Instructions to use ArunKr/gemma-3-270m-it-web-agent-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ArunKr/gemma-3-270m-it-web-agent-lora with Transformers:

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

pipe = pipeline("text-generation", model="ArunKr/gemma-3-270m-it-web-agent-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ArunKr/gemma-3-270m-it-web-agent-lora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ArunKr/gemma-3-270m-it-web-agent-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ArunKr/gemma-3-270m-it-web-agent-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ArunKr/gemma-3-270m-it-web-agent-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ArunKr/gemma-3-270m-it-web-agent-lora

SGLang

How to use ArunKr/gemma-3-270m-it-web-agent-lora 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 "ArunKr/gemma-3-270m-it-web-agent-lora" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ArunKr/gemma-3-270m-it-web-agent-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "ArunKr/gemma-3-270m-it-web-agent-lora" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ArunKr/gemma-3-270m-it-web-agent-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ArunKr/gemma-3-270m-it-web-agent-lora with Docker Model Runner:
```
docker model run hf.co/ArunKr/gemma-3-270m-it-web-agent-lora
```

gemma-3-270m-it-web-agent - Fine-tuned

This repository contains three variants of the model:

LoRA adapters → ArunKr/gemma-3-270m-it-web-agent-lora
Merged FP16 weights → ArunKr/gemma-3-270m-it-web-agent-16bit
GGUF quantizations → ArunKr/gemma-3-270m-it-web-agent-gguf

Training

Base model: unsloth/gemma-3-270m-it
Dataset: ArunKr/gui_grounding_dataset-100
Method: LoRA fine-tuning with Unsloth

Quantizations

We provide f16, bf16, f32, and q8_0 GGUF files for llama.cpp / Ollama.

Usage Example

from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("ArunKr/gemma-3-270m-it-web-agent-16bit")
model = AutoModelForCausalLM.from_pretrained("ArunKr/gemma-3-270m-it-web-agent-16bit")
print(model.generate(**tok("Hello", return_tensors="pt")))

Ollama Example

ollama run ArunKr/SmolLM-135M-Instruct-manim-gguf:<file_name>.gguf

www.ollama.com

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for ArunKr/gemma-3-270m-it-web-agent-lora

Base model

HuggingFaceTB/SmolLM-135M

Quantized

HuggingFaceTB/SmolLM-135M-Instruct

Adapter

(19)

this model

ArunKr
/

gemma-3-270m-it-web-agent-lora