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

Libraries

How to use kukidevalml/LORAs with Diffusers:

pip install -U diffusers transformers accelerate

import torch
from diffusers import DiffusionPipeline

# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("HuggingFaceH4/zephyr-7b-beta", torch_dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("kukidevalml/LORAs")

prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]

llama-cpp-python

How to use kukidevalml/LORAs with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="kukidevalml/LORAs",
	filename="aiorbust/nsfw/Qwen3-4b-Z-Image-Engineer-V4-F16.gguf",
)

llm.create_chat_completion(
	messages = "\"Astronaut riding a horse\""
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use kukidevalml/LORAs 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 kukidevalml/LORAs:F16
# Run inference directly in the terminal:
llama cli -hf kukidevalml/LORAs:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf kukidevalml/LORAs:F16
# Run inference directly in the terminal:
llama cli -hf kukidevalml/LORAs:F16

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 kukidevalml/LORAs:F16
# Run inference directly in the terminal:
./llama-cli -hf kukidevalml/LORAs:F16

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 kukidevalml/LORAs:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kukidevalml/LORAs:F16

Use Docker

docker model run hf.co/kukidevalml/LORAs:F16

LM Studio
Jan
Draw Things
DiffusionBee
Ollama
How to use kukidevalml/LORAs with Ollama:
```
ollama run hf.co/kukidevalml/LORAs:F16
```

Unsloth Studio

How to use kukidevalml/LORAs 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 kukidevalml/LORAs 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 kukidevalml/LORAs to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use kukidevalml/LORAs with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kukidevalml/LORAs:F16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "kukidevalml/LORAs:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use kukidevalml/LORAs with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kukidevalml/LORAs:F16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default kukidevalml/LORAs:F16

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use kukidevalml/LORAs with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kukidevalml/LORAs:F16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "kukidevalml/LORAs:F16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use kukidevalml/LORAs with Docker Model Runner:
```
docker model run hf.co/kukidevalml/LORAs:F16
```

Lemonade

How to use kukidevalml/LORAs with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kukidevalml/LORAs:F16

Run and chat with the model

lemonade run user.LORAs-F16

List all available models

lemonade list

Loras

This directory contains LoRA models fine-tuned on various datasets using the Diffusers LoRA Trainer

Downloads last month: 258

Safetensors

Model size

1B params

Tensor type

F16

Model tree for kukidevalml/LORAs

Base model

mistralai/Mistral-7B-v0.1

Finetuned

HuggingFaceH4/zephyr-7b-beta

Adapter

(415)

this model