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

Libraries

How to use unsloth/LFM2.5-1.2B-Instruct-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="unsloth/LFM2.5-1.2B-Instruct-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("unsloth/LFM2.5-1.2B-Instruct-GGUF", device_map="auto")

llama-cpp-python

How to use unsloth/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="unsloth/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 Settings

llama.cpp

How to use unsloth/LFM2.5-1.2B-Instruct-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 unsloth/LFM2.5-1.2B-Instruct-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf unsloth/LFM2.5-1.2B-Instruct-GGUF:UD-Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf unsloth/LFM2.5-1.2B-Instruct-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf unsloth/LFM2.5-1.2B-Instruct-GGUF:UD-Q4_K_XL

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 unsloth/LFM2.5-1.2B-Instruct-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf unsloth/LFM2.5-1.2B-Instruct-GGUF:UD-Q4_K_XL

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 unsloth/LFM2.5-1.2B-Instruct-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf unsloth/LFM2.5-1.2B-Instruct-GGUF:UD-Q4_K_XL

Use Docker

docker model run hf.co/unsloth/LFM2.5-1.2B-Instruct-GGUF:UD-Q4_K_XL

LM Studio
Jan

vLLM

How to use unsloth/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 "unsloth/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": "unsloth/LFM2.5-1.2B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/unsloth/LFM2.5-1.2B-Instruct-GGUF:UD-Q4_K_XL

SGLang

How to use unsloth/LFM2.5-1.2B-Instruct-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "unsloth/LFM2.5-1.2B-Instruct-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unsloth/LFM2.5-1.2B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "unsloth/LFM2.5-1.2B-Instruct-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unsloth/LFM2.5-1.2B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use unsloth/LFM2.5-1.2B-Instruct-GGUF with Ollama:
```
ollama run hf.co/unsloth/LFM2.5-1.2B-Instruct-GGUF:UD-Q4_K_XL
```

Unsloth Studio

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

How to use unsloth/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 serve -hf unsloth/LFM2.5-1.2B-Instruct-GGUF:UD-Q4_K_XL

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": "unsloth/LFM2.5-1.2B-Instruct-GGUF:UD-Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use unsloth/LFM2.5-1.2B-Instruct-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 unsloth/LFM2.5-1.2B-Instruct-GGUF:UD-Q4_K_XL

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 unsloth/LFM2.5-1.2B-Instruct-GGUF:UD-Q4_K_XL

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use unsloth/LFM2.5-1.2B-Instruct-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf unsloth/LFM2.5-1.2B-Instruct-GGUF:UD-Q4_K_XL

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 "unsloth/LFM2.5-1.2B-Instruct-GGUF:UD-Q4_K_XL" \
  --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"

Docker Model Runner
How to use unsloth/LFM2.5-1.2B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/unsloth/LFM2.5-1.2B-Instruct-GGUF:UD-Q4_K_XL
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull unsloth/LFM2.5-1.2B-Instruct-GGUF:UD-Q4_K_XL

Run and chat with the model

lemonade run user.LFM2.5-1.2B-Instruct-GGUF-UD-Q4_K_XL

List all available models

lemonade list

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

danielhanchen

Super-squash branch 'main' 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
	- unsloth
	- lfm2.5
	- edge
	base_model:
	- LiquidAI/LFM2.5-1.2B-Instruct
	---
	> [!NOTE]
	> Includes Unsloth chat template fixes! <br> For `llama.cpp`, use `--jinja`
	>

	<div>
	<p style="margin-top: 0;margin-bottom: 0;">
	<em><a href="https://docs.unsloth.ai/basics/unsloth-dynamic-v2.0-gguf">Unsloth Dynamic 2.0</a> achieves superior accuracy & outperforms other leading quants.</em>
	</p>
	<div style="display: flex; gap: 5px; align-items: center; ">
	<a href="https://github.com/unslothai/unsloth/">
	<img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133">
	</a>
	<a href="https://discord.gg/unsloth">
	<img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173">
	</a>
	<a href="https://docs.unsloth.ai/">
	<img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143">
	</a>
	</div>
	</div>


	<div align="center">
	<img
	src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png"
	alt="Liquid AI"
	style="width: 100%; max-width: 100%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;"
	/>
	<div style="display: flex; justify-content: center; gap: 0.5em; margin-bottom: 1em;">
	<a href="https://playground.liquid.ai/"><strong>Try LFM</strong></a> •
	<a href="https://docs.liquid.ai/lfm"><strong>Documentation</strong></a> •
	<a href="https://leap.liquid.ai/"><strong>LEAP</strong></a>
	</div>
	</div>

	# LFM2.5-1.2B-Instruct

	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.

