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 io | |
| import os | |
| from contextlib import contextmanager | |
| from dataclasses import dataclass | |
| from typing import IO, Any, Callable, Iterator | |
| import logging | |
| try: | |
| from blake3 import blake3 | |
| except ModuleNotFoundError: | |
| logging.warning("WARNING: blake3 package not installed") | |
| DEFAULT_CHUNK = 8 * 1024 * 1024 | |
| InterruptCheck = Callable[[], bool] | |
| class HashCheckpoint: | |
| """Saved state for resuming an interrupted hash computation.""" | |
| bytes_processed: int | |
| hasher: Any # blake3 hasher instance | |
| mtime_ns: int = 0 | |
| file_size: int = 0 | |
| def _open_for_hashing(fp: str | IO[bytes]) -> Iterator[tuple[IO[bytes], bool]]: | |
| """Yield (file_object, is_path) with appropriate setup/teardown.""" | |
| if hasattr(fp, "read"): | |
| seekable = getattr(fp, "seekable", lambda: False)() | |
| orig_pos = None | |
| if seekable: | |
| try: | |
| orig_pos = fp.tell() | |
| if orig_pos != 0: | |
| fp.seek(0) | |
| except io.UnsupportedOperation: | |
| orig_pos = None | |
| try: | |
| yield fp, False | |
| finally: | |
| if orig_pos is not None: | |
| fp.seek(orig_pos) | |
| else: | |
| with open(os.fspath(fp), "rb") as f: | |
| yield f, True | |
| def compute_blake3_hash( | |
| fp: str | IO[bytes], | |
| chunk_size: int = DEFAULT_CHUNK, | |
| interrupt_check: InterruptCheck | None = None, | |
| checkpoint: HashCheckpoint | None = None, | |
| ) -> tuple[str | None, HashCheckpoint | None]: | |
| """Compute BLAKE3 hash of a file, with optional checkpoint support. | |
| Args: | |
| fp: File path or file-like object | |
| chunk_size: Size of chunks to read at a time | |
| interrupt_check: Optional callable that returns True if the operation | |
| should be interrupted (e.g. paused or cancelled). Must be | |
| non-blocking so file handles are released immediately. Checked | |
| between chunk reads. | |
| checkpoint: Optional checkpoint to resume from (file paths only) | |
| Returns: | |
| Tuple of (hex_digest, None) on completion, or | |
| (None, checkpoint) on interruption (file paths only), or | |
| (None, None) on interruption of a file object | |
| """ | |
| if chunk_size <= 0: | |
| chunk_size = DEFAULT_CHUNK | |
| with _open_for_hashing(fp) as (f, is_path): | |
| if checkpoint is not None and is_path: | |
| f.seek(checkpoint.bytes_processed) | |
| h = checkpoint.hasher | |
| bytes_processed = checkpoint.bytes_processed | |
| else: | |
| h = blake3() | |
| bytes_processed = 0 | |
| while True: | |
| if interrupt_check is not None and interrupt_check(): | |
| if is_path: | |
| return None, HashCheckpoint( | |
| bytes_processed=bytes_processed, | |
| hasher=h, | |
| ) | |
| return None, None | |
| chunk = f.read(chunk_size) | |
| if not chunk: | |
| break | |
| h.update(chunk) | |
| bytes_processed += len(chunk) | |
| return h.hexdigest(), None | |