Instructions to use MidnightRunner/Misc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use MidnightRunner/Misc with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="MidnightRunner/Misc", filename="Qwen2.5-VL-7B-Instruct-Q3_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 MidnightRunner/Misc with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf MidnightRunner/Misc:UD-Q4_K_S # Run inference directly in the terminal: llama-cli -hf MidnightRunner/Misc:UD-Q4_K_S
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf MidnightRunner/Misc:UD-Q4_K_S # Run inference directly in the terminal: llama-cli -hf MidnightRunner/Misc:UD-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 MidnightRunner/Misc:UD-Q4_K_S # Run inference directly in the terminal: ./llama-cli -hf MidnightRunner/Misc:UD-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 MidnightRunner/Misc:UD-Q4_K_S # Run inference directly in the terminal: ./build/bin/llama-cli -hf MidnightRunner/Misc:UD-Q4_K_S
Use Docker
docker model run hf.co/MidnightRunner/Misc:UD-Q4_K_S
- LM Studio
- Jan
- Ollama
How to use MidnightRunner/Misc with Ollama:
ollama run hf.co/MidnightRunner/Misc:UD-Q4_K_S
- Unsloth Studio new
How to use MidnightRunner/Misc 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 MidnightRunner/Misc 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 MidnightRunner/Misc to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for MidnightRunner/Misc to start chatting
- Docker Model Runner
How to use MidnightRunner/Misc with Docker Model Runner:
docker model run hf.co/MidnightRunner/Misc:UD-Q4_K_S
- Lemonade
How to use MidnightRunner/Misc with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull MidnightRunner/Misc:UD-Q4_K_S
Run and chat with the model
lemonade run user.Misc-UD-Q4_K_S
List all available models
lemonade list
How to use from
llama.cppInstall from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf MidnightRunner/Misc:# Run inference directly in the terminal:
llama-cli -hf MidnightRunner/Misc: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 MidnightRunner/Misc:# Run inference directly in the terminal:
./llama-cli -hf MidnightRunner/Misc: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 MidnightRunner/Misc:# Run inference directly in the terminal:
./build/bin/llama-cli -hf MidnightRunner/Misc:Use Docker
docker model run hf.co/MidnightRunner/Misc:Quick Links
π MidnightRunner/Misc
Overview
This repo is my miscellaneous toolbox β a collection of models, upscalers, denoisers, configs, and other bits I keep around for quick pulls.
Itβs not meant to be polished, just fast standbys that I can drop into workflows when needed.
Contents
Upscalers
- ESRGAN, RealESRGAN, AnimeSharp, UltraSharp
- SwinIR, NMKD, Foolhardy
Denoisers & Sharpeners
- ITF SkinDiff Detail Lite
- Lexica Sharp series
- DeNoise realplksr
Experimental Checkpoints
- Astraali configs
- OmniSR (x2, x3, x4)
- SAM (Segment Anything) weights
- Motion tests (bounceV, danceMax, etc.)
Workflow Utilities
- FixFP16Errors
- Oddball safetensors
- βJust in caseβ helper models
Quick Pulls
Fetch files or the entire repo with Hugging Face tools:
# clone the whole repo
git lfs install
git clone https://huggingface.co/MidnightRunner/Misc
# download a single file
huggingface-cli download MidnightRunner/Misc 4x-UltraSharp.pth
# pull from Python
from huggingface_hub import hf_hub_download
file = hf_hub_download(
repo_id="MidnightRunner/Misc",
filename="4x-UltraSharp.pth"
)
Notes
- Disorganized on purpose: this is a stash, not a showcase.
- Everything here is tested, works, and has bailed me out more than once.
- Licenses follow their original sources.
- Downloads last month
- 120
Hardware compatibility
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Inference Providers NEW
This model isn't deployed by any Inference Provider. π Ask for provider support
Install from brew
# Start a local OpenAI-compatible server with a web UI: llama-server -hf MidnightRunner/Misc:# Run inference directly in the terminal: llama-cli -hf MidnightRunner/Misc: