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 Settings
- llama.cpp
How to use MidnightRunner/Misc 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 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 serve -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
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
- Atomic Chat new
- 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
File size: 1,602 Bytes
5ceff78 9b3bd44 5ceff78 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 | ---
license: creativeml-openrail-m
language:
- en
base_model: []
pipeline_tag: other
tags:
- upscaler
- denoiser
- comfyui
- automatic1111
datasets: []
metrics: []
---
# 🗂 MidnightRunner/Misc
## Overview
This repo is my **miscellaneous toolbox** — a collection of models, upscalers, denoisers, configs, and other bits I keep around for quick pulls.
It’s 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
- “Just in case” helper models
---
## Quick Pulls
Fetch files or the entire repo with Hugging Face tools:
```bash
# 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. |