Instructions to use skgufranahamed/functiongemma-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use skgufranahamed/functiongemma-finetuned with Transformers:

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

pipe = pipeline("text-generation", model="skgufranahamed/functiongemma-finetuned")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("skgufranahamed/functiongemma-finetuned")
model = AutoModelForCausalLM.from_pretrained("skgufranahamed/functiongemma-finetuned")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use skgufranahamed/functiongemma-finetuned with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="skgufranahamed/functiongemma-finetuned",
	filename="functiongemma-finetuned.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use skgufranahamed/functiongemma-finetuned with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf skgufranahamed/functiongemma-finetuned
# Run inference directly in the terminal:
llama cli -hf skgufranahamed/functiongemma-finetuned

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf skgufranahamed/functiongemma-finetuned
# Run inference directly in the terminal:
llama cli -hf skgufranahamed/functiongemma-finetuned

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf skgufranahamed/functiongemma-finetuned
# Run inference directly in the terminal:
./llama-cli -hf skgufranahamed/functiongemma-finetuned

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf skgufranahamed/functiongemma-finetuned
# Run inference directly in the terminal:
./build/bin/llama-cli -hf skgufranahamed/functiongemma-finetuned

Use Docker

docker model run hf.co/skgufranahamed/functiongemma-finetuned

LM Studio
Jan

vLLM

How to use skgufranahamed/functiongemma-finetuned with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/skgufranahamed/functiongemma-finetuned

SGLang

How to use skgufranahamed/functiongemma-finetuned 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 "skgufranahamed/functiongemma-finetuned" \
    --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": "skgufranahamed/functiongemma-finetuned",
		"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 "skgufranahamed/functiongemma-finetuned" \
        --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": "skgufranahamed/functiongemma-finetuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use skgufranahamed/functiongemma-finetuned with Ollama:
```
ollama run hf.co/skgufranahamed/functiongemma-finetuned
```

Unsloth Studio

How to use skgufranahamed/functiongemma-finetuned 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 skgufranahamed/functiongemma-finetuned 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 skgufranahamed/functiongemma-finetuned to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for skgufranahamed/functiongemma-finetuned to start chatting

How to use skgufranahamed/functiongemma-finetuned with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf skgufranahamed/functiongemma-finetuned

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "skgufranahamed/functiongemma-finetuned"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use skgufranahamed/functiongemma-finetuned with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf skgufranahamed/functiongemma-finetuned

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default skgufranahamed/functiongemma-finetuned

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use skgufranahamed/functiongemma-finetuned with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf skgufranahamed/functiongemma-finetuned

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "skgufranahamed/functiongemma-finetuned" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use skgufranahamed/functiongemma-finetuned with Docker Model Runner:
```
docker model run hf.co/skgufranahamed/functiongemma-finetuned
```

Lemonade

How to use skgufranahamed/functiongemma-finetuned with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull skgufranahamed/functiongemma-finetuned

Run and chat with the model

lemonade run user.functiongemma-finetuned-{{QUANT_TAG}}

List all available models

lemonade list

Model Card for functiongemma-finetuned

This model is a fine-tuned version of google/functiongemma-270m-it. It has been trained using TRL.

Quick start

from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with SFT.

Framework versions

TRL: 1.8.0
Transformers: 5.12.1
Pytorch: 2.11.0+cu128
Datasets: 5.0.0
Tokenizers: 0.22.2

Citations

Cite TRL as:

@software{vonwerra2020trl,
  title   = {{TRL: Transformers Reinforcement Learning}},
  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
  license = {Apache-2.0},
  url     = {https://github.com/huggingface/trl},
  year    = {2020}
}

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Safetensors

Model size

0.3B params

Tensor type

BF16

Model tree for skgufranahamed/functiongemma-finetuned

Base model

google/functiongemma-270m-it

Quantized

(50)

this model