Instructions to use hipinis/20260718 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use hipinis/20260718 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 hipinis/20260718:Q8_0
# Run inference directly in the terminal:
llama cli -hf hipinis/20260718:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf hipinis/20260718:Q8_0
# Run inference directly in the terminal:
llama cli -hf hipinis/20260718:Q8_0

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf hipinis/20260718:Q8_0
# Run inference directly in the terminal:
./build/bin/llama cli -hf hipinis/20260718:Q8_0

Use Docker

docker model run hf.co/hipinis/20260718:Q8_0

Llama new
LM Studio
Jan
Ollama
How to use hipinis/20260718 with Ollama:
```
ollama run hf.co/hipinis/20260718:Q8_0
```
Unsloth Desktop

How to use hipinis/20260718 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf hipinis/20260718:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "hipinis/20260718:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use hipinis/20260718 with Docker Model Runner:
```
docker model run hf.co/hipinis/20260718:Q8_0
```

Lemonade

How to use hipinis/20260718 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull hipinis/20260718:Q8_0

Run and chat with the model

lemonade run user.20260718-Q8_0

List all available models

lemonade list

Hermes Agent

How to use hipinis/20260718 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf hipinis/20260718:Q8_0

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 hipinis/20260718:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use hipinis/20260718 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf hipinis/20260718:Q8_0

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 "hipinis/20260718:Q8_0" \
  --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"

20260718 / custom_nodes /ComfyUI-Custom-Scripts /py /repeater.py

hipinis

Upload 1130 files

f859a6f verified 3 months ago

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# 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("*")


class Repeater:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {
            "source": (any, {}),
            "repeats": ("INT", {"min": 0, "max": 5000, "default": 2}),
            "output": (["single", "multi"], {}),
            "node_mode": (["reuse", "create"], {}),
        }}

    RETURN_TYPES = (any,)
    FUNCTION = "repeat"
    OUTPUT_NODE = False
    OUTPUT_IS_LIST = (True,)

    CATEGORY = "utils"

    def repeat(self, repeats, output, node_mode, **kwargs):
        if output == "multi":
            # Multi outputs are split to indiviual nodes on the frontend when serializing
            return ([kwargs["source"]],)
        elif node_mode == "reuse":
            # When reusing we have a single input node, repeat that N times
            return ([kwargs["source"]] * repeats,)
        else:
            # When creating new nodes, they'll be added dynamically when the graph is serialized
            return ((list(kwargs.values())),)


NODE_CLASS_MAPPINGS = {
    "Repeater|pysssss": Repeater,
}

NODE_DISPLAY_NAME_MAPPINGS = {
    "Repeater|pysssss": "Repeater 🐍",
}

	# 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("*")


	class Repeater:
	@classmethod
	def INPUT_TYPES(s):
	return {"required": {
	"source": (any, {}),
	"repeats": ("INT", {"min": 0, "max": 5000, "default": 2}),
	"output": (["single", "multi"], {}),
	"node_mode": (["reuse", "create"], {}),
	}}

	RETURN_TYPES = (any,)
	FUNCTION = "repeat"
	OUTPUT_NODE = False
	OUTPUT_IS_LIST = (True,)

	CATEGORY = "utils"

	def repeat(self, repeats, output, node_mode, **kwargs):
	if output == "multi":
	# Multi outputs are split to indiviual nodes on the frontend when serializing
	return ([kwargs["source"]],)
	elif node_mode == "reuse":
	# When reusing we have a single input node, repeat that N times
	return ([kwargs["source"]] * repeats,)
	else:
	# When creating new nodes, they'll be added dynamically when the graph is serialized
	return ((list(kwargs.values())),)


	NODE_CLASS_MAPPINGS = {
	"Repeater\|pysssss": Repeater,
	}

	NODE_DISPLAY_NAME_MAPPINGS = {
	"Repeater\|pysssss": "Repeater 🐍",
	}