Instructions to use Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b",
	filename="Super-Slop-Machina-Roleplay-1.2b.BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b 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 Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b:Q4_K_M

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 Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b:Q4_K_M

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 Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b:Q4_K_M

Use Docker

docker model run hf.co/Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b:Q4_K_M

LM Studio
Jan

vLLM

How to use Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b:Q4_K_M

Ollama
How to use Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b with Ollama:
```
ollama run hf.co/Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b:Q4_K_M
```

Unsloth Studio

How to use Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b 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 Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b 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 Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b to start chatting

How to use Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b:Q4_K_M

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": "Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b:Q4_K_M

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 Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b:Q4_K_M

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 "Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b:Q4_K_M" \
  --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 Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b with Docker Model Runner:
```
docker model run hf.co/Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b:Q4_K_M
```

Lemonade

How to use Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b:Q4_K_M

Run and chat with the model

lemonade run user.Super-Slop-Machina-Roleplay-1.2b-Q4_K_M

List all available models

lemonade list

Super Slop Machina: Complex Roleplay Finetune of LiquidAI's LFM 2.5 1.2b instruct.

"Its back, stronger than ever before. Its all yours, on your phone, on your toaster, its always here."

Overview:

This is, at the moment, my most recent finetune on LFM 1.2b, trained on 35M~ tokens, it overall improves on LFM 2.5 1.2b instruct roleplay tuned v2.

In comparison to previous version:

Writes better
Coherence was improved
Maintains creativity

Quants(Speaking from personal experience with this specific model):

BF16- Recommended, highest quality, least logical mistakes.
Q8_0- Recommended, high quality, makes slightly more mistakes but nonetheless near lossless.
Q6_K- Recommended if Q8_0 is too much, degradation begins, not exactly notable here, but you will notice minor detail loss.(When Mradermacher quantizes this model, I recommend getting his i1 Q6_K quant instead of the one I got in my repo, but in any case I still recommend Q8_0 or BF16)
Q5_K_M- Recommended if hardware is really, REALLY bad, degradation becomes noticeable.
Q4_K_M- Not recommended for most use cases, degradation is clearly noticeable.

Quants can be found in the repository, along with safetensors.

Note:

I may improve it further later, I will still have to say that its fundamentally the same 1.2b base model, even with how much I refined its style.

Downloads last month: 919

Safetensors

Model size

1B params

Tensor type

BF16

Model tree for Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b

Base model

LiquidAI/LFM2.5-1.2B-Base

Finetuned

LiquidAI/LFM2.5-1.2B-Instruct

Adapter

(34)

this model

Adapters

2 models