Instructions to use chimbiwide/Gemma3NPC-it-beta-Q4-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chimbiwide/Gemma3NPC-it-beta-Q4-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("chimbiwide/Gemma3NPC-it-beta-Q4-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use chimbiwide/Gemma3NPC-it-beta-Q4-GGUF 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 chimbiwide/Gemma3NPC-it-beta-Q4-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf chimbiwide/Gemma3NPC-it-beta-Q4-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf chimbiwide/Gemma3NPC-it-beta-Q4-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf chimbiwide/Gemma3NPC-it-beta-Q4-GGUF:Q4_K_M

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 chimbiwide/Gemma3NPC-it-beta-Q4-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf chimbiwide/Gemma3NPC-it-beta-Q4-GGUF:Q4_K_M

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 chimbiwide/Gemma3NPC-it-beta-Q4-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf chimbiwide/Gemma3NPC-it-beta-Q4-GGUF:Q4_K_M

Use Docker

docker model run hf.co/chimbiwide/Gemma3NPC-it-beta-Q4-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use chimbiwide/Gemma3NPC-it-beta-Q4-GGUF with Ollama:
```
ollama run hf.co/chimbiwide/Gemma3NPC-it-beta-Q4-GGUF:Q4_K_M
```

Unsloth Studio

How to use chimbiwide/Gemma3NPC-it-beta-Q4-GGUF 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 chimbiwide/Gemma3NPC-it-beta-Q4-GGUF 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 chimbiwide/Gemma3NPC-it-beta-Q4-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for chimbiwide/Gemma3NPC-it-beta-Q4-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use chimbiwide/Gemma3NPC-it-beta-Q4-GGUF with Docker Model Runner:
```
docker model run hf.co/chimbiwide/Gemma3NPC-it-beta-Q4-GGUF:Q4_K_M
```

Lemonade

How to use chimbiwide/Gemma3NPC-it-beta-Q4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull chimbiwide/Gemma3NPC-it-beta-Q4-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Gemma3NPC-it-beta-Q4-GGUF-Q4_K_M

List all available models

lemonade list

Gemma3NPC-it-beta

A test model with less convervative training parameters

The Q4_K_M quantized version of Gemma3NPC-it-beta-Float16.

As mentioned in our original article, we employed a very conservative training parameters for Gemma3NPC

Ever since then, we have always wanted to test the performance of the model when we make the training parameters less conservative.

So we present Gemma3NPC-it-beta.

Check out our training notebook here

Training parameters compared to `Gemma3NPC-it`

Parameter	Gemma3NPC-it	Gemma3NPC-it-beta
Learning Rate	2e-5	2.5e-5 (+25%)
Warmup Steps	800	100
gradient clipping	0.4	1.0

Here is a graph of the Step Training Loss, saved every 10 steps:

Downloads last month: 6

GGUF

Model size

7B params

Architecture

gemma3n

Hardware compatibility

4-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for chimbiwide/Gemma3NPC-it-beta-Q4-GGUF

Base model

chimbiwide/Gemma3NPC-it-beta

Quantized

(2)

this model

Dataset used to train chimbiwide/Gemma3NPC-it-beta-Q4-GGUF

Collection including chimbiwide/Gemma3NPC-it-beta-Q4-GGUF

Gemma3NPC-it-beta

Collection

A new attempt at training Gemma3NPC, with less conservative training parameters • 3 items • Updated Feb 27

Gemma3NPC-it-beta

A test model with less convervative training parameters

Training parameters compared to Gemma3NPC-it

Model tree for chimbiwide/Gemma3NPC-it-beta-Q4-GGUF

Dataset used to train chimbiwide/Gemma3NPC-it-beta-Q4-GGUF

Collection including chimbiwide/Gemma3NPC-it-beta-Q4-GGUF

Training parameters compared to `Gemma3NPC-it`