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
- OpenClaw new
How to use saik0s/comfy_backup with OpenClaw:
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 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 "saik0s/comfy_backup:Q4_K_S" \ --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 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
| import hashlib | |
| import json | |
| from aiohttp import web | |
| from server import PromptServer | |
| import folder_paths | |
| import os | |
| def get_metadata(filepath): | |
| with open(filepath, "rb") as file: | |
| # https://github.com/huggingface/safetensors#format | |
| # 8 bytes: N, an unsigned little-endian 64-bit integer, containing the size of the header | |
| header_size = int.from_bytes(file.read(8), "little", signed=False) | |
| if header_size <= 0: | |
| raise BufferError("Invalid header size") | |
| header = file.read(header_size) | |
| if header_size <= 0: | |
| raise BufferError("Invalid header") | |
| header_json = json.loads(header) | |
| return header_json["__metadata__"] if "__metadata__" in header_json else None | |
| async def save_notes(request): | |
| name = request.match_info["name"] | |
| pos = name.index("/") | |
| type = name[0:pos] | |
| name = name[pos+1:] | |
| file_path = None | |
| if type == "embeddings" or type == "loras": | |
| name = name.lower() | |
| files = folder_paths.get_filename_list(type) | |
| for f in files: | |
| lower_f = f.lower() | |
| if lower_f == name: | |
| file_path = folder_paths.get_full_path(type, f) | |
| else: | |
| n = os.path.splitext(f)[0].lower() | |
| if n == name: | |
| file_path = folder_paths.get_full_path(type, f) | |
| if file_path is not None: | |
| break | |
| else: | |
| file_path = folder_paths.get_full_path( | |
| type, name) | |
| if not file_path: | |
| return web.Response(status=404) | |
| file_no_ext = os.path.splitext(file_path)[0] | |
| info_file = file_no_ext + ".txt" | |
| with open(info_file, "w") as f: | |
| f.write(await request.text()) | |
| return web.Response(status=200) | |
| async def load_metadata(request): | |
| name = request.match_info["name"] | |
| pos = name.index("/") | |
| type = name[0:pos] | |
| name = name[pos+1:] | |
| file_path = None | |
| if type == "embeddings" or type == "loras": | |
| name = name.lower() | |
| files = folder_paths.get_filename_list(type) | |
| for f in files: | |
| lower_f = f.lower() | |
| if lower_f == name: | |
| file_path = folder_paths.get_full_path(type, f) | |
| else: | |
| n = os.path.splitext(f)[0].lower() | |
| if n == name: | |
| file_path = folder_paths.get_full_path(type, f) | |
| if file_path is not None: | |
| break | |
| else: | |
| file_path = folder_paths.get_full_path( | |
| type, name) | |
| if not file_path: | |
| return web.Response(status=404) | |
| try: | |
| meta = get_metadata(file_path) | |
| except: | |
| meta = None | |
| if meta is None: | |
| meta = {} | |
| file_no_ext = os.path.splitext(file_path)[0] | |
| info_file = file_no_ext + ".txt" | |
| if os.path.isfile(info_file): | |
| with open(info_file, "r") as f: | |
| meta["pysssss.notes"] = f.read() | |
| hash_file = file_no_ext + ".sha256" | |
| if os.path.isfile(hash_file): | |
| with open(hash_file, "rt") as f: | |
| meta["pysssss.sha256"] = f.read() | |
| else: | |
| with open(file_path, "rb") as f: | |
| meta["pysssss.sha256"] = hashlib.sha256(f.read()).hexdigest() | |
| with open(hash_file, "wt") as f: | |
| f.write(meta["pysssss.sha256"]) | |
| return web.json_response(meta) | |