Instructions to use saik0s/comfy_backup with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use saik0s/comfy_backup with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="saik0s/comfy_backup", filename="ComfyUI/models/text_encoders/gemma-3-12b-it-q2_k.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 saik0s/comfy_backup 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 saik0s/comfy_backup:Q4_K_S # Run inference directly in the terminal: llama cli -hf saik0s/comfy_backup:Q4_K_S
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf saik0s/comfy_backup:Q4_K_S # Run inference directly in the terminal: llama cli -hf saik0s/comfy_backup: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 saik0s/comfy_backup:Q4_K_S # Run inference directly in the terminal: ./llama-cli -hf saik0s/comfy_backup: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 saik0s/comfy_backup:Q4_K_S # Run inference directly in the terminal: ./build/bin/llama-cli -hf saik0s/comfy_backup:Q4_K_S
Use Docker
docker model run hf.co/saik0s/comfy_backup:Q4_K_S
- LM Studio
- Jan
- Ollama
How to use saik0s/comfy_backup with Ollama:
ollama run hf.co/saik0s/comfy_backup:Q4_K_S
- Unsloth Studio
How to use saik0s/comfy_backup 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 saik0s/comfy_backup 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 saik0s/comfy_backup to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for saik0s/comfy_backup to start chatting
- Pi
How to use saik0s/comfy_backup with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf saik0s/comfy_backup:Q4_K_S
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": "saik0s/comfy_backup:Q4_K_S" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use saik0s/comfy_backup with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf saik0s/comfy_backup:Q4_K_S
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 saik0s/comfy_backup:Q4_K_S
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use saik0s/comfy_backup with Docker Model Runner:
docker model run hf.co/saik0s/comfy_backup:Q4_K_S
- Lemonade
How to use saik0s/comfy_backup with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull saik0s/comfy_backup:Q4_K_S
Run and chat with the model
lemonade run user.comfy_backup-Q4_K_S
List all available models
lemonade list
| from server import PromptServer | |
| from aiohttp import web | |
| import os | |
| import inspect | |
| import json | |
| import importlib | |
| import sys | |
| sys.path.insert(0, os.path.dirname(os.path.dirname(__file__))) | |
| import pysssss | |
| root_directory = os.path.dirname(inspect.getfile(PromptServer)) | |
| workflows_directory = os.path.join(root_directory, "pysssss-workflows") | |
| workflows_directory = pysssss.get_config_value( | |
| "workflows.directory", workflows_directory) | |
| if not os.path.isabs(workflows_directory): | |
| workflows_directory = os.path.abspath(os.path.join(root_directory, workflows_directory)) | |
| NODE_CLASS_MAPPINGS = {} | |
| NODE_DISPLAY_NAME_MAPPINGS = {} | |
| async def get_workflows(request): | |
| files = [] | |
| for dirpath, directories, file in os.walk(workflows_directory): | |
| for file in file: | |
| if (file.endswith(".json")): | |
| files.append(os.path.relpath(os.path.join( | |
| dirpath, file), workflows_directory)) | |
| return web.json_response(list(map(lambda f: os.path.splitext(f)[0].replace("\\", "/"), files))) | |
| async def get_workflow(request): | |
| file = os.path.abspath(os.path.join( | |
| workflows_directory, request.match_info["name"] + ".json")) | |
| if os.path.commonpath([file, workflows_directory]) != workflows_directory: | |
| return web.Response(status=403) | |
| return web.FileResponse(file) | |
| async def save_workflow(request): | |
| json_data = await request.json() | |
| file = os.path.abspath(os.path.join( | |
| workflows_directory, json_data["name"] + ".json")) | |
| if os.path.commonpath([file, workflows_directory]) != workflows_directory: | |
| return web.Response(status=403) | |
| if os.path.exists(file) and ("overwrite" not in json_data or json_data["overwrite"] == False): | |
| return web.Response(status=409) | |
| sub_path = os.path.dirname(file) | |
| if not os.path.exists(sub_path): | |
| os.makedirs(sub_path) | |
| with open(file, "w") as f: | |
| f.write(json.dumps(json_data["workflow"])) | |
| return web.Response(status=201) | |