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 Desktop
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

Atomic Chat

Gemma3NPC-it-beta-Q4-GGUF / README.md

chimbiwide

Update README.md

f9d14ca verified 10 months ago

preview code

Raw History Blame

1.25 kB

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

	As mentioned in our [original article](https://huggingface.co/blog/chimbiwide/gemma3npc), 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](https://github.com/chimbiwide/Gemma3NPC/blob/main/Training/Gemma3NPC_Instruct_Beta.ipynb)

	---

	#### 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:

	![chart](https://cdn-uploads.huggingface.co/production/uploads/67d5b5a056a9d31aa0b49687/W3cJ_CPoLp9MZsomaZa3b.png)