Instructions to use madhuHuggingface/functiongemma-ec2-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use madhuHuggingface/functiongemma-ec2-finetuned with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("madhuHuggingface/functiongemma-ec2-finetuned", dtype="auto")

llama-cpp-python

How to use madhuHuggingface/functiongemma-ec2-finetuned with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="madhuHuggingface/functiongemma-ec2-finetuned",
	filename="gguf/functiongemma-270m-it.Q8_0.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use madhuHuggingface/functiongemma-ec2-finetuned with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf madhuHuggingface/functiongemma-ec2-finetuned:Q8_0
# Run inference directly in the terminal:
llama-cli -hf madhuHuggingface/functiongemma-ec2-finetuned:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf madhuHuggingface/functiongemma-ec2-finetuned:Q8_0
# Run inference directly in the terminal:
llama-cli -hf madhuHuggingface/functiongemma-ec2-finetuned:Q8_0

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 madhuHuggingface/functiongemma-ec2-finetuned:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf madhuHuggingface/functiongemma-ec2-finetuned:Q8_0

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 madhuHuggingface/functiongemma-ec2-finetuned:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf madhuHuggingface/functiongemma-ec2-finetuned:Q8_0

Use Docker

docker model run hf.co/madhuHuggingface/functiongemma-ec2-finetuned:Q8_0

LM Studio
Jan
Ollama
How to use madhuHuggingface/functiongemma-ec2-finetuned with Ollama:
```
ollama run hf.co/madhuHuggingface/functiongemma-ec2-finetuned:Q8_0
```

Unsloth Studio new

How to use madhuHuggingface/functiongemma-ec2-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 madhuHuggingface/functiongemma-ec2-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 madhuHuggingface/functiongemma-ec2-finetuned to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for madhuHuggingface/functiongemma-ec2-finetuned to start chatting

Pi new

How to use madhuHuggingface/functiongemma-ec2-finetuned with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf madhuHuggingface/functiongemma-ec2-finetuned:Q8_0

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": "madhuHuggingface/functiongemma-ec2-finetuned:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use madhuHuggingface/functiongemma-ec2-finetuned with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf madhuHuggingface/functiongemma-ec2-finetuned:Q8_0

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 madhuHuggingface/functiongemma-ec2-finetuned:Q8_0

Run Hermes

hermes

Docker Model Runner
How to use madhuHuggingface/functiongemma-ec2-finetuned with Docker Model Runner:
```
docker model run hf.co/madhuHuggingface/functiongemma-ec2-finetuned:Q8_0
```

Lemonade

How to use madhuHuggingface/functiongemma-ec2-finetuned with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull madhuHuggingface/functiongemma-ec2-finetuned:Q8_0

Run and chat with the model

lemonade run user.functiongemma-ec2-finetuned-Q8_0

List all available models

lemonade list

functiongemma-ec2-finetuned / README.md

madhuHuggingface

Training in progress, step 100

9ee5eb1 verified 22 days ago

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	---
	base_model: unsloth/functiongemma-270m-it
	library_name: transformers
	model_name: functiongemma-ec2-finetuned
	tags:
	- generated_from_trainer
	- trl
	- unsloth
	- sft
	licence: license
	---

	# Model Card for functiongemma-ec2-finetuned

	This model is a fine-tuned version of [unsloth/functiongemma-270m-it](https://huggingface.co/unsloth/functiongemma-270m-it).
	It has been trained using [TRL](https://github.com/huggingface/trl).

	## Quick start

	```python
	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="madhuHuggingface/functiongemma-ec2-finetuned", 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: 0.22.2
	- Transformers: 4.56.2
	- Pytorch: 2.10.0+cu128
	- Datasets: 4.3.0
	- Tokenizers: 0.22.2

	## Citations



	Cite TRL as:

	```bibtex
	@misc{vonwerra2022trl,
	title = {{TRL: Transformer Reinforcement Learning}},
	author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
	year = 2020,
	journal = {GitHub repository},
	publisher = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
	}
	```