Instructions to use NexaAI/LFM2.5-1.2B-thinking-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NexaAI/LFM2.5-1.2B-thinking-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="NexaAI/LFM2.5-1.2B-thinking-GGUF",
	filename="LFM2.5-1.2B-Thinking-BF16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use NexaAI/LFM2.5-1.2B-thinking-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf NexaAI/LFM2.5-1.2B-thinking-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf NexaAI/LFM2.5-1.2B-thinking-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf NexaAI/LFM2.5-1.2B-thinking-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf NexaAI/LFM2.5-1.2B-thinking-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 NexaAI/LFM2.5-1.2B-thinking-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf NexaAI/LFM2.5-1.2B-thinking-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 NexaAI/LFM2.5-1.2B-thinking-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NexaAI/LFM2.5-1.2B-thinking-GGUF:Q4_K_M

Use Docker

docker model run hf.co/NexaAI/LFM2.5-1.2B-thinking-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use NexaAI/LFM2.5-1.2B-thinking-GGUF with Ollama:
```
ollama run hf.co/NexaAI/LFM2.5-1.2B-thinking-GGUF:Q4_K_M
```

Unsloth Studio new

How to use NexaAI/LFM2.5-1.2B-thinking-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 NexaAI/LFM2.5-1.2B-thinking-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 NexaAI/LFM2.5-1.2B-thinking-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for NexaAI/LFM2.5-1.2B-thinking-GGUF to start chatting

Pi new

How to use NexaAI/LFM2.5-1.2B-thinking-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf NexaAI/LFM2.5-1.2B-thinking-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": "NexaAI/LFM2.5-1.2B-thinking-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use NexaAI/LFM2.5-1.2B-thinking-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf NexaAI/LFM2.5-1.2B-thinking-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 NexaAI/LFM2.5-1.2B-thinking-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use NexaAI/LFM2.5-1.2B-thinking-GGUF with Docker Model Runner:
```
docker model run hf.co/NexaAI/LFM2.5-1.2B-thinking-GGUF:Q4_K_M
```

Lemonade

How to use NexaAI/LFM2.5-1.2B-thinking-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NexaAI/LFM2.5-1.2B-thinking-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LFM2.5-1.2B-thinking-GGUF-Q4_K_M

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

LFM2.5-1.2B-Thinking

Model Description

LFM2.5-1.2B-Thinking is a ~1.17B-parameter “thinking” (reasoning-tuned) language model from Liquid AI’s LFM2.5 family, designed for efficient deployment (including on-device/edge scenarios).

It supports long-context usage (up to 32,768 tokens) and is trained/tuned with a focus on instruction following and reasoning-oriented behavior.

Quickstart

Install NexaSDK
Run the model on Qualcomm NPU in one line:

nexa infer NexaAI/LFM2.5-1.2B-thinking-GGUF

Features

Reasoning-oriented: tuned for stronger step-by-step problem solving vs. base variants.
Conversational AI: context-aware dialogue using a chat template format.
Tool / function calling: supports tool-use patterns for agentic workflows.
Long context: supports up to 32K context length.
Multilingual: supports multiple languages (including English and several major world languages).

Use Cases

On-device assistants and private “local-first” chat experiences
Tool-using agents (structured actions via function calls)
Document Q&A and summarization (especially when paired with retrieval)
Structured extraction and classification tasks

Inputs and Outputs

Input:

Text prompts or conversation history, typically formatted using the model’s chat template.

Output:

Generated text (answers, explanations, reasoning responses).
Optional structured tool calls when prompted for tool-use behavior.

License

This repo is licensed under the Creative Commons Attribution–NonCommercial 4.0 (CC BY-NC 4.0) license, which allows use, sharing, and modification only for non-commercial purposes with proper attribution. All NPU-related models, runtimes, and code in this project are protected under this non-commercial license and cannot be used in any commercial or revenue-generating applications. Commercial licensing or enterprise usage requires a separate agreement. For inquiries, please contact dev@nexa.ai

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GGUF

Model size

1B params

Architecture

lfm2

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

16-bit

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