Instructions to use oncu/X-Ray_Alpha-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use oncu/X-Ray_Alpha-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="oncu/X-Ray_Alpha-GGUF",
	filename="x-ray_alpha-f16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use oncu/X-Ray_Alpha-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 oncu/X-Ray_Alpha-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf oncu/X-Ray_Alpha-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 oncu/X-Ray_Alpha-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf oncu/X-Ray_Alpha-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 oncu/X-Ray_Alpha-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf oncu/X-Ray_Alpha-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 oncu/X-Ray_Alpha-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf oncu/X-Ray_Alpha-GGUF:Q4_K_M

Use Docker

docker model run hf.co/oncu/X-Ray_Alpha-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use oncu/X-Ray_Alpha-GGUF with Ollama:
```
ollama run hf.co/oncu/X-Ray_Alpha-GGUF:Q4_K_M
```

Unsloth Studio

How to use oncu/X-Ray_Alpha-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 oncu/X-Ray_Alpha-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 oncu/X-Ray_Alpha-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for oncu/X-Ray_Alpha-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use oncu/X-Ray_Alpha-GGUF with Docker Model Runner:
```
docker model run hf.co/oncu/X-Ray_Alpha-GGUF:Q4_K_M
```

Lemonade

How to use oncu/X-Ray_Alpha-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull oncu/X-Ray_Alpha-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.X-Ray_Alpha-GGUF-Q4_K_M

List all available models

lemonade list

X-Ray_Alpha-GGUF / README.md

oncu

Upload README.md with huggingface_hub

f2362d0 verified about 1 year ago

preview code

Raw

History Blame Contribute Delete

7.94 kB

	---
	base_model: SicariusSicariiStuff/X-Ray_Alpha
	datasets:
	- SicariusSicariiStuff/UBW_Tapestries
	language:
	- en
	license: gemma
	tags:
	- llama-cpp
	- matrixportal
	---

	# oncu/X-Ray_Alpha-GGUF
	This model was converted to GGUF format from [`SicariusSicariiStuff/X-Ray_Alpha`](https://huggingface.co/SicariusSicariiStuff/X-Ray_Alpha) using llama.cpp via the ggml.ai's [all-gguf-same-where](https://huggingface.co/spaces/matrixportal/all-gguf-same-where) space.
	Refer to the [original model card](https://huggingface.co/SicariusSicariiStuff/X-Ray_Alpha) for more details on the model.

	## ✅ Quantized Models Download List

	### 🔍 Recommended Quantizations
	- ✨ General CPU Use: [`Q4_K_M`](https://huggingface.co/oncu/X-Ray_Alpha-GGUF/resolve/main/x-ray_alpha-q4_k_m.gguf) (Best balance of speed/quality)
	- 📱 ARM Devices: [`Q4_0`](https://huggingface.co/oncu/X-Ray_Alpha-GGUF/resolve/main/x-ray_alpha-q4_0.gguf) (Optimized for ARM CPUs)
	- 🏆 Maximum Quality: [`Q8_0`](https://huggingface.co/oncu/X-Ray_Alpha-GGUF/resolve/main/x-ray_alpha-q8_0.gguf) (Near-original quality)

