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

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

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

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use LiquidAI/LFM2.5-1.2B-Instruct-GGUF with llama.cpp:

Install from brew

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

Use Docker

docker model run hf.co/LiquidAI/LFM2.5-1.2B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use LiquidAI/LFM2.5-1.2B-Instruct-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/LiquidAI/LFM2.5-1.2B-Instruct-GGUF:Q4_K_M

Ollama
How to use LiquidAI/LFM2.5-1.2B-Instruct-GGUF with Ollama:
```
ollama run hf.co/LiquidAI/LFM2.5-1.2B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio new

How to use LiquidAI/LFM2.5-1.2B-Instruct-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 LiquidAI/LFM2.5-1.2B-Instruct-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 LiquidAI/LFM2.5-1.2B-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

Pi new

How to use LiquidAI/LFM2.5-1.2B-Instruct-GGUF with Pi:

Start the llama.cpp server

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

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

How to use LiquidAI/LFM2.5-1.2B-Instruct-GGUF with Lemonade:

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

LFM2.5-1.2B-Instruct-GGUF / README.md

mlabonne

Upload folder using huggingface_hub

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library_name: transformers
license: other
license_name: lfm1.0
license_link: LICENSE
language:
  - en
  - ar
  - zh
  - fr
  - de
  - ja
  - ko
  - es
pipeline_tag: text-generation
tags:
  - liquid
  - lfm2.5
  - edge
  - gguf

Try LFM • Documentation • LEAP • Blog

LFM2.5-1.2B-Instruct (W.I.P.)

LFM2.5 is a new family of hybrid models designed for on-device deployment. It builds on the LFM2 architecture with extended pre-training and reinforcement learning.

Highlights

Best performance among sub-2B models, particularly in instruction following.
2x faster inference on CPU compared to Qwen3, with optimized prefill and decode speeds.
Hybrid architecture with a combination of convolution and attention blocks.

LFM2.5 Family

In the LFM2.5 family, we release:

Model	Description
LFM2.5-1.2B-Base	Pre-trained base model
LFM2.5-1.2B-Instruct	General-purpose chat model
LFM2.5-1.2B-JP	Japanese-optimized chat model
LFM2.5-VL-1.6B	Vision-language model
LFM2.5-1.5B-Audio	Audio-language model

Model Details

Property	Value
Parameters	1.17B
Context length	32,768 tokens
Architecture	16 layers (10 conv + 6 attn)
Vocabulary	65,536
Precision	bfloat16
Training budget	10T tokens
License	LFM Open License v1.0

Supported languages: English, Arabic, Chinese, French, German, Japanese, Korean, Spanish

Recommended use cases: Agentic tasks, data extraction, RAG, creative writing, multi-turn conversations

Not recommended for: Knowledge-intensive tasks, programming

Quick Start

pip install -U transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "LiquidAI/LFM2.5-1.2B-Instruct"
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype="bfloat16")
tokenizer = AutoTokenizer.from_pretrained(model_id)

messages = [{"role": "user", "content": "What is C. elegans?"}]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)

output = model.generate(
    input_ids,
    do_sample=True,
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_new_tokens=512,
)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Recommended generation parameters: temperature=0.3, min_p=0.15, repetition_penalty=1.05

Chat Template

LFM2.5 uses a ChatML-like format. See the Chat Template documentation for details.

<|startoftext|><|im_start|>system
You are a helpful assistant trained by Liquid AI.<|im_end|>
<|im_start|>user
What is C. elegans?<|im_end|>
<|im_start|>assistant

Use tokenizer.apply_chat_template() to automatically format your messages.

Tool Use

LFM2.5 supports function calling. See the Tool Use documentation for the full guide.

tools = [{
    "name": "get_weather",
    "description": "Get current weather for a location",
    "parameters": {
        "type": "object",
        "properties": {"location": {"type": "string"}},
        "required": ["location"]
    }
}]

messages = [{"role": "user", "content": "What's the weather in Paris?"}]
input_ids = tokenizer.apply_chat_template(messages, tools=tools, add_generation_prompt=True, return_tensors="pt")

Inference

Framework	Documentation
Transformers	docs.liquid.ai/lfm/inference/transformers
vLLM	docs.liquid.ai/lfm/inference/vllm
llama.cpp	LFM2.5-1.2B-Instruct-GGUF
Ollama	docs.liquid.ai/lfm/inference/ollama

Fine-tuning

We recommend fine-tuning on your specific use case for best results.

Method	Link
SFT with Unsloth	Colab Notebook
SFT with TRL	Colab Notebook
DPO with TRL	Colab Notebook

See the Fine-tuning documentation for more details.

Benchmarks

Model	GPQA	MMLU-Pro	IFEval	IFBench	Multi-IF	AIME25	BFCLv3
LFM2.5-1.2B-Instruct (BF16)	35.81	44.76	85.76	47.33	61.22	12.33	49.04
Qwen3-1.7B	34.85	42.91	73.68	21.33	56.48	9.33	46.30
Granite 4.0-1B	24.24	33.53	79.61	21.00	43.65	3.33	52.43
Llama 3.2 1B Instruct	16.57	20.80	52.37	15.93	30.16	0.33	21.44
Gemma 3 1B IT	24.24	14.04	63.25	20.47	44.31	1.00	16.64

Resources

Contact

For enterprise solutions and edge deployment, contact sales@liquid.ai.

Citation

@article{liquidai2025lfm2,
  title={LFM2 Technical Report},
  author={Liquid AI},
  journal={arXiv preprint arXiv:2511.23404},
  year={2025}
}