	- Best-in-class performance: A 1.2B model rivaling much larger models, bringing high-quality AI to your pocket.
	- Fast edge inference: 239 tok/s decode on AMD CPU, 82 tok/s on mobile NPU. Runs under 1GB of memory with day-one support for llama.cpp, MLX, and vLLM.
	- Scaled training: Extended pre-training from 10T to 28T tokens and large-scale multi-stage reinforcement learning.

	![image](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/dxnYF2fuLpulismtFSGFi.png)

	Find more information about LFM2.5 in our [blog post](https://www.liquid.ai/blog/introducing-lfm2-5-the-next-generation-of-on-device-ai).

	## 🗒️ Model Details

	\| Model \| Parameters \| Description \|
	\|-------\|------------\|-------------\|
	\| [LFM2.5-1.2B-Base](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Base) \| 1.2B \| Pre-trained base model for fine-tuning \|
	\| [LFM2.5-1.2B-Instruct](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct) \| 1.2B \| General-purpose instruction-tuned model \|
	\| [LFM2.5-1.2B-JP](https://huggingface.co/LiquidAI/LFM2.5-1.2B-JP) \| 1.2B \| Japanese-optimized chat model \|
	\| [LFM2.5-VL-1.6B](https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B) \| 1.6B \| Vision-language model with fast inference \|
	\| [LFM2.5-Audio-1.5B](https://huggingface.co/LiquidAI/LFM2.5-Audio-1.5B) \| 1.5B \| Audio-language model for speech and text I/O \|

	LFM2.5-1.2B-Instruct is a general-purpose text-only model with the following features:

	- Number of parameters: 1.17B
	- Number of layers: 16 (10 double-gated LIV convolution blocks + 6 GQA blocks)
	- Training budget: 28T tokens
	- Context length: 32,768 tokens
	- Vocabulary size: 65,536
	- Languages: English, Arabic, Chinese, French, German, Japanese, Korean, Spanish
	- Generation parameters:
	- `temperature: 0.1`
	- `top_k: 50`
	- `top_p: 0.1`
	- `repetition_penalty: 1.05`

	\| Model \| Description \|
	\|-------\|-------------\|
	\| [LFM2.5-1.2B-Instruct](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct) \| Original model checkpoint in native format. Best for fine-tuning or inference with Transformers and vLLM. \|
	\| [LFM2.5-1.2B-Instruct-GGUF](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct-GGUF) \| Quantized format for llama.cpp and compatible tools. Optimized for CPU inference and local deployment with reduced memory usage. \|
	\| [LFM2.5-1.2B-Instruct-ONNX](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct-ONNX) \| ONNX Runtime format for cross-platform deployment. Enables hardware-accelerated inference across diverse environments (cloud, edge, mobile). \|

	We recommend using it for agentic tasks, data extraction, and RAG. It is not recommended for knowledge-intensive tasks and programming.

	### Chat Template

	LFM2.5 uses a ChatML-like format. See the [Chat Template documentation](https://docs.liquid.ai/lfm/key-concepts/chat-template) for details. Example:

	```
	<\|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
	```

	You can use [`tokenizer.apply_chat_template()`](https://huggingface.co/docs/transformers/en/chat_templating#using-applychattemplate) to format your messages automatically.

