Instructions to use Indexnusrefather/Super-Slop-Machina-Micro-Roleplay-Instruct-750M-v1-0812 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Indexnusrefather/Super-Slop-Machina-Micro-Roleplay-Instruct-750M-v1-0812 with Transformers:

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

pipe = pipeline("text-generation", model="Indexnusrefather/Super-Slop-Machina-Micro-Roleplay-Instruct-750M-v1-0812")
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-Micro-Roleplay-Instruct-750M-v1-0812")
model = AutoModelForCausalLM.from_pretrained("Indexnusrefather/Super-Slop-Machina-Micro-Roleplay-Instruct-750M-v1-0812", 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-Micro-Roleplay-Instruct-750M-v1-0812 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-Micro-Roleplay-Instruct-750M-v1-0812:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Indexnusrefather/Super-Slop-Machina-Micro-Roleplay-Instruct-750M-v1-0812: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-Micro-Roleplay-Instruct-750M-v1-0812:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Indexnusrefather/Super-Slop-Machina-Micro-Roleplay-Instruct-750M-v1-0812: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-Micro-Roleplay-Instruct-750M-v1-0812:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Indexnusrefather/Super-Slop-Machina-Micro-Roleplay-Instruct-750M-v1-0812: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-Micro-Roleplay-Instruct-750M-v1-0812:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Indexnusrefather/Super-Slop-Machina-Micro-Roleplay-Instruct-750M-v1-0812:Q4_K_M

Use Docker

docker model run hf.co/Indexnusrefather/Super-Slop-Machina-Micro-Roleplay-Instruct-750M-v1-0812:Q4_K_M

LM Studio
Jan

vLLM

How to use Indexnusrefather/Super-Slop-Machina-Micro-Roleplay-Instruct-750M-v1-0812 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-Micro-Roleplay-Instruct-750M-v1-0812"
# 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-Micro-Roleplay-Instruct-750M-v1-0812",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Indexnusrefather/Super-Slop-Machina-Micro-Roleplay-Instruct-750M-v1-0812:Q4_K_M

SGLang

How to use Indexnusrefather/Super-Slop-Machina-Micro-Roleplay-Instruct-750M-v1-0812 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-Micro-Roleplay-Instruct-750M-v1-0812" \
    --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-Micro-Roleplay-Instruct-750M-v1-0812",
		"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-Micro-Roleplay-Instruct-750M-v1-0812" \
        --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-Micro-Roleplay-Instruct-750M-v1-0812",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Indexnusrefather/Super-Slop-Machina-Micro-Roleplay-Instruct-750M-v1-0812 with Ollama:
```
ollama run hf.co/Indexnusrefather/Super-Slop-Machina-Micro-Roleplay-Instruct-750M-v1-0812:Q4_K_M
```

Unsloth Studio

How to use Indexnusrefather/Super-Slop-Machina-Micro-Roleplay-Instruct-750M-v1-0812 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-Micro-Roleplay-Instruct-750M-v1-0812 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-Micro-Roleplay-Instruct-750M-v1-0812 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-Micro-Roleplay-Instruct-750M-v1-0812 to start chatting

How to use Indexnusrefather/Super-Slop-Machina-Micro-Roleplay-Instruct-750M-v1-0812 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-Micro-Roleplay-Instruct-750M-v1-0812: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-Micro-Roleplay-Instruct-750M-v1-0812:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use Indexnusrefather/Super-Slop-Machina-Micro-Roleplay-Instruct-750M-v1-0812 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-Micro-Roleplay-Instruct-750M-v1-0812: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-Micro-Roleplay-Instruct-750M-v1-0812: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-Micro-Roleplay-Instruct-750M-v1-0812 with Docker Model Runner:
```
docker model run hf.co/Indexnusrefather/Super-Slop-Machina-Micro-Roleplay-Instruct-750M-v1-0812:Q4_K_M
```

Lemonade

How to use Indexnusrefather/Super-Slop-Machina-Micro-Roleplay-Instruct-750M-v1-0812 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Indexnusrefather/Super-Slop-Machina-Micro-Roleplay-Instruct-750M-v1-0812:Q4_K_M

Run and chat with the model

lemonade run user.Super-Slop-Machina-Micro-Roleplay-Instruct-750M-v1-0812-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Indexnusrefather/Super-Slop-Machina-Micro-Roleplay-Instruct-750M-v1-0812 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-Micro-Roleplay-Instruct-750M-v1-0812: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-Micro-Roleplay-Instruct-750M-v1-0812:Q4_K_M

Run Hermes

hermes

Atomic Chat

Super-Slop-Machina-Micro-Roleplay-Instruct-750M-v1-0812: Microwave Powered Local Roleplay.

"No limits, no sanity, no hardware constraints. Local SLM that was finetuned specifically for the purpose of roleplay, now not only on your toaster, but also your microwave."

Improvements:

Better at roleplay than the base model
Writes more consistently
Somewhat better at keeping track of the plot
Handles long term conversations slightly better now

Quants:

Are lying in the repo, I can't say anything for sure about them but wouldn't recommend going any lower than Q6_K, since its my first experiment with under 1b SLM.

Note:

This model is an experiment, methodology I used with it can't make the model physically larger, meaning that logic is capped at 752M parameters either way.

This is also a text only version without vision encoder, just because I found it more convenient.

Also, used my own dataset to finetune it, I hope my data filtering helped a little here. LFM 2.5 350M and LFM 2.5 230M finetunes coming soon, I had to be more aggressive with them so it promises to become very interesting, of course, I fully removed thinking from both.

Big announcement:

I experimented with Qwen 3.5 9b and I think I am somewhat succeeding, I will soon release the finetuned weights, result that I was able to achieve with it was VERY interesting...

Downloads last month: 116

Safetensors

Model size

0.8B params

Tensor type

F32

BF16

Model tree for Indexnusrefather/Super-Slop-Machina-Micro-Roleplay-Instruct-750M-v1-0812

Base model

Qwen/Qwen3.5-0.8B-Base

Finetuned

Qwen/Qwen3.5-0.8B

Finetuned

(322)

this model