	### 📦 Full Quantization Options
	\| 🚀 Download \| 🔢 Type \| 📝 Notes \|
	\|:---------\|:-----\|:------\|
	\| [Download](https://huggingface.co/oncu/X-Ray_Alpha-GGUF/resolve/main/x-ray_alpha-q2_k.gguf) \| ![Q2_K](https://img.shields.io/badge/Q2_K-1A73E8) \| Basic quantization \|
	\| [Download](https://huggingface.co/oncu/X-Ray_Alpha-GGUF/resolve/main/x-ray_alpha-q3_k_s.gguf) \| ![Q3_K_S](https://img.shields.io/badge/Q3_K_S-34A853) \| Small size \|
	\| [Download](https://huggingface.co/oncu/X-Ray_Alpha-GGUF/resolve/main/x-ray_alpha-q3_k_m.gguf) \| ![Q3_K_M](https://img.shields.io/badge/Q3_K_M-FBBC05) \| Balanced quality \|
	\| [Download](https://huggingface.co/oncu/X-Ray_Alpha-GGUF/resolve/main/x-ray_alpha-q3_k_l.gguf) \| ![Q3_K_L](https://img.shields.io/badge/Q3_K_L-4285F4) \| Better quality \|
	\| [Download](https://huggingface.co/oncu/X-Ray_Alpha-GGUF/resolve/main/x-ray_alpha-q4_0.gguf) \| ![Q4_0](https://img.shields.io/badge/Q4_0-EA4335) \| Fast on ARM \|
	\| [Download](https://huggingface.co/oncu/X-Ray_Alpha-GGUF/resolve/main/x-ray_alpha-q4_k_s.gguf) \| ![Q4_K_S](https://img.shields.io/badge/Q4_K_S-673AB7) \| Fast, recommended \|
	\| [Download](https://huggingface.co/oncu/X-Ray_Alpha-GGUF/resolve/main/x-ray_alpha-q4_k_m.gguf) \| ![Q4_K_M](https://img.shields.io/badge/Q4_K_M-673AB7) ⭐ \| Best balance \|
	\| [Download](https://huggingface.co/oncu/X-Ray_Alpha-GGUF/resolve/main/x-ray_alpha-q5_0.gguf) \| ![Q5_0](https://img.shields.io/badge/Q5_0-FF6D01) \| Good quality \|
	\| [Download](https://huggingface.co/oncu/X-Ray_Alpha-GGUF/resolve/main/x-ray_alpha-q5_k_s.gguf) \| ![Q5_K_S](https://img.shields.io/badge/Q5_K_S-0F9D58) \| Balanced \|
	\| [Download](https://huggingface.co/oncu/X-Ray_Alpha-GGUF/resolve/main/x-ray_alpha-q5_k_m.gguf) \| ![Q5_K_M](https://img.shields.io/badge/Q5_K_M-0F9D58) \| High quality \|
	\| [Download](https://huggingface.co/oncu/X-Ray_Alpha-GGUF/resolve/main/x-ray_alpha-q6_k.gguf) \| ![Q6_K](https://img.shields.io/badge/Q6_K-4285F4) 🏆 \| Very good quality \|
	\| [Download](https://huggingface.co/oncu/X-Ray_Alpha-GGUF/resolve/main/x-ray_alpha-q8_0.gguf) \| ![Q8_0](https://img.shields.io/badge/Q8_0-EA4335) ⚡ \| Fast, best quality \|
	\| [Download](https://huggingface.co/oncu/X-Ray_Alpha-GGUF/resolve/main/x-ray_alpha-f16.gguf) \| ![F16](https://img.shields.io/badge/F16-000000) \| Maximum accuracy \|

	💡 Tip: Use `F16` for maximum precision when quality is critical


	---
	# 🚀 Applications and Tools for Locally Quantized LLMs
	## 🖥️ Desktop Applications

	\| Application \| Description \| Download Link \|
	\|-----------------\|----------------------------------------------------------------------------------------------\|--------------------------------------------------------------------------------\|
	\| Llama.cpp \| A fast and efficient inference engine for GGUF models. \| [GitHub Repository](https://github.com/ggml-org/llama.cpp) \|
	\| Ollama \| A streamlined solution for running LLMs locally. \| [Website](https://ollama.com/) \|
	\| AnythingLLM \| An AI-powered knowledge management tool. \| [GitHub Repository](https://github.com/Mintplex-Labs/anything-llm) \|
	\| Open WebUI \| A user-friendly web interface for running local LLMs. \| [GitHub Repository](https://github.com/open-webui/open-webui) \|
	\| GPT4All \| A user-friendly desktop application supporting various LLMs, compatible with GGUF models. \| [GitHub Repository](https://github.com/nomic-ai/gpt4all) \|
	\| LM Studio \| A desktop application designed to run and manage local LLMs, supporting GGUF format. \| [Website](https://lmstudio.ai/) \|
	\| GPT4All Chat\| A chat application compatible with GGUF models for local, offline interactions. \| [GitHub Repository](https://github.com/nomic-ai/gpt4all) \|

	---

	## 📱 Mobile Applications

	\| Application \| Description \| Download Link \|
	\|-------------------\|----------------------------------------------------------------------------------------------\|--------------------------------------------------------------------------------\|
	\| ChatterUI \| A simple and lightweight LLM app for mobile devices. \| [GitHub Repository](https://github.com/Vali-98/ChatterUI) \|
	\| Maid \| Mobile Artificial Intelligence Distribution for running AI models on mobile devices. \| [GitHub Repository](https://github.com/Mobile-Artificial-Intelligence/maid) \|
	\| PocketPal AI \| A mobile AI assistant powered by local models. \| [GitHub Repository](https://github.com/a-ghorbani/pocketpal-ai) \|
	\| Layla \| A flexible platform for running various AI models on mobile devices. \| [Website](https://www.layla-network.ai/) \|

