Instructions to use MadlabOSS/LFM2-2.6b-lmsguide-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MadlabOSS/LFM2-2.6b-lmsguide-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="MadlabOSS/LFM2-2.6b-lmsguide-GGUF",
	filename="lfm2-2.6b-lmsguide-f16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use MadlabOSS/LFM2-2.6b-lmsguide-GGUF 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 MadlabOSS/LFM2-2.6b-lmsguide-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf MadlabOSS/LFM2-2.6b-lmsguide-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf MadlabOSS/LFM2-2.6b-lmsguide-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf MadlabOSS/LFM2-2.6b-lmsguide-GGUF:F16

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 MadlabOSS/LFM2-2.6b-lmsguide-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf MadlabOSS/LFM2-2.6b-lmsguide-GGUF:F16

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 MadlabOSS/LFM2-2.6b-lmsguide-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MadlabOSS/LFM2-2.6b-lmsguide-GGUF:F16

Use Docker

docker model run hf.co/MadlabOSS/LFM2-2.6b-lmsguide-GGUF:F16

LM Studio
Jan
Ollama
How to use MadlabOSS/LFM2-2.6b-lmsguide-GGUF with Ollama:
```
ollama run hf.co/MadlabOSS/LFM2-2.6b-lmsguide-GGUF:F16
```

Unsloth Studio

How to use MadlabOSS/LFM2-2.6b-lmsguide-GGUF 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 MadlabOSS/LFM2-2.6b-lmsguide-GGUF 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 MadlabOSS/LFM2-2.6b-lmsguide-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for MadlabOSS/LFM2-2.6b-lmsguide-GGUF to start chatting

How to use MadlabOSS/LFM2-2.6b-lmsguide-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf MadlabOSS/LFM2-2.6b-lmsguide-GGUF:F16

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": "MadlabOSS/LFM2-2.6b-lmsguide-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use MadlabOSS/LFM2-2.6b-lmsguide-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf MadlabOSS/LFM2-2.6b-lmsguide-GGUF:F16

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 MadlabOSS/LFM2-2.6b-lmsguide-GGUF:F16

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use MadlabOSS/LFM2-2.6b-lmsguide-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf MadlabOSS/LFM2-2.6b-lmsguide-GGUF:F16

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 "MadlabOSS/LFM2-2.6b-lmsguide-GGUF:F16" \
  --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 MadlabOSS/LFM2-2.6b-lmsguide-GGUF with Docker Model Runner:
```
docker model run hf.co/MadlabOSS/LFM2-2.6b-lmsguide-GGUF:F16
```

Lemonade

How to use MadlabOSS/LFM2-2.6b-lmsguide-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MadlabOSS/LFM2-2.6b-lmsguide-GGUF:F16

Run and chat with the model

lemonade run user.LFM2-2.6b-lmsguide-GGUF-F16

List all available models

lemonade list

LMS Guide 2.6b

🧠 Overview

The LMS Guide 2.6b is part of the MadlabOSS LM Studio Guide family — a lineup of small, efficient, and highly aligned assistant models trained specifically to provide deterministic, hallucination‑resistant guidance for LM Studio users.

This model is trained on a curated dataset of LM Studio–specific instructions, workflows, troubleshooting steps, and conceptual explanations.

🚀 Intended Use

This model is optimized for:

LM Studio onboarding
workflow explanations
feature descriptions
troubleshooting guidance
plugin/server integration help
safe, deterministic assistant behavior

It is not intended as a general‑purpose chatbot.

🧩 Model Details

Base Model: LFM2‑2.6B

Parameter Count: 2.6 Billion

Training Type: Supervised fine‑tuning

Sequence Length: 1024

Precision: FP16

Framework: PyTorch / Transformers

📦 Training Data

The model was trained on:

~36,000 LM Studio–specific instruction/response pairs
Clean, domain‑specific, ontology‑consistent data
Minor general‑purpose conversational data
No web‑scraped content
Full LM Studio Documentation

🏋️ Training Procedure

Hyperparameters

Epochs: 6
Batch size: 16
Learning rate: cosine schedule, peak ~4e‑5
Optimizer: AdamW
Gradient clipping: 1.0
Gradient accumulation: 1

Hardware

Training was performed on:

RTX 6000 Blackwell (96GB)
Dual RTX 3090 (Magic Judge)

📊 Evaluation

Judge Score

Semantic correctness, ontology adherence, and hallucination resistance.

Qualitative Behavior

Strong adherence to LM Studio terminology
Low hallucination rate
Deterministic, predictable responses
Not optimized for open‑domain reasoning

🔒 Safety

This model is trained exclusively on LM Studio–specific content.
It avoids hallucinating non‑existent LM Studio features and adheres to a strict ontology.

It is not designed for:

political content
medical advice
legal advice
general‑purpose conversation

⚠️ Limitations

Not a general assistant
Not trained for coding, math, or open‑domain reasoning
May refuse tasks outside LM Studio scope

Downloads last month: 44

GGUF

Model size

3B params

Architecture

lfm2

Hardware compatibility

8-bit

16-bit

Inference Providers NEW

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Model tree for MadlabOSS/LFM2-2.6b-lmsguide-GGUF

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LiquidAI/LFM2-2.6B

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Collection including MadlabOSS/LFM2-2.6b-lmsguide-GGUF

LM Studio Guide GGUF

Collection

The MadlabOSS LM Studio Guide family — a lineup of small assistant models trained on a curated dataset of LM Studio–specific docs • 4 items • Updated Dec 25, 2025 • 2