Instructions to use Nichonauta/LFM2.5-1.2B-Thinking-ToMoE-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Nichonauta/LFM2.5-1.2B-Thinking-ToMoE-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 Nichonauta/LFM2.5-1.2B-Thinking-ToMoE-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Nichonauta/LFM2.5-1.2B-Thinking-ToMoE-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 Nichonauta/LFM2.5-1.2B-Thinking-ToMoE-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Nichonauta/LFM2.5-1.2B-Thinking-ToMoE-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 Nichonauta/LFM2.5-1.2B-Thinking-ToMoE-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Nichonauta/LFM2.5-1.2B-Thinking-ToMoE-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 Nichonauta/LFM2.5-1.2B-Thinking-ToMoE-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Nichonauta/LFM2.5-1.2B-Thinking-ToMoE-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Nichonauta/LFM2.5-1.2B-Thinking-ToMoE-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Nichonauta/LFM2.5-1.2B-Thinking-ToMoE-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Nichonauta/LFM2.5-1.2B-Thinking-ToMoE-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": "Nichonauta/LFM2.5-1.2B-Thinking-ToMoE-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Nichonauta/LFM2.5-1.2B-Thinking-ToMoE-GGUF:Q4_K_M
- Ollama
How to use Nichonauta/LFM2.5-1.2B-Thinking-ToMoE-GGUF with Ollama:
ollama run hf.co/Nichonauta/LFM2.5-1.2B-Thinking-ToMoE-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use Nichonauta/LFM2.5-1.2B-Thinking-ToMoE-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Nichonauta/LFM2.5-1.2B-Thinking-ToMoE-GGUF:Q4_K_M
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": "Nichonauta/LFM2.5-1.2B-Thinking-ToMoE-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Nichonauta/LFM2.5-1.2B-Thinking-ToMoE-GGUF with Docker Model Runner:
docker model run hf.co/Nichonauta/LFM2.5-1.2B-Thinking-ToMoE-GGUF:Q4_K_M
- Lemonade
How to use Nichonauta/LFM2.5-1.2B-Thinking-ToMoE-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Nichonauta/LFM2.5-1.2B-Thinking-ToMoE-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.LFM2.5-1.2B-Thinking-ToMoE-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use Nichonauta/LFM2.5-1.2B-Thinking-ToMoE-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 Nichonauta/LFM2.5-1.2B-Thinking-ToMoE-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 Nichonauta/LFM2.5-1.2B-Thinking-ToMoE-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Nichonauta/LFM2.5-1.2B-Thinking-ToMoE-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Nichonauta/LFM2.5-1.2B-Thinking-ToMoE-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 "Nichonauta/LFM2.5-1.2B-Thinking-ToMoE-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"
LFM2.5-1.2B-Thinking-ToMoE-GGUF
GGUF quantizations of Nichonauta/LFM2.5-1.2B-Thinking-ToMoE — the ToMoE Mixture-of-Experts conversion of LiquidAI/LFM2.5-1.2B-Thinking.
Files
| File | Quantization | Size | BPW |
|---|---|---|---|
LFM2.5-1.2B-Thinking-ToMoE-BF16.gguf |
BF16 | 2343 MB | 16.0 |
LFM2.5-1.2B-Thinking-ToMoE-Q8_0.gguf |
Q8_0 | 1186 MB | 8.50 |
LFM2.5-1.2B-Thinking-ToMoE-Q4_K_M.gguf |
Q4_K_M | 695 MB | 4.98 |
The Q4_K_M version runs under 1 GB of storage — matching the "on-device under 1 GB" goal of the original model.
Important note about the conversion
The ToMoE channel-MoE experts and the per-token dynamic V masks have no native representation in llama.cpp's LFM2 architecture. To produce a runnable GGUF, the pruned model was rebuilt as a dense-equivalent:
- The cut conv layers were restored to their full width using the original dense weights (the conv weights were never modified by ToMoE — only masked at runtime).
- The MLP union cut (~99% of the FFN was already preserved) was restored to the full FFN.
- The attention Q/K were already full-width in the pruned model.
As a result, the GGUF behaves like the dense base model rather than the pruned MoE (including the native <think> reasoning behavior of LFM2.5-Thinking). For the actual MoE behavior, use the safetensors version with trust_remote_code.
Usage (llama.cpp, CUDA)
llama-server.exe ^
--model LFM2.5-1.2B-Thinking-ToMoE-Q4_K_M.gguf ^
--gpu-layers all ^
--ctx-size 32768 ^
--temp 0.05 ^
--top-k 50 ^
--repeat-penalty 1.05 ^
--alias LFM2.5-1.2B-Thinking-ToMoE
Or with llama-cli (single turn, shows the thinking block):
llama-cli.exe -m LFM2.5-1.2B-Thinking-ToMoE-Q4_K_M.gguf -ngl 99 -p "What is the capital of France?" -n 90
Measured on an RTX 3060 (CUDA 13.3 build, llama.cpp b10603): ~302 t/s generation for Q4_K_M, ~212 t/s for Q8_0, ~125 t/s for BF16. The official generation parameters of the base model (temperature 0.05, top_k 50, repetition_penalty 1.05) are recommended.
Base model
- MoE source: Nichonauta/LFM2.5-1.2B-Thinking-ToMoE
- Dense base: LiquidAI/LFM2.5-1.2B-Thinking (LFM Open License v1.0)
License
Derivative of LiquidAI/LFM2.5-1.2B-Thinking — released under the LFM Open License v1.0 (see LICENSE).
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Model tree for Nichonauta/LFM2.5-1.2B-Thinking-ToMoE-GGUF
Base model
LiquidAI/LFM2.5-1.2B-Base