Instructions to use gggff123/lopus3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use gggff123/lopus3 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 gggff123/lopus3
# Run inference directly in the terminal:
llama cli -hf gggff123/lopus3

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf gggff123/lopus3
# Run inference directly in the terminal:
llama cli -hf gggff123/lopus3

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 gggff123/lopus3
# Run inference directly in the terminal:
./llama-cli -hf gggff123/lopus3

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 gggff123/lopus3
# Run inference directly in the terminal:
./build/bin/llama-cli -hf gggff123/lopus3

Use Docker

docker model run hf.co/gggff123/lopus3

LM Studio
Jan
Ollama
How to use gggff123/lopus3 with Ollama:
```
ollama run hf.co/gggff123/lopus3
```
Unsloth Desktop

How to use gggff123/lopus3 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf gggff123/lopus3

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "gggff123/lopus3"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use gggff123/lopus3 with Docker Model Runner:
```
docker model run hf.co/gggff123/lopus3
```

Lemonade

How to use gggff123/lopus3 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull gggff123/lopus3

Run and chat with the model

lemonade run user.lopus3-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use gggff123/lopus3 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf gggff123/lopus3

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 gggff123/lopus3

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use gggff123/lopus3 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf gggff123/lopus3

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 "gggff123/lopus3" \
  --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"

This is my attempt to copy claude opus 3 behaviour into lfm 2.5 350m

Model details

It succesfully recognises itself and behaves 70% of claudes helpful behaviour Though the intellegence remains the same it considers himself as a claude model.

How to use it?

This is a gguf / quantisized model so it can be run with llama.cpp , ollama, vllm etc. Using a system prompt might enhance the claude like behavior to 90%

Advantages

Small model
Testing model
Copies claudes helpful behaviour
Fast and free

Disadvantages

Decreased intellgence ( cant answer complex problems very good )
Often gives nonsense

I will be pleased if you try this model i tuned

Downloads last month: 90

GGUF

Model size

0.4B params

Architecture

lfm2

Hardware compatibility

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for gggff123/lopus3

Base model

LiquidAI/LFM2.5-350M-Base

Finetuned

LiquidAI/LFM2.5-350M

Quantized

(78)

this model

gggff123
/

lopus3

This is my attempt to copy claude opus 3 behaviour into lfm 2.5 350m

Model details

How to use it?

Advantages

Disadvantages

I will be pleased if you try this model i tuned

Model tree for gggff123/lopus3

Dataset used to train gggff123/lopus3