LFM2 1.2B GGUF

🚀 LFM2-1.2B

LFM2 is a new - generation hybrid model developed by Liquid AI. It's specifically designed for edge AI and on - device deployment, setting new standards in quality, speed, and memory efficiency.

✨ Features

• Fast training & inference: LFM2 trains 3x faster than its previous generation and has 2x faster decode and prefill speed on CPU compared to Qwen3.
• Best performance: It outperforms similarly - sized models in multiple benchmark categories, including knowledge, mathematics, instruction following, and multilingual capabilities.
• New architecture: A new hybrid Liquid model with multiplicative gates and short convolutions.
• Flexible deployment: Runs efficiently on CPU, GPU, and NPU hardware, enabling flexible deployment on smartphones, laptops, or vehicles.

Find more information about LFM2 in our blog post.

📦 Installation

To run LFM2, you need to install Hugging Face 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".

💻 Usage Examples

Basic Usage

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.

📚 Documentation

📄 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.

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() 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 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.

🔧 Technical Details

We recommend fine - tuning LFM2 models on your use cases to maximize performance.

Notebook	Description	Link
SFT + LoRA	Supervised Fine - Tuning (SFT) notebook with a LoRA adapter in TRL.
DPO	Preference alignment with Direct Preference Optimization (DPO) in TRL.

📈 Performance

LFM2 outperforms similar - sized models across different evaluation categories.

1. Automated benchmarks

2. LLM - as - a - Judge

3. Inference

Throughput comparison on CPU in ExecuTorch

Throughput comparison on CPU in Llama.cpp

📄 License

The model is under the LFM Open License v1.0. You can find the license details here.

📬 Contact

If you are interested in custom solutions with edge deployment, please contact our sales team.

⚠️ Important Note

Includes our chat template fixes!
For llama.cpp, use --jinja

💡 Usage Tip

Due to their small size, we recommend fine - tuning LFM2 models on narrow use cases to maximize performance.