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
File size: 8,623 Bytes
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import math
import random
import operator as op
# Hack: string type that is always equal in not equal comparisons
class AnyType(str):
def __ne__(self, __value: object) -> bool:
return False
# Our any instance wants to be a wildcard string
any = AnyType("*")
operators = {
ast.Add: op.add,
ast.Sub: op.sub,
ast.Mult: op.mul,
ast.Div: op.truediv,
ast.FloorDiv: op.floordiv,
ast.Pow: op.pow,
ast.BitXor: op.xor,
ast.USub: op.neg,
ast.Mod: op.mod,
ast.BitAnd: op.and_,
ast.BitOr: op.or_,
ast.Invert: op.invert,
ast.And: lambda a, b: 1 if a and b else 0,
ast.Or: lambda a, b: 1 if a or b else 0,
ast.Not: lambda a: 0 if a else 1,
ast.RShift: op.rshift,
ast.LShift: op.lshift
}
# TODO: restructure args to provide more info, generate hint based on args to save duplication
functions = {
"round": {
"args": (1, 2),
"call": lambda a, b = None: round(a, b),
"hint": "number, dp? = 0"
},
"ceil": {
"args": (1, 1),
"call": lambda a: math.ceil(a),
"hint": "number"
},
"floor": {
"args": (1, 1),
"call": lambda a: math.floor(a),
"hint": "number"
},
"min": {
"args": (2, None),
"call": lambda *args: min(*args),
"hint": "...numbers"
},
"max": {
"args": (2, None),
"call": lambda *args: max(*args),
"hint": "...numbers"
},
"randomint": {
"args": (2, 2),
"call": lambda a, b: random.randint(a, b),
"hint": "min, max"
},
"randomchoice": {
"args": (2, None),
"call": lambda *args: random.choice(args),
"hint": "...numbers"
},
"sqrt": {
"args": (1, 1),
"call": lambda a: math.sqrt(a),
"hint": "number"
},
"int": {
"args": (1, 1),
"call": lambda a = None: int(a),
"hint": "number"
},
"iif": {
"args": (3, 3),
"call": lambda a, b, c = None: b if a else c,
"hint": "value, truepart, falsepart"
},
}
autocompleteWords = list({
"text": x,
"value": f"{x}()",
"showValue": False,
"hint": f"{functions[x]['hint']}",
"caretOffset": -1
} for x in functions.keys())
class MathExpression:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"expression": ("STRING", {"multiline": True, "dynamicPrompts": False, "pysssss.autocomplete": {
"words": autocompleteWords,
"separator": ""
}}),
},
"optional": {
"a": (any, ),
"b": (any,),
"c": (any, ),
},
"hidden": {"extra_pnginfo": "EXTRA_PNGINFO",
"prompt": "PROMPT"},
}
RETURN_TYPES = ("INT", "FLOAT", )
FUNCTION = "evaluate"
CATEGORY = "utils"
OUTPUT_NODE = True
@classmethod
def IS_CHANGED(s, expression, **kwargs):
if "random" in expression:
return float("nan")
return expression
def get_widget_value(self, extra_pnginfo, prompt, node_name, widget_name):
workflow = extra_pnginfo["workflow"] if "workflow" in extra_pnginfo else { "nodes": [] }
node_id = None
for node in workflow["nodes"]:
name = node["type"]
if "properties" in node:
if "Node name for S&R" in node["properties"]:
name = node["properties"]["Node name for S&R"]
if name == node_name:
node_id = node["id"]
break
if "title" in node:
name = node["title"]
if name == node_name:
node_id = node["id"]
break
if node_id is not None:
values = prompt[str(node_id)]
if "inputs" in values:
if widget_name in values["inputs"]:
value = values["inputs"][widget_name]
if isinstance(value, list):
raise ValueError("Converted widgets are not supported via named reference, use the inputs instead.")
return value
raise NameError(f"Widget not found: {node_name}.{widget_name}")
raise NameError(f"Node not found: {node_name}.{widget_name}")
def get_size(self, target, property):
if isinstance(target, dict) and "samples" in target:
# Latent
if property == "width":
return target["samples"].shape[3] * 8
return target["samples"].shape[2] * 8
else:
# Image
if property == "width":
return target.shape[2]
return target.shape[1]
def evaluate(self, expression, prompt, extra_pnginfo={}, a=None, b=None, c=None):
expression = expression.replace('\n', ' ').replace('\r', '')
node = ast.parse(expression, mode='eval').body
lookup = {"a": a, "b": b, "c": c}
def eval_op(node, l, r):
l = eval_expr(l)
r = eval_expr(r)
l = l if isinstance(l, int) else float(l)
r = r if isinstance(r, int) else float(r)
return operators[type(node.op)](l, r)
def eval_expr(node):
if isinstance(node, ast.Constant) or isinstance(node, ast.Num):
return node.n
elif isinstance(node, ast.BinOp):
return eval_op(node, node.left, node.right)
elif isinstance(node, ast.BoolOp):
return eval_op(node, node.values[0], node.values[1])
elif isinstance(node, ast.UnaryOp):
return operators[type(node.op)](eval_expr(node.operand))
elif isinstance(node, ast.Attribute):
if node.value.id in lookup:
if node.attr == "width" or node.attr == "height":
return self.get_size(lookup[node.value.id], node.attr)
return self.get_widget_value(extra_pnginfo, prompt, node.value.id, node.attr)
elif isinstance(node, ast.Name):
if node.id in lookup:
val = lookup[node.id]
if isinstance(val, (int, float, complex)):
return val
else:
raise TypeError(
f"Compex types (LATENT/IMAGE) need to reference their width/height, e.g. {node.id}.width")
raise NameError(f"Name not found: {node.id}")
elif isinstance(node, ast.Call):
if node.func.id in functions:
fn = functions[node.func.id]
l = len(node.args)
if l < fn["args"][0] or (fn["args"][1] is not None and l > fn["args"][1]):
if fn["args"][1] is None:
toErr = " or more"
else:
toErr = f" to {fn['args'][1]}"
raise SyntaxError(
f"Invalid function call: {node.func.id} requires {fn['args'][0]}{toErr} arguments")
args = []
for arg in node.args:
args.append(eval_expr(arg))
return fn["call"](*args)
raise NameError(f"Invalid function call: {node.func.id}")
elif isinstance(node, ast.Compare):
l = eval_expr(node.left)
r = eval_expr(node.comparators[0])
if isinstance(node.ops[0], ast.Eq):
return 1 if l == r else 0
if isinstance(node.ops[0], ast.NotEq):
return 1 if l != r else 0
if isinstance(node.ops[0], ast.Gt):
return 1 if l > r else 0
if isinstance(node.ops[0], ast.GtE):
return 1 if l >= r else 0
if isinstance(node.ops[0], ast.Lt):
return 1 if l < r else 0
if isinstance(node.ops[0], ast.LtE):
return 1 if l <= r else 0
raise NotImplementedError(
"Operator " + node.ops[0].__class__.__name__ + " not supported.")
else:
raise TypeError(node)
r = eval_expr(node)
return {"ui": {"value": [r]}, "result": (int(r), float(r),)}
NODE_CLASS_MAPPINGS = {
"MathExpression|pysssss": MathExpression,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"MathExpression|pysssss": "Math Expression 🐍",
}
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