Instructions to use Tushar1K/gemma-function-calling-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tushar1K/gemma-function-calling-lora with Transformers:

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

pipe = pipeline("text-generation", model="Tushar1K/gemma-function-calling-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Tushar1K/gemma-function-calling-lora", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Tushar1K/gemma-function-calling-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Tushar1K/gemma-function-calling-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": "Tushar1K/gemma-function-calling-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Tushar1K/gemma-function-calling-lora

SGLang

How to use Tushar1K/gemma-function-calling-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 "Tushar1K/gemma-function-calling-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": "Tushar1K/gemma-function-calling-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 "Tushar1K/gemma-function-calling-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": "Tushar1K/gemma-function-calling-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use Tushar1K/gemma-function-calling-lora with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Tushar1K/gemma-function-calling-lora to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Tushar1K/gemma-function-calling-lora to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Tushar1K/gemma-function-calling-lora to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Tushar1K/gemma-function-calling-lora",
    max_seq_length=2048,
)

Docker Model Runner
How to use Tushar1K/gemma-function-calling-lora with Docker Model Runner:
```
docker model run hf.co/Tushar1K/gemma-function-calling-lora
```

Developed by: Tushar Kamthe
License: apache-2.0
Finetuned from model : unsloth/gemma-2-2b-it-bnb-4bit

Gemma-2B QLoRA Fine-tuned Model

Model Description

This model is a fine-tuned version of Google's Gemma-2B using the QLoRA (Quantized Low-Rank Adaptation) technique. The model was trained using parameter-efficient fine-tuning (PEFT), where only LoRA adapters were trained while keeping the base model weights frozen.

The model is designed for instruction-following text generation tasks.

Fine-tuning was performed using:

QLoRA (4-bit quantization)
LoRA adapters
HuggingFace Transformers
PEFT
Unsloth for faster training

Base Model

Base model used for training:

google/gemma-2b

Training Details

Training Method

The model was trained using QLoRA, which enables efficient training of large language models by:

Loading the base model in 4-bit quantized format
Training LoRA adapter weights only
Keeping base model weights frozen

This significantly reduces GPU memory requirements.

Training Configuration

Parameter	Value
Method	QLoRA
Quantization	4-bit (NF4)
LoRA Rank (r)	16
LoRA Alpha	64
LoRA Dropout	0.05
Optimizer	AdamW
Precision	bfloat16
Framework	HuggingFace Transformers

Hardware

Training was performed on:

GPU: NVIDIA GPU (Colab / Local GPU)
Framework: PyTorch
Libraries:
- transformers
- peft
- datasets
- unsloth

Dataset

The model was fine-tuned on a custom instruction dataset containing prompt-response pairs.

Dataset format:

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

Model tree for Tushar1K/gemma-function-calling-lora

Base model

google/gemma-2b

Adapter

(23700)

this model