Instructions to use OrbitMC/Mew1-2.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use OrbitMC/Mew1-2.6B 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 OrbitMC/Mew1-2.6B
# Run inference directly in the terminal:
llama cli -hf OrbitMC/Mew1-2.6B

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf OrbitMC/Mew1-2.6B
# Run inference directly in the terminal:
llama cli -hf OrbitMC/Mew1-2.6B

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 OrbitMC/Mew1-2.6B
# Run inference directly in the terminal:
./llama-cli -hf OrbitMC/Mew1-2.6B

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 OrbitMC/Mew1-2.6B
# Run inference directly in the terminal:
./build/bin/llama-cli -hf OrbitMC/Mew1-2.6B

Use Docker

docker model run hf.co/OrbitMC/Mew1-2.6B

LM Studio
Jan
Ollama
How to use OrbitMC/Mew1-2.6B with Ollama:
```
ollama run hf.co/OrbitMC/Mew1-2.6B
```

Unsloth Studio

How to use OrbitMC/Mew1-2.6B 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 OrbitMC/Mew1-2.6B 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 OrbitMC/Mew1-2.6B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for OrbitMC/Mew1-2.6B to start chatting

How to use OrbitMC/Mew1-2.6B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf OrbitMC/Mew1-2.6B

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": "OrbitMC/Mew1-2.6B"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use OrbitMC/Mew1-2.6B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf OrbitMC/Mew1-2.6B

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 "OrbitMC/Mew1-2.6B" \
  --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 OrbitMC/Mew1-2.6B with Docker Model Runner:
```
docker model run hf.co/OrbitMC/Mew1-2.6B
```

Lemonade

How to use OrbitMC/Mew1-2.6B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull OrbitMC/Mew1-2.6B

Run and chat with the model

lemonade run user.Mew1-2.6B-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use OrbitMC/Mew1-2.6B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf OrbitMC/Mew1-2.6B

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 OrbitMC/Mew1-2.6B

Run Hermes

hermes

Atomic Chat

Mew1-2.6B

File size: 1,756 Bytes

9c9d7ed

{
    "architectures": [
        "Lfm2ForCausalLM"
    ],
    "block_auto_adjust_ff_dim": false,
    "block_dim": 2048,
    "block_ffn_dim_multiplier": 1.0,
    "block_mlp_init_scale": 1.0,
    "block_multiple_of": 256,
    "block_norm_eps": 1e-05,
    "block_out_init_scale": 1.0,
    "block_use_swiglu": true,
    "block_use_xavier_init": true,
    "bos_token_id": 124894,
    "conv_L_cache": 3,
    "conv_bias": false,
    "conv_dim": 2048,
    "conv_use_xavier_init": true,
    "torch_dtype": "float16",
    "eos_token_id": 124900,
    "full_attn_idxs": null,
    "hidden_size": 2048,
    "initializer_range": 0.02,
    "intermediate_size": 10752,
    "layer_types": [
        "conv",
        "conv",
        "full_attention",
        "conv",
        "conv",
        "full_attention",
        "conv",
        "conv",
        "conv",
        "full_attention",
        "conv",
        "conv",
        "conv",
        "full_attention",
        "conv",
        "conv",
        "conv",
        "full_attention",
        "conv",
        "conv",
        "conv",
        "full_attention",
        "conv",
        "conv",
        "full_attention",
        "conv",
        "conv",
        "full_attention",
        "conv",
        "conv"
    ],
    "max_position_embeddings": 128000,
    "model_name": "SC117/LFM2.5-2.6B-Uncensored",
    "model_type": "lfm2",
    "norm_eps": 1e-05,
    "num_attention_heads": 32,
    "num_heads": 32,
    "num_hidden_layers": 30,
    "num_key_value_heads": 8,
    "pad_token_id": 124893,
    "rope_parameters": {
        "rope_theta": 10000000.0,
        "rope_type": "default"
    },
    "tie_word_embeddings": true,
    "unsloth_version": "2026.8.18",
    "use_cache": true,
    "use_pos_enc": true,
    "vocab_size": 128000
}