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

Libraries

How to use Indexnusrefather/LFM-2.5-1.2b-Instruct-roleplay-tuned with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Indexnusrefather/LFM-2.5-1.2b-Instruct-roleplay-tuned",
	filename="LFM2.5-1.2B-Instruct-roleplay-tuned.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/LFM-2.5-1.2b-Instruct-roleplay-tuned 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:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Indexnusrefather/LFM-2.5-1.2b-Instruct-roleplay-tuned: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:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Indexnusrefather/LFM-2.5-1.2b-Instruct-roleplay-tuned: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:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Indexnusrefather/LFM-2.5-1.2b-Instruct-roleplay-tuned: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:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Indexnusrefather/LFM-2.5-1.2b-Instruct-roleplay-tuned:Q4_K_M

Use Docker

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

LM Studio
Jan

vLLM

How to use Indexnusrefather/LFM-2.5-1.2b-Instruct-roleplay-tuned 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"
# 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",
		"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:Q4_K_M

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

Unsloth Studio

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

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

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Indexnusrefather/LFM-2.5-1.2b-Instruct-roleplay-tuned 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: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: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 with Docker Model Runner:
```
docker model run hf.co/Indexnusrefather/LFM-2.5-1.2b-Instruct-roleplay-tuned:Q4_K_M
```

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

What is it?

LFM-2.5-1.2b-Instruct-roleplay-tuned is my attempt at making a roleplay model that anybody can run, no matter how bad the hardware is, it was tuned on 5M~ tokens of high quality roleplay data with the aim to make it better at roleplay.

Strengths and challenges:

Advantages

Better creative writing, less slop, a little bit of repetitiveness remains due to size, fixed by samplers
Better formatting, knows how to use asterisks really well
Has a certain "Soul" to it, was personally pretty fun to test
Runs literally anywhere, almost anywhere
Insane inference speed

Disadvantages

Context not that good
Undestanding of complex concepts not that good
Sensitive to quantization, Q8_0 or BF16 is recommended
My first tune, altough I tried my best
Because its a 1.2b dense model, its not the smartest, and may sometimes use wrong pronouns, if it was performed on any model of this size that is not LFM2.5 1.2b instruct, the results would probably be worse.

Why I made this tune?

Because I was very bored, and I wanted something that can work anywhere on virtually any hardware, my own hardware is not the best, but was enough for making this tune, I think there are a lot to improve on with this model, and I will probably work on its mistakes just when I get the time, it can still sometimes glitch a bit, especially in lower quants, but I'm still personally satisfied with the result.

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.
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.

Wow

I didnt expect this finetune to become that popular, sincere thanks to anyone reading this! V2 is already cooked up and ready for release, I lately realised that 5M~ tokens of data may have been too little for this model, I already tested the v2 and seen real improvements in the way it writes.

Downloads last month: 1,225

Safetensors

Model size

1B params

Tensor type

BF16

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

Base model

LiquidAI/LFM2.5-1.2B-Base

Finetuned

LiquidAI/LFM2.5-1.2B-Instruct

Adapter

(40)

this model

Adapters

2 models

Indexnusrefather
/

LFM-2.5-1.2b-Instruct-roleplay-tuned

What is it?

Strengths and challenges:

Advantages

Disadvantages

Why I made this tune?

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

Wow

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

Dataset used to train Indexnusrefather/LFM-2.5-1.2b-Instruct-roleplay-tuned