Instructions to use benndev/MyGemmaNPC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use benndev/MyGemmaNPC with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("benndev/MyGemmaNPC", dtype="auto") - llama-cpp-python
How to use benndev/MyGemmaNPC with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="benndev/MyGemmaNPC", filename="gemma-3-270m-it-NPC.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 benndev/MyGemmaNPC with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf benndev/MyGemmaNPC # Run inference directly in the terminal: llama-cli -hf benndev/MyGemmaNPC
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf benndev/MyGemmaNPC # Run inference directly in the terminal: llama-cli -hf benndev/MyGemmaNPC
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 benndev/MyGemmaNPC # Run inference directly in the terminal: ./llama-cli -hf benndev/MyGemmaNPC
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 benndev/MyGemmaNPC # Run inference directly in the terminal: ./build/bin/llama-cli -hf benndev/MyGemmaNPC
Use Docker
docker model run hf.co/benndev/MyGemmaNPC
- LM Studio
- Jan
- Ollama
How to use benndev/MyGemmaNPC with Ollama:
ollama run hf.co/benndev/MyGemmaNPC
- Unsloth Studio new
How to use benndev/MyGemmaNPC 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 benndev/MyGemmaNPC 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 benndev/MyGemmaNPC to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for benndev/MyGemmaNPC to start chatting
- Docker Model Runner
How to use benndev/MyGemmaNPC with Docker Model Runner:
docker model run hf.co/benndev/MyGemmaNPC
- Lemonade
How to use benndev/MyGemmaNPC with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull benndev/MyGemmaNPC
Run and chat with the model
lemonade run user.MyGemmaNPC-{{QUANT_TAG}}List all available models
lemonade list
llm.create_chat_completion(
messages = "No input example has been defined for this model task."
)Model Card for MyGemmaNPC
This model is a fine-tuned version of google/gemma-3-270m-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="benndev/MyGemmaNPC", 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: 0.21.0
- Transformers: 4.55.0
- Pytorch: 2.6.0+cu124
- Datasets: 4.0.0
- Tokenizers: 0.21.4
Citations
Cite TRL as:
@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}}
}
- Downloads last month
- 10
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="benndev/MyGemmaNPC", filename="gemma-3-270m-it-NPC.gguf", )