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 / best_model /tokenizer_config.json

Commit History

Training in progress, step 800

1aa0aba
verified

madhuHuggingface commited on Apr 24

Commit History

Training in progress, step 800 1aa0aba verified

Training in progress, step 800

1aa0aba
verified