Instructions to use chimbiwide/Gemma3NPC-it-beta-Q8-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chimbiwide/Gemma3NPC-it-beta-Q8-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("chimbiwide/Gemma3NPC-it-beta-Q8-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use chimbiwide/Gemma3NPC-it-beta-Q8-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-Q8-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf chimbiwide/Gemma3NPC-it-beta-Q8-GGUF:Q8_0

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-Q8-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf chimbiwide/Gemma3NPC-it-beta-Q8-GGUF: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 chimbiwide/Gemma3NPC-it-beta-Q8-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf chimbiwide/Gemma3NPC-it-beta-Q8-GGUF: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 chimbiwide/Gemma3NPC-it-beta-Q8-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf chimbiwide/Gemma3NPC-it-beta-Q8-GGUF:Q8_0

Use Docker

docker model run hf.co/chimbiwide/Gemma3NPC-it-beta-Q8-GGUF:Q8_0

LM Studio
Jan
Ollama
How to use chimbiwide/Gemma3NPC-it-beta-Q8-GGUF with Ollama:
```
ollama run hf.co/chimbiwide/Gemma3NPC-it-beta-Q8-GGUF:Q8_0
```
Unsloth Desktop
Docker Model Runner
How to use chimbiwide/Gemma3NPC-it-beta-Q8-GGUF with Docker Model Runner:
```
docker model run hf.co/chimbiwide/Gemma3NPC-it-beta-Q8-GGUF:Q8_0
```

Lemonade

How to use chimbiwide/Gemma3NPC-it-beta-Q8-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull chimbiwide/Gemma3NPC-it-beta-Q8-GGUF:Q8_0

Run and chat with the model

lemonade run user.Gemma3NPC-it-beta-Q8-GGUF-Q8_0

List all available models

lemonade list

Atomic Chat

Gemma3NPC-it-beta-Q8-GGUF / README.md

chimbiwide

Update README.md

e64c35b verified 10 months ago

preview code

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metadata

base_model: chimbiwide/gemma-3NPC-it-beta
tags:
  - text-generation-inference
  - transformers
  - unsloth
  - gemma3n
  - llama-cpp
  - gguf-my-repo
license: apache-2.0
language:
  - en
datasets:
  - chimbiwide/RolePlay-NPC

Gemma3NPC-it-beta

A test model with less convervative training parameters

The Q8_0 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:

Gemma3NPC-it-beta

A test model with less convervative training parameters

Training parameters compared to Gemma3NPC-it

Training parameters compared to `Gemma3NPC-it`