Instructions to use nzgnzg73/llama_cpp_WebUI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nzgnzg73/llama_cpp_WebUI with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="nzgnzg73/llama_cpp_WebUI",
	filename="Image-Text-to-Text Models/gemma-3/gemma-3-12b-it-Q4_K_S.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 nzgnzg73/llama_cpp_WebUI with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf nzgnzg73/llama_cpp_WebUI:Q4_K_S
# Run inference directly in the terminal:
llama-cli -hf nzgnzg73/llama_cpp_WebUI:Q4_K_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf nzgnzg73/llama_cpp_WebUI:Q4_K_S
# Run inference directly in the terminal:
llama-cli -hf nzgnzg73/llama_cpp_WebUI:Q4_K_S

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 nzgnzg73/llama_cpp_WebUI:Q4_K_S
# Run inference directly in the terminal:
./llama-cli -hf nzgnzg73/llama_cpp_WebUI:Q4_K_S

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 nzgnzg73/llama_cpp_WebUI:Q4_K_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nzgnzg73/llama_cpp_WebUI:Q4_K_S

Use Docker

docker model run hf.co/nzgnzg73/llama_cpp_WebUI:Q4_K_S

LM Studio
Jan
Ollama
How to use nzgnzg73/llama_cpp_WebUI with Ollama:
```
ollama run hf.co/nzgnzg73/llama_cpp_WebUI:Q4_K_S
```

Unsloth Studio

How to use nzgnzg73/llama_cpp_WebUI 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 nzgnzg73/llama_cpp_WebUI 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 nzgnzg73/llama_cpp_WebUI to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for nzgnzg73/llama_cpp_WebUI to start chatting

Docker Model Runner
How to use nzgnzg73/llama_cpp_WebUI with Docker Model Runner:
```
docker model run hf.co/nzgnzg73/llama_cpp_WebUI:Q4_K_S
```

Lemonade

How to use nzgnzg73/llama_cpp_WebUI with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nzgnzg73/llama_cpp_WebUI:Q4_K_S

Run and chat with the model

lemonade run user.llama_cpp_WebUI-Q4_K_S

List all available models

lemonade list

nzgnzg73 commited on Nov 14, 2025

Commit

073ce48

verified ·

1 Parent(s): 528aea5

Update README.md

Browse files

Files changed (1) hide show

README.md +33 -0

README.md CHANGED Viewed

@@ -14,6 +14,10 @@ llama_cpp_WebUI
 ## Gemma-3
 ![1](https://cdn-uploads.huggingface.co/production/uploads/68edac11571026107ce50f6c/qoYFWiyKbxfec608p3AbF.png)
@@ -23,6 +27,12 @@ llama_cpp_WebUI
 ## Qwen3
 1. Qwen3-VL-2B-Instruct-Q8_0.gguf
 2. mmproj-Qwen3-VL-2B-Instruct-Q8_0.gguf
@@ -31,6 +41,26 @@ llama_cpp_WebUI
 ![2](https://cdn-uploads.huggingface.co/production/uploads/68edac11571026107ce50f6c/jmba7H290hQTPhBVL-v5Q.png)
 ## Audio-Text-to-Text
@@ -39,6 +69,9 @@ llama_cpp_WebUI
 ## Llama-3.2
 ![3](https://cdn-uploads.huggingface.co/production/uploads/68edac11571026107ce50f6c/VJ9-OMFQYX8x-4HhhtknP.png)

 ## Gemma-3
+CPU.  RAM 20GB
+OR
+GPU.  4 VRAM
 ![1](https://cdn-uploads.huggingface.co/production/uploads/68edac11571026107ce50f6c/qoYFWiyKbxfec608p3AbF.png)
 ## Qwen3
+CPU.  RAM 25GB
+OR
+GPU.  4 VRAM
 1. Qwen3-VL-2B-Instruct-Q8_0.gguf
 2. mmproj-Qwen3-VL-2B-Instruct-Q8_0.gguf
 ![2](https://cdn-uploads.huggingface.co/production/uploads/68edac11571026107ce50f6c/jmba7H290hQTPhBVL-v5Q.png)
+## Qwen2.5-Omni
+CPU.  RAM 40GB
+OR
+GPU.  8 VRAM
+1. Qwen2.5-Omni-7B-BF16.gguf
+2. mmproj-F16.gguf           2GB
+![4](https://cdn-uploads.huggingface.co/production/uploads/68edac11571026107ce50f6c/v0ACCIgBmFUmcMH5rTjj_.png)
 ## Audio-Text-to-Text
 ## Llama-3.2
+CPU.  RAM 10GB
 ![3](https://cdn-uploads.huggingface.co/production/uploads/68edac11571026107ce50f6c/VJ9-OMFQYX8x-4HhhtknP.png)