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 Settings

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

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

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

Atomic Chat new
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

Seriki commited on Apr 26

Commit

1f6ae44

verified ·

1 Parent(s): 6c58c34

Update README.md

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README.md +18 -0

README.md CHANGED Viewed

@@ -3,6 +3,13 @@ base_model: openai/gpt-oss-safeguard-120b
 license: apache-2.0
 tags:
 - gguf
 ---
 ## 💫 Community Model> gpt-oss-safeguard-120b by openai
@@ -14,8 +21,19 @@ Use in LM Studio with [gpt-oss-safeguard](https://lmstudio.ai/models/gpt-oss-saf
 **Original model**: [gpt-oss-safeguard-120b](https://huggingface.co/openai/gpt-oss-safeguard-120b)<br>
 **GGUF quantization**: provided by [LM Studio team](https://x.com/lmstudio) using `llama.cpp` release [b6866](https://github.com/ggerganov/llama.cpp/releases/tag/b6866)<br>
 ## Special thanks
 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.
 ## Disclaimers

 license: apache-2.0
 tags:
 - gguf
+datasets:
+- markov-ai/computer-use-large
+- qubuhub/LMLM-pretrain-dwiki6.1M
+language:
+- en
+pipeline_tag: any-to-any
+library_name: fastai, adapter-transformers, nlp, mlx, lmlm, allenlp, lmkm, llama, gpt
 ---
 ## 💫 Community Model> gpt-oss-safeguard-120b by openai
 **Original model**: [gpt-oss-safeguard-120b](https://huggingface.co/openai/gpt-oss-safeguard-120b)<br>
 **GGUF quantization**: provided by [LM Studio team](https://x.com/lmstudio) using `llama.cpp` release [b6866](https://github.com/ggerganov/llama.cpp/releases/tag/b6866)<br>
+gpt-oss-safeguard-120b
+gpt-oss-safeguard-120b is a safety reasoning model by OpenAI, built-upon their original gpt-oss release. With these models, you can classify text content based on safety policies that you provide and perform a suite of foundational safety tasks. These models are intended for safety use cases. For other applications, we recommend using gpt-oss.
+This 120b variant is designed for production, general purpose, high reasoning use cases that fits into a single H100 GPU (117B parameters with 5.1B active parameters).
+This model is released under a permissive Apache 2.0 license and it features configurable reasoning effort—low, medium, or high, so users can balance output quality and latency based on their needs. The model offers full chain-of-thought visibility to support easier debugging and increased trust, though this output is not intended for end users.
+This model supports a context length of 131k.
 ## Special thanks
 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.
 ## Disclaimers