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
| /** | |
| * @fileoverview A set of methods that mimic a bit of the Jasmine testing library, but simpler and | |
| * more succinct for manipulating a comfy integration test. | |
| * | |
| * Tests are not bundled by default, to test build with "--with-tests" and then invoke from the | |
| * dev console like `rgthree_tests.TestDescribeLabel()`. The output is in the test itself. | |
| */ | |
| import { wait } from "rgthree/common/shared_utils.js"; | |
| declare global { | |
| interface Window { | |
| rgthree_tests: { | |
| [key: string]: any; | |
| }; | |
| } | |
| } | |
| window.rgthree_tests = window.rgthree_tests || {}; | |
| type TestContext = { | |
| label?: string; | |
| beforeEach?: Function[]; | |
| }; | |
| let contexts: TestContext[] = []; | |
| export function describe(label: string, fn: Function) { | |
| if (!label.startsWith('Test')) { | |
| throw new Error('Test labels should start with "Test"'); | |
| } | |
| window.rgthree_tests[label] = async () => { | |
| await describeRun(label, fn); | |
| }; | |
| return window.rgthree_tests[label]; | |
| } | |
| export async function describeRun(label: string, fn: Function) { | |
| await wait(); | |
| contexts.push({ label }); | |
| console.group(`[Start] ${contexts[contexts.length - 1]!.label}`); | |
| await fn(); | |
| contexts.pop(); | |
| console.groupEnd(); | |
| } | |
| export async function should(declaration: string, fn: Function) { | |
| if (!contexts[contexts.length - 1]) { | |
| throw Error("Called should outside of a describe."); | |
| } | |
| console.group(`...should ${declaration}`); | |
| try { | |
| for (const context of contexts) { | |
| for (const beforeEachFn of context?.beforeEach || []) { | |
| await beforeEachFn(); | |
| } | |
| } | |
| await fn(); | |
| } catch (e: any) { | |
| fail(e); | |
| } | |
| console.groupEnd(); | |
| } | |
| export async function beforeEach(fn: Function) { | |
| if (!contexts[contexts.length - 1]) { | |
| throw Error("Called beforeEach outside of a describe."); | |
| } | |
| const last = contexts[contexts.length - 1]!; | |
| last.beforeEach = last?.beforeEach || []; | |
| last.beforeEach.push(fn); | |
| } | |
| export function fail(e: Error) { | |
| log(`X Failure: ${e}`, "color:#600; background:#fdd; padding: 2px 6px;"); | |
| } | |
| function log(msg: string, styles: string) { | |
| if (styles) { | |
| console.log(`%c ${msg}`, styles); | |
| } else { | |
| console.log(msg); | |
| } | |
| } | |
| class Expectation { | |
| private propertyLabel: string | null = ""; | |
| private expectedLabel: string | null = ""; | |
| private verbLabel: string | null = "be"; | |
| private expectedFn!: (v: any) => boolean; | |
| private value: any; | |
| constructor(value: any) { | |
| this.value = value; | |
| } | |
| toBe(labelOrExpected: any, maybeExpected?: any) { | |
| const expected = maybeExpected !== undefined ? maybeExpected : labelOrExpected; | |
| this.propertyLabel = maybeExpected !== undefined ? labelOrExpected : null; | |
| this.expectedLabel = JSON.stringify(expected); | |
| this.expectedFn = (v) => v == expected; | |
| return this.toBeEval(); | |
| } | |
| toMatchJson(labelOrExpected: any, maybeExpected?: any) { | |
| const expected = maybeExpected !== undefined ? maybeExpected : labelOrExpected; | |
| this.propertyLabel = maybeExpected !== undefined ? labelOrExpected : null; | |
| this.expectedLabel = JSON.stringify(expected); | |
| this.expectedFn = (v) => JSON.stringify(JSON.parse(v)) == JSON.stringify(JSON.parse(expected)); | |
| return this.toBeEval(); | |
| } | |
| toBeUndefined(propertyLabel: string) { | |
| this.expectedFn = (v) => v === undefined; | |
| this.propertyLabel = propertyLabel || ""; | |
| this.expectedLabel = "undefined"; | |
| return this.toBeEval(true); | |
| } | |
| toBeNullOrUndefined(propertyLabel: string) { | |
| this.expectedFn = (v) => v == null; | |
| this.propertyLabel = propertyLabel || ""; | |
| this.expectedLabel = "null or undefined"; | |
| return this.toBeEval(true); | |
| } | |
| toBeTruthy(propertyLabel: string) { | |
| this.expectedFn = (v) => !v; | |
| this.propertyLabel = propertyLabel || ""; | |
| this.expectedLabel = "truthy"; | |
| return this.toBeEval(false); | |
| } | |
| toBeANumber(propertyLabel: string) { | |
| this.expectedFn = (v) => typeof v === "number"; | |
| this.propertyLabel = propertyLabel || ""; | |
| this.expectedLabel = "a number"; | |
| return this.toBeEval(); | |
| } | |
| toContain(labelOrExpected: any, maybeExpected?: any) { | |
| const expected = maybeExpected !== undefined ? maybeExpected : labelOrExpected; | |
| this.propertyLabel = maybeExpected !== undefined ? labelOrExpected : null; | |
| this.verbLabel = 'contain'; | |
| this.expectedLabel = JSON.stringify(expected); | |
| this.expectedFn = (v) => v.includes(expected); | |
| return this.toBeEval(); | |
| } | |
| toBeEval(strict = false) { | |
| let evaluation = this.expectedFn(this.value); | |
| let msg = `Expected ${this.propertyLabel ? this.propertyLabel + ` to ${this.verbLabel} ` : ""}${ | |
| this.expectedLabel | |
| }`; | |
| msg += evaluation ? "." : `, but was ${JSON.stringify(this.value)}`; | |
| this.log(evaluation, msg); | |
| return evaluation; | |
| } | |
| log(value: boolean, msg: string) { | |
| if (value) { | |
| log(`🗸 ${msg}`, "color:#060; background:#cec; padding: 2px 6px;"); | |
| } else { | |
| log(`X ${msg}`, "color:#600; background:#fdd; padding: 2px 6px;"); | |
| } | |
| } | |
| } | |
| export function expect(value: any, msg?: string) { | |
| const expectation = new Expectation(value); | |
| if (msg) { | |
| expectation.log(value, msg); | |
| } | |
| return expectation; | |
| } | |