	### Tool Use

	LFM2.5 supports function calling as follows:

	1. Function definition: We recommend providing the list of tools as a JSON object in the system prompt. You can also use the [`tokenizer.apply_chat_template()`](https://huggingface.co/docs/transformers/en/chat_extras#passing-tools) function with tools.
	2. Function call: By default, LFM2.5 writes Pythonic function calls (a Python list between `<\|tool_call_start\|>` and `<\|tool_call_end\|>` special tokens), as the assistant answer. You can override this behavior by asking the model to output JSON function calls in the system prompt.
	3. Function execution: The function call is executed, and the result is returned as a "tool" role.
	4. Final answer: LFM2 interprets the outcome of the function call to address the original user prompt in plain text.

	See the [Tool Use documentation](https://docs.liquid.ai/lfm/key-concepts/tool-use) for the full guide. Example:

	```
	<\|startoftext\|><\|im_start\|>system
	List of tools: [{"name": "get_candidate_status", "description": "Retrieves the current status of a candidate in the recruitment process", "parameters": {"type": "object", "properties": {"candidate_id": {"type": "string", "description": "Unique identifier for the candidate"}}, "required": ["candidate_id"]}}]<\|im_end\|>
	<\|im_start\|>user
	What is the current status of candidate ID 12345?<\|im_end\|>
	<\|im_start\|>assistant
	<\|tool_call_start\|>[get_candidate_status(candidate_id="12345")]<\|tool_call_end\|>Checking the current status of candidate ID 12345.<\|im_end\|>
	<\|im_start\|>tool
	[{"candidate_id": "12345", "status": "Interview Scheduled", "position": "Clinical Research Associate", "date": "2023-11-20"}]<\|im_end\|>
	<\|im_start\|>assistant
	The candidate with ID 12345 is currently in the "Interview Scheduled" stage for the position of Clinical Research Associate, with an interview date set for 2023-11-20.<\|im_end\|>
	```

	## 🏃 Inference

	LFM2.5 is supported by many inference frameworks. See the [Inference documentation](https://docs.liquid.ai/lfm/inference/transformers) for the full list.

	\| Name \| Description \| Docs \| Notebook \|
	\|------\|-------------\|------\|:--------:\|
	\| [Transformers](https://github.com/huggingface/transformers) \| Simple inference with direct access to model internals. \| <a href="https://docs.liquid.ai/lfm/inference/transformers">Link</a> \| <a href="https://colab.research.google.com/drive/1_q3jQ6LtyiuPzFZv7Vw8xSfPU5FwkKZY?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> \|
	\| [vLLM](https://github.com/vllm-project/vllm) \| High-throughput production deployments with GPU. \| <a href="https://docs.liquid.ai/lfm/inference/vllm">Link</a> \| <a href="https://colab.research.google.com/drive/1VfyscuHP8A3we_YpnzuabYJzr5ju0Mit?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> \|
	\| [llama.cpp](https://github.com/ggml-org/llama.cpp) \| Cross-platform inference with CPU offloading. \| <a href="https://docs.liquid.ai/lfm/inference/llama-cpp">Link</a> \| <a href="https://colab.research.google.com/drive/1ohLl3w47OQZA4ELo46i5E4Z6oGWBAyo8?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> \|
	\| [MLX](https://github.com/ml-explore/mlx) \| Apple's machine learning framework optimized for Apple Silicon. \| <a href="https://docs.liquid.ai/lfm/inference/mlx">Link</a> \| — \|
	\| [LM Studio](https://lmstudio.ai/) \| Desktop application for running LLMs locally. \| <a href="https://docs.liquid.ai/lfm/inference/lm-studio">Link</a> \| — \|

	Here's a quick start example with Transformers:

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

	model_id = "LiquidAI/LFM2.5-1.2B-Instruct"
	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	device_map="auto",
	dtype="bfloat16",
	# attn_implementation="flash_attention_2" <- uncomment on compatible GPU
	)
	tokenizer = AutoTokenizer.from_pretrained(model_id)
	streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

	prompt = "What is C. elegans?"