	---

	## 🎨 Image Generation Applications

	\| Application \| Description \| Download Link \|
	\|-------------------------------------\|----------------------------------------------------------------------------------------------\|--------------------------------------------------------------------------------\|
	\| Stable Diffusion \| An open-source AI model for generating images from text. \| [GitHub Repository](https://github.com/CompVis/stable-diffusion) \|
	\| Stable Diffusion WebUI \| A web application providing access to Stable Diffusion models via a browser interface. \| [GitHub Repository](https://github.com/AUTOMATIC1111/stable-diffusion-webui) \|
	\| Local Dream \| Android Stable Diffusion with Snapdragon NPU acceleration. Also supports CPU inference. \| [GitHub Repository](https://github.com/xororz/local-dream) \|
	\| Stable-Diffusion-Android (SDAI) \| An open-source AI art application for Android devices, enabling digital art creation. \| [GitHub Repository](https://github.com/ShiftHackZ/Stable-Diffusion-Android) \|

	---

	---
	base_model: SicariusSicariiStuff/X-Ray_Alpha
	datasets:
	- SicariusSicariiStuff/UBW_Tapestries
	language:
	- en
	license: gemma
	tags:
	- llama-cpp
	- matrixportal
	---

	# oncu/X-Ray_Alpha-GGUF
	This model was converted to GGUF format from [`SicariusSicariiStuff/X-Ray_Alpha`](https://huggingface.co/SicariusSicariiStuff/X-Ray_Alpha) using llama.cpp via the ggml.ai's [all-gguf-same-where](https://huggingface.co/spaces/matrixportal/all-gguf-same-where) space.
	Refer to the [original model card](https://huggingface.co/SicariusSicariiStuff/X-Ray_Alpha) for more details on the model.

	## ✅ Quantized Models Download List

	### 🔍 Recommended Quantizations
	- ✨ General CPU Use: [`Q4_K_M`](https://huggingface.co/oncu/X-Ray_Alpha-GGUF/resolve/main/x-ray_alpha-q4_k_m.gguf) (Best balance of speed/quality)
	- 📱 ARM Devices: [`Q4_0`](https://huggingface.co/oncu/X-Ray_Alpha-GGUF/resolve/main/x-ray_alpha-q4_0.gguf) (Optimized for ARM CPUs)
	- 🏆 Maximum Quality: [`Q8_0`](https://huggingface.co/oncu/X-Ray_Alpha-GGUF/resolve/main/x-ray_alpha-q8_0.gguf) (Near-original quality)

	### 📦 Full Quantization Options
	\| 🚀 Download \| 🔢 Type \| 📝 Notes \|
	\|:---------\|:-----\|:------\|
	\| [Download](https://huggingface.co/oncu/X-Ray_Alpha-GGUF/resolve/main/x-ray_alpha-q2_k.gguf) \| ![Q2_K](https://img.shields.io/badge/Q2_K-1A73E8) \| Basic quantization \|
	\| [Download](https://huggingface.co/oncu/X-Ray_Alpha-GGUF/resolve/main/x-ray_alpha-q3_k_s.gguf) \| ![Q3_K_S](https://img.shields.io/badge/Q3_K_S-34A853) \| Small size \|
	\| [Download](https://huggingface.co/oncu/X-Ray_Alpha-GGUF/resolve/main/x-ray_alpha-q3_k_m.gguf) \| ![Q3_K_M](https://img.shields.io/badge/Q3_K_M-FBBC05) \| Balanced quality \|
	\| [Download](https://huggingface.co/oncu/X-Ray_Alpha-GGUF/resolve/main/x-ray_alpha-q3_k_l.gguf) \| ![Q3_K_L](https://img.shields.io/badge/Q3_K_L-4285F4) \| Better quality \|
	\| [Download](https://huggingface.co/oncu/X-Ray_Alpha-GGUF/resolve/main/x-ray_alpha-q4_0.gguf) \| ![Q4_0](https://img.shields.io/badge/Q4_0-EA4335) \| Fast on ARM \|
	\| [Download](https://huggingface.co/oncu/X-Ray_Alpha-GGUF/resolve/main/x-ray_alpha-q4_k_s.gguf) \| ![Q4_K_S](https://img.shields.io/badge/Q4_K_S-673AB7) \| Fast, recommended \|
	\| [Download](https://huggingface.co/oncu/X-Ray_Alpha-GGUF/resolve/main/x-ray_alpha-q4_k_m.gguf) \| ![Q4_K_M](https://img.shields.io/badge/Q4_K_M-673AB7) ⭐ \| Best balance \|
	\| [Download](https://huggingface.co/oncu/X-Ray_Alpha-GGUF/resolve/main/x-ray_alpha-q5_0.gguf) \| ![Q5_0](https://img.shields.io/badge/Q5_0-FF6D01) \| Good quality \|
	\| [Download](https://huggingface.co/oncu/X-Ray_Alpha-GGUF/resolve/main/x-ray_alpha-q5_k_s.gguf) \| ![Q5_K_S](https://img.shields.io/badge/Q5_K_S-0F9D58) \| Balanced \|
	\| [Download](https://huggingface.co/oncu/X-Ray_Alpha-GGUF/resolve/main/x-ray_alpha-q5_k_m.gguf) \| ![Q5_K_M](https://img.shields.io/badge/Q5_K_M-0F9D58) \| High quality \|
	\| [Download](https://huggingface.co/oncu/X-Ray_Alpha-GGUF/resolve/main/x-ray_alpha-q6_k.gguf) \| ![Q6_K](https://img.shields.io/badge/Q6_K-4285F4) 🏆 \| Very good quality \|
	\| [Download](https://huggingface.co/oncu/X-Ray_Alpha-GGUF/resolve/main/x-ray_alpha-q8_0.gguf) \| ![Q8_0](https://img.shields.io/badge/Q8_0-EA4335) ⚡ \| Fast, best quality \|
	\| [Download](https://huggingface.co/oncu/X-Ray_Alpha-GGUF/resolve/main/x-ray_alpha-f16.gguf) \| ![F16](https://img.shields.io/badge/F16-000000) \| Maximum accuracy \|

