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

Libraries

How to use Kelexine/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="Kelexine/LFM2.5-1.2B-Thinking-GGUF",
	filename="LFM2.5-1.2B-Thinking-Q8_0.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Kelexine/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 Kelexine/LFM2.5-1.2B-Thinking-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf Kelexine/LFM2.5-1.2B-Thinking-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Kelexine/LFM2.5-1.2B-Thinking-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf Kelexine/LFM2.5-1.2B-Thinking-GGUF:Q8_0

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 Kelexine/LFM2.5-1.2B-Thinking-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Kelexine/LFM2.5-1.2B-Thinking-GGUF:Q8_0

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 Kelexine/LFM2.5-1.2B-Thinking-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Kelexine/LFM2.5-1.2B-Thinking-GGUF:Q8_0

Use Docker

docker model run hf.co/Kelexine/LFM2.5-1.2B-Thinking-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use Kelexine/LFM2.5-1.2B-Thinking-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kelexine/LFM2.5-1.2B-Thinking-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": "Kelexine/LFM2.5-1.2B-Thinking-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Kelexine/LFM2.5-1.2B-Thinking-GGUF:Q8_0

Ollama
How to use Kelexine/LFM2.5-1.2B-Thinking-GGUF with Ollama:
```
ollama run hf.co/Kelexine/LFM2.5-1.2B-Thinking-GGUF:Q8_0
```

Unsloth Studio

How to use Kelexine/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 Kelexine/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 Kelexine/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 Kelexine/LFM2.5-1.2B-Thinking-GGUF to start chatting

How to use Kelexine/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 Kelexine/LFM2.5-1.2B-Thinking-GGUF:Q8_0

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": "Kelexine/LFM2.5-1.2B-Thinking-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Kelexine/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 Kelexine/LFM2.5-1.2B-Thinking-GGUF:Q8_0

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 Kelexine/LFM2.5-1.2B-Thinking-GGUF:Q8_0

Run Hermes

hermes

Docker Model Runner
How to use Kelexine/LFM2.5-1.2B-Thinking-GGUF with Docker Model Runner:
```
docker model run hf.co/Kelexine/LFM2.5-1.2B-Thinking-GGUF:Q8_0
```

Lemonade

How to use Kelexine/LFM2.5-1.2B-Thinking-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Kelexine/LFM2.5-1.2B-Thinking-GGUF:Q8_0

Run and chat with the model

lemonade run user.LFM2.5-1.2B-Thinking-GGUF-Q8_0

List all available models

lemonade list

LFM 2.5 1.2B Thinking (GGUF)

Description

This repository contains the GGUF quantized version of LiquidAI/LFM2.5-1.2B-Thinking, a 1.2 billion parameter "thinking" language model by Liquid AI.

The model uses the novel Lfm2ForCausalLM architecture featuring a hybrid design of 10 double-gated LIV convolution blocks + 6 GQA attention blocks — a departure from standard transformer-only designs. This architecture alternates between local convolution-based mixing and sparse global attention, enabling efficient sequence processing with strong reasoning capabilities.

Model Details

Property	Value
Architecture	Lfm2ForCausalLM
Parameter Count	1.17B
Layers	16 (10 conv blocks + 6 GQA blocks)
Hidden Size	2048
Intermediate (FFN)	8192
Attention Heads	32
KV Heads (GQA)	8 (on attention layers)
Context Length	32,768 tokens
Vocabulary Size	65,536
Languages	English, Arabic, Chinese, French, German, Japanese, Korean, Spanish
Quantization	Q8_0 (8-bit)
File Type	GGUF

Quantization Details

This model was quantized using llama.cpp with the Q8_0 scheme:

Source format: F16 (converted from HuggingFace safetensors)
Quantization: Q8_0 — 8-bit quantization with block-wise scaling
Quality: Near-lossless; ideal for deployment where precision matters
Size reduction: ~50% smaller than F16 while retaining virtually all model quality

Usage with llama.cpp

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build && cmake --build build --config Release -j$(nproc)

./build/bin/llama-cli \
  -hf Kelexine/LFM2.5-1.2B-Thinking-GGUF \
  --temp 0.05 --top-k 50 --repeat-penalty 1.05 -n 4096 -cnv

Or with a local file:

./build/bin/llama-cli \
  -m LFM2.5-1.2B-Thinking-Q8_0.gguf \
  -p "<|im_start|>user\nYour prompt here<|im_end|>\n<|im_start|>assistant\n" \
  --temp 0.05 --top-k 50 --repeat-penalty 1.05 -n 4096

Usage with Python (llama-cpp-python)

from llama_cpp import Llama

llm = Llama(
    model_path="LFM2.5-1.2B-Thinking-Q8_0.gguf",
    n_ctx=4096,
    temperature=0.05,
    top_k=50,
    repeat_penalty=1.05,
)

response = llm(
    "<|im_start|>user\nWhat is machine learning?<|im_end|>\n<|im_start|>assistant\n",
    max_tokens=4096,
    stop=["<|im_end|>"],
)
print(response["choices"][0]["text"])

Provided Files

File	Description
`LFM2.5-1.2B-Thinking-Q8_0.gguf`	8-bit quantized GGUF (recommended)

Limitations

This is a 1.17B parameter model — suited for lightweight tasks, quick prototyping, and edge deployment.
The "Thinking" variant is designed for chain-of-thought reasoning but may produce verbose <think>...</think> blocks; strip these in downstream integrations.
Requires a recent version of llama.cpp with Lfm2ForCausalLM architecture support.
Not recommended for knowledge-intensive tasks or programming per Liquid AI's own guidance.

License

This repository inherits the LFM 1.0 License from the base model LiquidAI/LFM2.5-1.2B-Thinking.

Credits

Base model: Liquid AI
Quantization: kelexine
Framework: llama.cpp by ggml-org

Downloads last month: 14

GGUF

Model size

1B params

Architecture

lfm2

Hardware compatibility

8-bit

Model tree for Kelexine/LFM2.5-1.2B-Thinking-GGUF

Base model

LiquidAI/LFM2.5-1.2B-Base

Finetuned

LiquidAI/LFM2.5-1.2B-Thinking

Quantized

(31)

this model