	input_ids = tokenizer.apply_chat_template(
	[{"role": "user", "content": prompt}],
	add_generation_prompt=True,
	return_tensors="pt",
	tokenize=True,
	).to(model.device)

	output = model.generate(
	input_ids,
	do_sample=True,
	temperature=0.1,
	top_k=50,
	top_p=0.1,
	repetition_penalty=1.05,
	max_new_tokens=512,
	streamer=streamer,
	)
	```

	## 🔧 Fine-Tuning

	We recommend fine-tuning LFM2.5 for your specific use case to achieve the best results.

	\| Name \| Description \| Docs \| Notebook \|
	\|------\|-------------\|------\|----------\|
	\| SFT ([Unsloth](https://github.com/unslothai/unsloth)) \| Supervised Fine-Tuning with LoRA using Unsloth. \| <a href="https://docs.liquid.ai/lfm/fine-tuning/unsloth">Link</a> \| <a href="https://colab.research.google.com/drive/1HROdGaPFt1tATniBcos11-doVaH7kOI3?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> \|
	\| SFT ([TRL](https://github.com/huggingface/trl)) \| Supervised Fine-Tuning with LoRA using TRL. \| <a href="https://docs.liquid.ai/lfm/fine-tuning/trl">Link</a> \| <a href="https://colab.research.google.com/drive/1j5Hk_SyBb2soUsuhU0eIEA9GwLNRnElF?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> \|
	\| DPO ([TRL](https://github.com/huggingface/trl)) \| Direct Preference Optimization with LoRA using TRL. \| <a href="https://docs.liquid.ai/lfm/fine-tuning/trl">Link</a> \| <a href="https://colab.research.google.com/drive/1MQdsPxFHeZweGsNx4RH7Ia8lG8PiGE1t?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> \|

	## 📊 Performance

	### Benchmarks

	We compared LFM2.5-1.2B-Instruct with relevant sub-2B models on a diverse suite of benchmarks.

	\| Model \| GPQA \| MMLU-Pro \| IFEval \| IFBench \| Multi-IF \| AIME25 \| BFCLv3 \|
	\|-------\|------\|----------\|--------\|---------\|----------\|--------\|--------\|
	\| LFM2.5-1.2B-Instruct \| 38.89 \| 44.35 \| 86.23 \| 47.33 \| 60.98 \| 14.00 \| 49.12 \|
	\| 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 \|

	GPQA, MMLU-Pro, IFBench, and AIME25 follow [ArtificialAnalysis's methodology](https://artificialanalysis.ai/methodology/intelligence-benchmarking). For IFEval and Multi-IF, we report the average score across strict and loose prompt and instruction accuracies. For BFCLv3, we report the final weighted average score with a custom Liquid handler to support our tool use template.

	### Inference speed

	LFM2.5-1.2B-Instruct offers extremely fast inference speed on CPUs with a low memory profile compared to similar-sized models.

	![image](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/dbbI-15p9re2ROhAkqnZm.png)

	In addition, we are partnering with AMD, Qualcomm, and Nexa AI to bring the LFM2.5 family to NPUs. These optimized models are available through our partners, enabling highly efficient on-device inference.

	\| Device \| Inference \| Framework \| Model \| Prefill (tok/s) \| Decode (tok/s) \| Memory (GB) \|
	\| ---------------------------------------------------- \| --------- \| ---------------- \| -------------------- \| --------------- \| -------------- \| ----------- \|
	\| Qualcomm Snapdragon® X Elite \| NPU \| NexaML \| LFM2.5-1.2B-instruct \| 2591 \| 63 \| 0.9GB \|
	\| Qualcomm Snapdragon® Gen4 (ROG Phone9 Pro) \| NPU \| NexaML \| LFM2.5-1.2B-instruct \| 4391 \| 82 \| 0.9GB \|
	\| Qualcomm Snapdragon® Gen4 (Samsung Galaxy S25 Ultra) \| CPU \| llama.cpp (Q4_0) \| LFM2.5-1.2B-instruct \| 335 \| 70 \| 719MB \|
	\| Qualcomm Snapdragon® Gen4 (Samsung Galaxy S25 Ultra) \| CPU \| llama.cpp (Q4_0) \| Qwen3-1.7B \| 181 \| 40 \| 1306MB \|

	These capabilities unlock new deployment scenarios across various devices, including vehicles, mobile devices, laptops, IoT devices, and embedded systems.

	## Contact

	For enterprise solutions and edge deployment, contact [sales@liquid.ai](mailto:sales@liquid.ai).

	## Citation

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