---
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
- edge
---
# LFM2-1.2B
LFM2 is a new generation of hybrid models developed by [Liquid AI](https://www.liquid.ai/), specifically designed for edge AI and on-device deployment. It sets a new standard in terms of quality, speed, and memory efficiency.
We're releasing the weights of three post-trained checkpoints with 350M, 700M, and 1.2B parameters. They provide the following key features to create AI-powered edge applications:
* **Fast training & inference** – LFM2 achieves 3x faster training compared to its previous generation. It also benefits from 2x faster decode and prefill speed on CPU compared to Qwen3.
* **Best performance** – LFM2 outperforms similarly-sized models across multiple benchmark categories, including knowledge, mathematics, instruction following, and multilingual capabilities.
* **New architecture** – LFM2 is a new hybrid Liquid model with multiplicative gates and short convolutions.
* **Flexible deployment** – LFM2 runs efficiently on CPU, GPU, and NPU hardware for flexible deployment on smartphones, laptops, or vehicles.
Find more information about LFM2 in our [blog post](https://www.liquid.ai/blog/liquid-foundation-models-v2-our-second-series-of-generative-ai-models).
## 📄 Model details
Due to their small size, **we recommend fine-tuning LFM2 models on narrow use cases** to maximize performance.
They are particularly suited for agentic tasks, data extraction, RAG, creative writing, and multi-turn conversations.
However, we do not recommend using them for tasks that are knowledge-intensive or require programming skills.
| Property | Value |
| ------------------- | ----------------------------- |
| **Parameters** | 1,170,340,608 |
| **Layers** | 16 (10 conv + 6 attn) |
| **Context length** | 32,768 tokens |
| **Vocabulary size** | 65,536 |
| **Precision** | bfloat16 |
| **Training budget** | 10 trillion tokens |
| **License** | LFM Open License v1.0 |
**Supported languages**: English, Arabic, Chinese, French, German, Japanese, Korean, and Spanish.
**Generation parameters**: We recommend the following parameters:
* `temperature=0.3`
* `min_p=0.15`
* `repetition_penalty=1.05`
**Chat template**: LFM2 uses a ChatML-like chat template as follows:
```
<|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
It's a tiny nematode that lives in temperate soil environments.<|im_end|>
```
You can apply it using the dedicated [`.apply_chat_template()`](https://huggingface.co/docs/transformers/en/chat_templating#applychattemplate) function from Hugging Face transformers.
**Tool use**: It consists of four main steps:
1. **Function definition**: LFM2 takes JSON function definitions as input (JSON objects between `<|tool_list_start|>` and `<|tool_list_end|>` special tokens), usually in the system prompt
2. **Function call**: LFM2 writes Pythonic function calls (a Python list between `<|tool_call_start|>` and `<|tool_call_end|>` special tokens), as the assistant answer.
3. **Function execution**: The function call is executed and the result is returned (string between `<|tool_response_start|>` and `<|tool_response_end|>` special tokens), as a "tool" role.
4. **Final answer**: LFM2 interprets the outcome of the function call to address the original user prompt in plain text.
Here is a simple example of a conversation using tool use:
```
<|startoftext|><|im_start|>system
List of tools: <|tool_list_start|>[{"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"]}}]<|tool_list_end|><|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
<|tool_response_start|>{"candidate_id": "12345", "status": "Interview Scheduled", "position": "Clinical Research Associate", "date": "2023-11-20"}<|tool_response_end|><|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|>
```
**Architecture**: Hybrid model with multiplicative gates and short convolutions: 10 double-gated short-range LIV convolution blocks and 6 grouped query attention (GQA) blocks.
**Pre-training mixture**: Approximately 75% English, 20% multilingual, and 5% code data sourced from the web and licensed materials.
