Instructions to use Indexnusrefather/LFM-2.5-1.2b-Instruct-roleplay-tuned-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Indexnusrefather/LFM-2.5-1.2b-Instruct-roleplay-tuned-v2 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/LFM-2.5-1.2b-Instruct-roleplay-tuned-v2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Indexnusrefather/LFM-2.5-1.2b-Instruct-roleplay-tuned-v2: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/LFM-2.5-1.2b-Instruct-roleplay-tuned-v2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Indexnusrefather/LFM-2.5-1.2b-Instruct-roleplay-tuned-v2: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/LFM-2.5-1.2b-Instruct-roleplay-tuned-v2:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Indexnusrefather/LFM-2.5-1.2b-Instruct-roleplay-tuned-v2: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/LFM-2.5-1.2b-Instruct-roleplay-tuned-v2:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Indexnusrefather/LFM-2.5-1.2b-Instruct-roleplay-tuned-v2:Q4_K_M

Use Docker

docker model run hf.co/Indexnusrefather/LFM-2.5-1.2b-Instruct-roleplay-tuned-v2:Q4_K_M

LM Studio
Jan

vLLM

How to use Indexnusrefather/LFM-2.5-1.2b-Instruct-roleplay-tuned-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Indexnusrefather/LFM-2.5-1.2b-Instruct-roleplay-tuned-v2"
# 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/LFM-2.5-1.2b-Instruct-roleplay-tuned-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Indexnusrefather/LFM-2.5-1.2b-Instruct-roleplay-tuned-v2:Q4_K_M

Ollama
How to use Indexnusrefather/LFM-2.5-1.2b-Instruct-roleplay-tuned-v2 with Ollama:
```
ollama run hf.co/Indexnusrefather/LFM-2.5-1.2b-Instruct-roleplay-tuned-v2:Q4_K_M
```

Unsloth Studio

How to use Indexnusrefather/LFM-2.5-1.2b-Instruct-roleplay-tuned-v2 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/LFM-2.5-1.2b-Instruct-roleplay-tuned-v2 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/LFM-2.5-1.2b-Instruct-roleplay-tuned-v2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Indexnusrefather/LFM-2.5-1.2b-Instruct-roleplay-tuned-v2 to start chatting

How to use Indexnusrefather/LFM-2.5-1.2b-Instruct-roleplay-tuned-v2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Indexnusrefather/LFM-2.5-1.2b-Instruct-roleplay-tuned-v2: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/LFM-2.5-1.2b-Instruct-roleplay-tuned-v2:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Indexnusrefather/LFM-2.5-1.2b-Instruct-roleplay-tuned-v2 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/LFM-2.5-1.2b-Instruct-roleplay-tuned-v2: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/LFM-2.5-1.2b-Instruct-roleplay-tuned-v2:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Indexnusrefather/LFM-2.5-1.2b-Instruct-roleplay-tuned-v2 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Indexnusrefather/LFM-2.5-1.2b-Instruct-roleplay-tuned-v2: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/LFM-2.5-1.2b-Instruct-roleplay-tuned-v2: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/LFM-2.5-1.2b-Instruct-roleplay-tuned-v2 with Docker Model Runner:
```
docker model run hf.co/Indexnusrefather/LFM-2.5-1.2b-Instruct-roleplay-tuned-v2:Q4_K_M
```

Lemonade

How to use Indexnusrefather/LFM-2.5-1.2b-Instruct-roleplay-tuned-v2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Indexnusrefather/LFM-2.5-1.2b-Instruct-roleplay-tuned-v2:Q4_K_M

Run and chat with the model

lemonade run user.LFM-2.5-1.2b-Instruct-roleplay-tuned-v2-Q4_K_M

List all available models

lemonade list

INTRODUCTION

This model is further improvement over the success of my previous LFM2.5 1.2b finetune, it was trained on a dataset thats over 3 times bigger than the one I used for v1(5m vs 17m tokens), and it serves to improve creative writing further.

I will have to admit that my gemma 3 4b it v2 finetune performed better, I'd say even surprisingly so, if you experience any issues with this model, consider getting my bigger tunes.

How good is it?

Advantages:

Excellent writing
Improved emotional understanding
Size remains the same regardless, meaning anyone can run this model anywhere!

Disadvantages:

Architecture remains the same, its still a 1.2b model. Though, I did do my best!

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.

Thank you!

I appreciate that people actually found my initially released model interesting enough to download it, I didnt even think that it would attract any attention. But somehow, it did, I wanted anyone to be able to experience local roleplay, without the need for expensive hardware, without the need to pay for API keys, without the need to purchase subscriptions, and without the need to risk their data, and I believe small models like this one are very valuable for this task, because I know perfectly well that not everybody can run a 8b model.

v3:

v3 is ready, and it will be called Super-Slop-Machina-Roleplay-1.2b, I took a little different approach to it, the release is soon, wait until my horrible wifi finally loads it, but right now I can say enhancing it worked.

Downloads last month: 1,405

Safetensors

Model size

1B params

Tensor type

BF16

Model tree for Indexnusrefather/LFM-2.5-1.2b-Instruct-roleplay-tuned-v2

Base model

LiquidAI/LFM2.5-1.2B-Base

Finetuned

LiquidAI/LFM2.5-1.2B-Instruct

Adapter

(40)

this model

Adapters

2 models