Instructions to use Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b-V4-ERP-Tolerant-Preview 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-V4-ERP-Tolerant-Preview with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b-V4-ERP-Tolerant-Preview")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b-V4-ERP-Tolerant-Preview")
model = AutoModelForCausalLM.from_pretrained("Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b-V4-ERP-Tolerant-Preview", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b-V4-ERP-Tolerant-Preview 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-V4-ERP-Tolerant-Preview:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b-V4-ERP-Tolerant-Preview: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-V4-ERP-Tolerant-Preview:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b-V4-ERP-Tolerant-Preview: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-V4-ERP-Tolerant-Preview:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b-V4-ERP-Tolerant-Preview: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-V4-ERP-Tolerant-Preview:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b-V4-ERP-Tolerant-Preview:Q4_K_M

Use Docker

docker model run hf.co/Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b-V4-ERP-Tolerant-Preview:Q4_K_M

LM Studio
Jan

vLLM

How to use Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b-V4-ERP-Tolerant-Preview 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-V4-ERP-Tolerant-Preview"
# 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-V4-ERP-Tolerant-Preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b-V4-ERP-Tolerant-Preview:Q4_K_M

SGLang

How to use Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b-V4-ERP-Tolerant-Preview with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b-V4-ERP-Tolerant-Preview" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b-V4-ERP-Tolerant-Preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b-V4-ERP-Tolerant-Preview" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b-V4-ERP-Tolerant-Preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b-V4-ERP-Tolerant-Preview with Ollama:
```
ollama run hf.co/Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b-V4-ERP-Tolerant-Preview:Q4_K_M
```

Unsloth Studio

How to use Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b-V4-ERP-Tolerant-Preview 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-V4-ERP-Tolerant-Preview 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-V4-ERP-Tolerant-Preview 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-V4-ERP-Tolerant-Preview to start chatting

How to use Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b-V4-ERP-Tolerant-Preview 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-V4-ERP-Tolerant-Preview: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-V4-ERP-Tolerant-Preview: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-V4-ERP-Tolerant-Preview 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-V4-ERP-Tolerant-Preview: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-V4-ERP-Tolerant-Preview:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b-V4-ERP-Tolerant-Preview 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-V4-ERP-Tolerant-Preview: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-V4-ERP-Tolerant-Preview: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-V4-ERP-Tolerant-Preview with Docker Model Runner:
```
docker model run hf.co/Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b-V4-ERP-Tolerant-Preview:Q4_K_M
```

Lemonade

How to use Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b-V4-ERP-Tolerant-Preview with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b-V4-ERP-Tolerant-Preview:Q4_K_M

Run and chat with the model

lemonade run user.Super-Slop-Machina-Roleplay-1.2b-V4-ERP-Tolerant-Preview-Q4_K_M

List all available models

lemonade list

Super-Slop-Machina-Roleplay-1.2b-V4-ERP-Tolerant-Preview: Early Release of The Super-Slop-Machina-Roleplay-1.2b-V4

"It changed. Became better... stronger... faster... I dunno what else to put here."

Improvements:

That is now a full parameter finetune, I realized that high rank LoRAs that I have been using didnt quite satisfy my needs, so I decided to tone down batch size a little and train on all parameters, it overall improved quality.
Training extended to 101M tokens

And what does that mean?

Way better roleplay than the previous version.

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:

That is a preview, which means I may or may not have more plans for it later, it already took quite a while to train.

Why?

This model exists because I really have nothing else to do and so I decided to finetune small language models to do roleplay, I don't know whether I'm succeeding or no, but I'm constantly learning and determined.

Downloads last month: 6

Safetensors

Model size

1B params

Tensor type

F32

BF16

Model tree for Indexnusrefather/Super-Slop-Machina-Roleplay-1.2b-V4-ERP-Tolerant-Preview

Base model

LiquidAI/LFM2.5-1.2B-Base

Finetuned

LiquidAI/LFM2.5-1.2B-Instruct

Finetuned

(108)

this model