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

File size: 658 Bytes

374eb16

{
  "backend": "tokenizers",
  "bos_token": "<bos>",
  "clean_up_tokenization_spaces": false,
  "eos_token": "<eos>",
  "extra_special_tokens": [
    "<tools>",
    "</tools>",
    "<think>",
    "</think>",
    "<tool_call>",
    "</tool_call>",
    "<tool_response>",
    "</tool_response>",
    "<pad>",
    "<eos>"
  ],
  "from_slow": true,
  "is_local": false,
  "legacy": false,
  "mask_token": "<mask>",
  "model_max_length": 8192,
  "pad_token": "<pad>",
  "padding_side": "left",
  "sp_model_kwargs": {},
  "spaces_between_special_tokens": false,
  "tokenizer_class": "GemmaTokenizer",
  "unk_token": "<unk>",
  "use_default_system_prompt": false
}