Instructions to use Seriki/Lmlm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Seriki/Lmlm with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Seriki/Lmlm",
	filename="gpt-oss-safeguard-120b-MXFP4-00001-of-00002.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Seriki/Lmlm with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Seriki/Lmlm
# Run inference directly in the terminal:
llama-cli -hf Seriki/Lmlm

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Seriki/Lmlm
# Run inference directly in the terminal:
llama-cli -hf Seriki/Lmlm

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 Seriki/Lmlm
# Run inference directly in the terminal:
./llama-cli -hf Seriki/Lmlm

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 Seriki/Lmlm
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Seriki/Lmlm

Use Docker

docker model run hf.co/Seriki/Lmlm

LM Studio
Jan
Ollama
How to use Seriki/Lmlm with Ollama:
```
ollama run hf.co/Seriki/Lmlm
```

Unsloth Studio new

How to use Seriki/Lmlm 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 Seriki/Lmlm 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 Seriki/Lmlm to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Seriki/Lmlm to start chatting

Pi new

How to use Seriki/Lmlm with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Seriki/Lmlm

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": "Seriki/Lmlm"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Seriki/Lmlm with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Seriki/Lmlm

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 Seriki/Lmlm

Run Hermes

hermes

Docker Model Runner
How to use Seriki/Lmlm with Docker Model Runner:
```
docker model run hf.co/Seriki/Lmlm
```

Lemonade

How to use Seriki/Lmlm with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Seriki/Lmlm

Run and chat with the model

lemonade run user.Lmlm-{{QUANT_TAG}}

List all available models

lemonade list

Lmlm

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\usepackage{hyperref}

\subsection{CO2 Emission Related to Experiments}

Experiments were conducted using {cloud provider} in region {region of compute}, which has a carbon efficiency of  kgCO$_2$eq/kWh. A cumulative of {hours used} hours of computation was performed on hardware of type {hardware type} (TDP of W).

Total emissions are estimated to be {emission} kgCO$_2$eq of which {percentage offset} percents were directly offset by the cloud provider.
    
%Uncomment if you bought additional offsets:
%XX kg CO2eq were manually offset through \href{link}{Offset Provider}.

Estimations were conducted using the \href{https://mlco2.github.io/impact#compute}{MachineLearning Impact calculator} presented in \cite{lacoste2019quantifying}.

@article{lacoste2019quantifying,
  title={Quantifying the Carbon Emissions of Machine Learning},
  author={Lacoste, Alexandre and Luccioni, Alexandra and Schmidt, Victor and Dandres, seriki ,Thomas},
  journal={arXiv preprint arXiv:1910.09700},
  year={2019}
}