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
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| "#version 300 es\nprecision highp float;\n\n// Levels Adjustment\n// u_int0: channel (0=RGB, 1=R, 2=G, 3=B) default: 0\n// u_float0: input black (0-255) default: 0\n// u_float1: input white (0-255) default: 255\n// u_float2: gamma (0.01-9.99) default: 1.0\n// u_float3: output black (0-255) default: 0\n// u_float4: output white (0-255) default: 255\n\nuniform sampler2D u_image0;\nuniform int u_int0;\nuniform float u_float0;\nuniform float u_float1;\nuniform float u_float2;\nuniform float u_float3;\nuniform float u_float4;\n\nin vec2 v_texCoord;\nout vec4 fragColor;\n\nvec3 applyLevels(vec3 color, float inBlack, float inWhite, float gamma, float outBlack, float outWhite) {\n float inRange = max(inWhite - inBlack, 0.0001);\n vec3 result = clamp((color - inBlack) / inRange, 0.0, 1.0);\n result = pow(result, vec3(1.0 / gamma));\n result = mix(vec3(outBlack), vec3(outWhite), result);\n return result;\n}\n\nfloat applySingleChannel(float value, float inBlack, float inWhite, float gamma, float outBlack, float outWhite) {\n float inRange = max(inWhite - inBlack, 0.0001);\n float result = clamp((value - inBlack) / inRange, 0.0, 1.0);\n result = pow(result, 1.0 / gamma);\n result = mix(outBlack, outWhite, result);\n return result;\n}\n\nvoid main() {\n vec4 texColor = texture(u_image0, v_texCoord);\n vec3 color = texColor.rgb;\n \n float inBlack = u_float0 / 255.0;\n float inWhite = u_float1 / 255.0;\n float gamma = u_float2;\n float outBlack = u_float3 / 255.0;\n float outWhite = u_float4 / 255.0;\n \n vec3 result;\n \n if (u_int0 == 0) {\n result = applyLevels(color, inBlack, inWhite, gamma, outBlack, outWhite);\n }\n else if (u_int0 == 1) {\n result = color;\n result.r = applySingleChannel(color.r, inBlack, inWhite, gamma, outBlack, outWhite);\n }\n else if (u_int0 == 2) {\n result = color;\n result.g = applySingleChannel(color.g, inBlack, inWhite, gamma, outBlack, outWhite);\n }\n else if (u_int0 == 3) {\n result = color;\n result.b = applySingleChannel(color.b, inBlack, inWhite, gamma, outBlack, outWhite);\n }\n else {\n result = color;\n }\n \n fragColor = vec4(result, texColor.a);\n}", | |
| "from_input" | |
| ] | |
| } | |
| ], | |
| "groups": [], | |
| "links": [ | |
| { | |
| "id": 2, | |
| "origin_id": 3, | |
| "origin_slot": 0, | |
| "target_id": 1, | |
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| "type": "FLOAT" | |
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| "type": "INT" | |
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| { | |
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| "type": "IMAGE" | |
| }, | |
| { | |
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| "target_id": -20, | |
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| "type": "IMAGE" | |
| } | |
| ], | |
| "extra": { | |
| "workflowRendererVersion": "LG" | |
| }, | |
| "category": "Image Tools/Color adjust", | |
| "description": "Adjusts black point, white point, and gamma for tonal range control via GPU shader." | |
| } | |
| ] | |
| }, | |
| "extra": {} | |
| } |