**Training approach**:
* Knowledge distillation using [LFM1-7B](https://www.liquid.ai/blog/introducing-lfm-7b-setting-new-standards-for-efficient-language-models) as teacher model
* Very large-scale SFT on 50% downstream tasks, 50% general domains
* Custom DPO with length normalization and semi-online datasets
* Iterative model merging
## 🏃 How to run LFM2
You can run LFM2 with transformers and llama.cpp. vLLM support is coming.
### 1. Transformers
To run LFM2, you need to install Hugging Face [`transformers`](https://github.com/huggingface/transformers) from source (v4.54.0.dev0).
You can update or install it with the following command: `pip install "transformers @ git+https://github.com/huggingface/transformers.git@main"`.
Here is an example of how to generate an answer with transformers in Python:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load model and tokenizer
model_id = "LiquidAI/LFM2-1.2B"
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
torch_dtype="bfloat16",
trust_remote_code=True,
# attn_implementation="flash_attention_2" <- uncomment on compatible GPU
)
tokenizer = AutoTokenizer.from_pretrained(model_id)
# Generate answer
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=0.3,
min_p=0.15,
repetition_penalty=1.05,
max_new_tokens=512,
)
print(tokenizer.decode(output[0], skip_special_tokens=False))
# <|startoftext|><|im_start|>user
# What is C. elegans?<|im_end|>
# <|im_start|>assistant
# C. elegans, also known as Caenorhabditis elegans, is a small, free-living
# nematode worm (roundworm) that belongs to the phylum Nematoda.
```
You can directly run and test the model with this [Colab notebook](https://colab.research.google.com/drive/1_q3jQ6LtyiuPzFZv7Vw8xSfPU5FwkKZY?usp=sharing).
### 2. Llama.cpp
You can run LFM2 with llama.cpp using its [GGUF checkpoint](https://huggingface.co/LiquidAI/LFM2-1.2B-GGUF). Find more information in the model card.
## 🔧 How to fine-tune LFM2
We recommend fine-tuning LFM2 models on your use cases to maximize performance.
| Notebook | Description | Link |
|-------|------|------|
| SFT (Axolotl) | Supervised Fine-Tuning (SFT) notebook with a LoRA adapter in Axolotl. | |
| SFT (TRL) | Supervised Fine-Tuning (SFT) notebook with a LoRA adapter in TRL. | |
| DPO (TRL) | Preference alignment with Direct Preference Optimization (DPO) in TRL. | |
## 📈 Performance
LFM2 outperforms similar-sized models across different evaluation categories.
### 1. Automated benchmarks
| Model | MMLU | GPQA | IFEval | IFBench | GSM8K | MGSM | MMMLU |
|-------|------|------|--------|---------|-------|------|-------|
| LFM2-350M | 43.43 | 27.46 | 65.12 | 16.41 | 30.1 | 29.52 | 37.99 |
| LFM2-700M | 49.9 | 28.48 | 72.23 | 20.56 | 46.4 | 45.36 | 43.28 |
| LFM2-1.2B | *55.23* | **31.47** | **74.89** | *20.7* | *58.3* | *55.04* | **46.73** |
| Qwen3-0.6B | 44.93 | 22.14 | 64.24 | 19.75 | 36.47 | 41.28 | 30.84 |
| Qwen3-1.7B | **59.11** | 27.72 | *73.98* | **21.27** | 51.4 | **66.56** | *46.51* |
| Llama-3.2-1B-Instruct | 46.6 | *28.84* | 52.39 | 16.86 | 35.71 | 29.12 | 38.15 |
| gemma-3-1b-it | 40.08 | 21.07 | 62.9 | 17.72 | **59.59** | 43.6 | 34.43 |
### 2. LLM-as-a-Judge
### 3. Inference
#### Throughput comparison on CPU in ExecuTorch
#### Throughput comparison on CPU in Llama.cpp
## 📬 Contact
If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact).
|
| SFT (TRL) | Supervised Fine-Tuning (SFT) notebook with a LoRA adapter in TRL. |