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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Unmid/Mew1-2.6B-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 Unmid/Mew1-2.6B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Unmid/Mew1-2.6B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Unmid/Mew1-2.6B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Unmid/Mew1-2.6B-GGUF:Q4_K_M

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 Unmid/Mew1-2.6B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Unmid/Mew1-2.6B-GGUF:Q4_K_M

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 Unmid/Mew1-2.6B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Unmid/Mew1-2.6B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Unmid/Mew1-2.6B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Unmid/Mew1-2.6B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Unmid/Mew1-2.6B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Unmid/Mew1-2.6B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Unmid/Mew1-2.6B-GGUF:Q4_K_M

Ollama
How to use Unmid/Mew1-2.6B-GGUF with Ollama:
```
ollama run hf.co/Unmid/Mew1-2.6B-GGUF:Q4_K_M
```

Unsloth Studio

How to use Unmid/Mew1-2.6B-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 Unmid/Mew1-2.6B-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 Unmid/Mew1-2.6B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use Unmid/Mew1-2.6B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Unmid/Mew1-2.6B-GGUF:Q4_K_M

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": "Unmid/Mew1-2.6B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Unmid/Mew1-2.6B-GGUF with Docker Model Runner:
```
docker model run hf.co/Unmid/Mew1-2.6B-GGUF:Q4_K_M
```

Lemonade

How to use Unmid/Mew1-2.6B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Unmid/Mew1-2.6B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Mew1-2.6B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Unmid/Mew1-2.6B-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 Unmid/Mew1-2.6B-GGUF:Q4_K_M

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 Unmid/Mew1-2.6B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Unmid/Mew1-2.6B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Unmid/Mew1-2.6B-GGUF:Q4_K_M

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 "Unmid/Mew1-2.6B-GGUF:Q4_K_M" \
  --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"

🐱 Mew1-2.6B-GGUF

Owner-maintained GGUF quantizations of Unmid/Mew1-2.6B, prepared for llama.cpp, LM Studio, and other GGUF-compatible applications.

The canonical BF16 Transformers checkpoint is available at Unmid/Mew1-2.6B. Additional importance-matrix quantizations are available from mradermacher.

Provided files

Files are sorted from smallest to largest. Sizes are the actual decimal file sizes reported by the Hugging Face Hub.

File	Quant	Size	Notes
Mew1-2.6B.Q2_K.gguf	Q2_K	1.09 GB	Smallest; substantial quality loss
Mew1-2.6B.Q4_0.gguf	Q4_0	1.59 GB	Fast legacy 4-bit quant
Mew1-2.6B.Q4_K_M.gguf	Q4_K_M	1.67 GB	Recommended size/quality balance
Mew1-2.6B.Q5_K_M.gguf	Q5_K_M	1.94 GB	Higher quality
Mew1-2.6B.Q6_K.gguf	Q6_K	2.22 GB	Very high quality
Mew1-2.6B.Q8_0.gguf	Q8_0	2.87 GB	Largest quantized version

Download and run

Install the Hugging Face CLI and download the recommended quant:

pip install -U huggingface_hub
hf download Unmid/Mew1-2.6B-GGUF Mew1-2.6B.Q4_K_M.gguf --local-dir .

Run it with llama.cpp:

llama-cli \
  -m Mew1-2.6B.Q4_K_M.gguf \
  -c 4096 \
  --temp 0.7 \
  --top-k 50 \
  --top-p 0.9 \
  --repeat-penalty 1.1 \
  -cnv

The model metadata supports a context of approximately 128K tokens, but memory use grows with context length. Start with 4K–16K and increase only if your hardware allows it.

Quant selection

Q4_K_M — recommended starting point for most users.
Q5_K_M or Q6_K — use when quality matters more than memory.
Q8_0 — largest quantized file; useful when memory is plentiful.
Q2_K — only for severe memory constraints.

LEAP manifests

The leap/ directory contains one manifest per quantization. Each manifest references the corresponding root-level GGUF filename and uses the checkpoint chat template.

Model lineage

LiquidAI/LFM2.5-2.6B → SC117/LFM2.5-2.6B-Uncensored → Unmid/Mew1-2.6B → this GGUF release.

This is a community derivative and is not an official Liquid AI or SC117 release.

License

Distributed under the LFM Open License v1.0. Review LICENSE, including its attribution and commercial-use conditions.

Downloads last month: -

GGUF

Model size

3B params

Architecture

lfm2

Hardware compatibility

2-bit

4-bit

5-bit

6-bit

8-bit

Model tree for Unmid/Mew1-2.6B-GGUF

Base model

Unmid/Mew1-2.6B

Quantized

(3)

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