	💡 Tip: Use `F16` for maximum precision when quality is critical


	---
	# 🚀 Applications and Tools for Locally Quantized LLMs
	## 🖥️ Desktop Applications

	\| Application \| Description \| Download Link \|
	\|-----------------\|----------------------------------------------------------------------------------------------\|--------------------------------------------------------------------------------\|
	\| Llama.cpp \| A fast and efficient inference engine for GGUF models. \| [GitHub Repository](https://github.com/ggml-org/llama.cpp) \|
	\| Ollama \| A streamlined solution for running LLMs locally. \| [Website](https://ollama.com/) \|
	\| AnythingLLM \| An AI-powered knowledge management tool. \| [GitHub Repository](https://github.com/Mintplex-Labs/anything-llm) \|
	\| Open WebUI \| A user-friendly web interface for running local LLMs. \| [GitHub Repository](https://github.com/open-webui/open-webui) \|
	\| GPT4All \| A user-friendly desktop application supporting various LLMs, compatible with GGUF models. \| [GitHub Repository](https://github.com/nomic-ai/gpt4all) \|
	\| LM Studio \| A desktop application designed to run and manage local LLMs, supporting GGUF format. \| [Website](https://lmstudio.ai/) \|
	\| GPT4All Chat\| A chat application compatible with GGUF models for local, offline interactions. \| [GitHub Repository](https://github.com/nomic-ai/gpt4all) \|

	---

	## 📱 Mobile Applications

	\| Application \| Description \| Download Link \|
	\|-------------------\|----------------------------------------------------------------------------------------------\|--------------------------------------------------------------------------------\|
	\| ChatterUI \| A simple and lightweight LLM app for mobile devices. \| [GitHub Repository](https://github.com/Vali-98/ChatterUI) \|
	\| Maid \| Mobile Artificial Intelligence Distribution for running AI models on mobile devices. \| [GitHub Repository](https://github.com/Mobile-Artificial-Intelligence/maid) \|
	\| PocketPal AI \| A mobile AI assistant powered by local models. \| [GitHub Repository](https://github.com/a-ghorbani/pocketpal-ai) \|
	\| Layla \| A flexible platform for running various AI models on mobile devices. \| [Website](https://www.layla-network.ai/) \|

	---

	## 🎨 Image Generation Applications

	\| Application \| Description \| Download Link \|
	\|-------------------------------------\|----------------------------------------------------------------------------------------------\|--------------------------------------------------------------------------------\|
	\| Stable Diffusion \| An open-source AI model for generating images from text. \| [GitHub Repository](https://github.com/CompVis/stable-diffusion) \|
	\| Stable Diffusion WebUI \| A web application providing access to Stable Diffusion models via a browser interface. \| [GitHub Repository](https://github.com/AUTOMATIC1111/stable-diffusion-webui) \|
	\| Local Dream \| Android Stable Diffusion with Snapdragon NPU acceleration. Also supports CPU inference. \| [GitHub Repository](https://github.com/xororz/local-dream) \|
	\| Stable-Diffusion-Android (SDAI) \| An open-source AI art application for Android devices, enabling digital art creation. \| [GitHub Repository](https://github.com/ShiftHackZ/Stable-Diffusion-Android) \|

	---