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
```

gemma-function-calling-lora / README.md

Tushar1K

Update README.md

0f04eca verified 4 months ago

preview code

Raw

History Blame Contribute Delete

1.84 kB

	---
	license: apache-2.0
	base_model: google/gemma-2b
	tags:
	- gemma
	- lora
	- qlora
	- instruction-tuning
	- unsloth
	- transformers
	- text-generation
	library_name: transformers
	---
	- 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: