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
| """Shared utilities for database query modules.""" | |
| import os | |
| from decimal import Decimal | |
| from typing import Iterable, Sequence | |
| import sqlalchemy as sa | |
| from sqlalchemy import exists | |
| from app.assets.database.models import AssetReference, AssetReferenceMeta, AssetReferenceTag | |
| from app.assets.helpers import escape_sql_like_string, normalize_tags | |
| MAX_BIND_PARAMS = 800 | |
| def calculate_rows_per_statement(cols: int) -> int: | |
| """Calculate how many rows can fit in one statement given column count.""" | |
| return max(1, MAX_BIND_PARAMS // max(1, cols)) | |
| def iter_chunks(seq, n: int): | |
| """Yield successive n-sized chunks from seq.""" | |
| for i in range(0, len(seq), n): | |
| yield seq[i : i + n] | |
| def iter_row_chunks(rows: list[dict], cols_per_row: int) -> Iterable[list[dict]]: | |
| """Yield chunks of rows sized to fit within bind param limits.""" | |
| if not rows: | |
| return | |
| yield from iter_chunks(rows, calculate_rows_per_statement(cols_per_row)) | |
| def build_visible_owner_clause(owner_id: str) -> sa.sql.ClauseElement: | |
| """Build owner visibility predicate for reads. | |
| Owner-less rows are visible to everyone. | |
| """ | |
| owner_id = (owner_id or "").strip() | |
| if owner_id == "": | |
| return AssetReference.owner_id == "" | |
| return AssetReference.owner_id.in_(["", owner_id]) | |
| def build_prefix_like_conditions( | |
| prefixes: list[str], | |
| ) -> list[sa.sql.ColumnElement]: | |
| """Build LIKE conditions for matching file paths under directory prefixes.""" | |
| conds = [] | |
| for p in prefixes: | |
| base = os.path.abspath(p) | |
| if not base.endswith(os.sep): | |
| base += os.sep | |
| escaped, esc = escape_sql_like_string(base) | |
| conds.append(AssetReference.file_path.like(escaped + "%", escape=esc)) | |
| return conds | |
| def apply_tag_filters( | |
| stmt: sa.sql.Select, | |
| include_tags: Sequence[str] | None = None, | |
| exclude_tags: Sequence[str] | None = None, | |
| ) -> sa.sql.Select: | |
| """include_tags: every tag must be present; exclude_tags: none may be present.""" | |
| include_tags = normalize_tags(include_tags) | |
| exclude_tags = normalize_tags(exclude_tags) | |
| if include_tags: | |
| for tag_name in include_tags: | |
| stmt = stmt.where( | |
| exists().where( | |
| (AssetReferenceTag.asset_reference_id == AssetReference.id) | |
| & (AssetReferenceTag.tag_name == tag_name) | |
| ) | |
| ) | |
| if exclude_tags: | |
| stmt = stmt.where( | |
| ~exists().where( | |
| (AssetReferenceTag.asset_reference_id == AssetReference.id) | |
| & (AssetReferenceTag.tag_name.in_(exclude_tags)) | |
| ) | |
| ) | |
| return stmt | |
| def apply_metadata_filter( | |
| stmt: sa.sql.Select, | |
| metadata_filter: dict | None = None, | |
| ) -> sa.sql.Select: | |
| """Apply filters using asset_reference_meta projection table.""" | |
| if not metadata_filter: | |
| return stmt | |
| def _exists_for_pred(key: str, *preds) -> sa.sql.ClauseElement: | |
| return sa.exists().where( | |
| AssetReferenceMeta.asset_reference_id == AssetReference.id, | |
| AssetReferenceMeta.key == key, | |
| *preds, | |
| ) | |
| def _exists_clause_for_value(key: str, value) -> sa.sql.ClauseElement: | |
| if value is None: | |
| return sa.not_( | |
| sa.exists().where( | |
| AssetReferenceMeta.asset_reference_id == AssetReference.id, | |
| AssetReferenceMeta.key == key, | |
| ) | |
| ) | |
| if isinstance(value, bool): | |
| return _exists_for_pred(key, AssetReferenceMeta.val_bool == bool(value)) | |
| if isinstance(value, (int, float, Decimal)): | |
| num = value if isinstance(value, Decimal) else Decimal(str(value)) | |
| return _exists_for_pred(key, AssetReferenceMeta.val_num == num) | |
| if isinstance(value, str): | |
| return _exists_for_pred(key, AssetReferenceMeta.val_str == value) | |
| return _exists_for_pred(key, AssetReferenceMeta.val_json == value) | |
| for k, v in metadata_filter.items(): | |
| if isinstance(v, list): | |
| ors = [_exists_clause_for_value(k, elem) for elem in v] | |
| if ors: | |
| stmt = stmt.where(sa.or_(*ors)) | |
| else: | |
| stmt = stmt.where(_exists_clause_for_value(k, v)) | |
| return stmt | |