fine-Tuned Model

Model Overview

This model is a parameter-efficient fine-tuned version of the Gemma 2B base model using IA3 (Infused Adapter by Inhibiting and Amplifying Inner Activations). It is trained on an instruction-following dataset to improve structured response generation and general task understanding.

Model Details

Base Model: google/gemma-2b
Fine-Tuning Method: IA3 (PEFT)
Task Type: Causal Language Modeling
Language: English
Dataset: yahma/alpaca-cleaned (subset ~2000 samples)

Intended Use

Direct Use

Instruction following
Question answering
General text generation

Downstream Use

Domain-specific fine-tuning (e.g., cybersecurity, assistants)
Chatbot or API integration

Out-of-Scope Use

High-risk domains (medical, legal, financial decisions)
Safety-critical applications
Real-time autonomous systems

Training Details

Preprocessing

Instruction-input-response formatting
Tokenization with max length 512
Labels copied from input_ids for causal LM

Hyperparameters

Epochs: 1
Batch size: 2
Gradient accumulation: 8
Learning rate: 2e-4
Precision: FP16

IA3 Configuration

Target modules:
- k_proj
- v_proj
- down_proj

Evaluation

Evaluation was performed qualitatively using prompt-based testing. The model demonstrates improved instruction-following capability compared to the base model.

Limitations

Small dataset size limits generalization
May produce hallucinations or incorrect outputs
No alignment tuning (e.g., RLHF/DPO) applied

Bias and Risks

Inherits biases from base model and dataset
May generate harmful or misleading content
Requires monitoring in production systems

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("your-username/gemma-ia3-alpaca")
tokenizer = AutoTokenizer.from_pretrained("your-username/gemma-ia3-alpaca")

prompt = "### Instruction:\nExplain SQL injection attack\n\n### Response:"
inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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