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LiquidAI/LFM2-8B-A1B

sourceHugging Faceotherupdated 2mo agoView on Hugging Face

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<center> <div style="text-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> <div style="display: flex; justify-content: center; gap: 0.5em;"> <a href="https://playground.liquid.ai/"><strong>Try LFM</strong></a> • <a href="https://docs.liquid.ai/lfm/getting-started/welcome"><strong>Docs</strong></a> • <a href="https://leap.liquid.ai/"><strong>LEAP</strong></a> • <a href="https://discord.com/invite/liquid-ai"><strong>Discord</strong></a> </div> </center>

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LFM2-8B-A1B

LFM2 is a new generation of hybrid models developed by 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 our first MoE based on LFM2, with 8.3B total parameters and 1.5B active parameters.

—LFM2-8B-A1B is the best on-device MoE in terms of both quality (comparable to 3-4B dense models) and speed (faster than Qwen3-1.7B).
—Code and knowledge capabilities are significantly improved compared to LFM2-2.6B.
—Quantized variants fit comfortably on high-end phones, tablets, and laptops.

Find more information about LFM2-8B-A1B in our blog post.

📄 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	[LFM2-8B-A1B](https://huggingface.co/LiquidAI/LFM2-8B-A1B)	[LFM2-24B-A2B](https://huggingface.co/LiquidAI/LFM2-24B-A2B)
Total parameters	8.3B	24B
Active parameters	1.5B	2.3B
Layers	24 (18 conv + 6 attn)	40 (30 conv + 10 attn)
Context length	32,768 tokens	32,768 tokens
Vocabulary size	65,536	65,536
Training precision	Mixed BF16/FP8	Mixed BF16/FP8
Training budget	12 trillion tokens	17 trillion tokens
License	LFM Open License v1.0	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 automatically 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|>

You can directly pass tools as JSON schema or Python functions with .apply_chat_template() as shown in this page to automatically format the system prompt.

Architecture: Hybrid model with multiplicative gates and short convolutions: 18 double-gated short-range 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:

—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

1. Transformers

To run LFM2, you need to install Hugging Face `transformers`:

bash

pip install transformers

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/LFM2-8B-A1B"
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)

# 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.

2. vLLM

You can run the model in `vLLM` by building from source:

bash

git clone https://github.com/vllm-project/vllm.git
cd vllm
pip install -e . -v

Here is an example of how to use it for inference:

python

from vllm import LLM, SamplingParams

prompts = [
    [
        {
            "content": "What is C. elegans?",
            "role": "user",
        },
    ],
    [
        {
            "content": "Say hi in JSON format",
            "role": "user",
        },
    ],
    [
        {
            "content": "Define AI in Spanish",
            "role": "user",
        },
    ],
]

sampling_params = SamplingParams(
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_tokens=30
)

llm = LLM(model="LiquidAI/LFM2-8B-A1B", dtype="bfloat16")

outputs = llm.chat(prompts, sampling_params)

for i, output in enumerate(outputs):
    prompt = prompts[i][0]["content"]
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

3. llama.cpp

You can run LFM2 with llama.cpp using its GGUF checkpoint. 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 (TRL)	Supervised Fine-Tuning (SFT) notebook with a LoRA adapter using TRL.	<a href="https://colab.research.google.com/drive/1OXLEuSmzF4AjJ7yqRCDTn-ltvFjoGR9j?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHabLXysEu2E.png" width="110" alt="Colab link"></a>
DPO (TRL)	Preference alignment with Direct Preference Optimization (DPO) using TRL.	<a href="https://colab.research.google.com/drive/1Q8hIHIQ8oofshcNYHUcYp1akUcZ-ufSn?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHabLXysEu2E.png" width="110" alt="Colab link"></a>

📈 Performance

1. Automated benchmarks

Compared to similar-sized models, LFM2-8B-A1B displays strong performance in instruction following and math while also running significantly faster.

Model	MMLU	MMLU-Pro	GPQA	IFEval	IFBench	Multi-IF
LFM2-8B-A1B	64.84	37.42	29.29	77.58	25.85	58.19
LFM2-2.6B	64.42	25.96	26.57	79.56	22.19	60.26
Llama-3.2-3B-Instruct	60.35	22.25	30.6	71.43	20.78	50.91
SmolLM3-3B	59.84	23.90	26.31	72.44	17.93	58.86
gemma-3-4b-it	58.35	34.76	29.51	76.85	23.53	66.61
Qwen3-4B-Instruct-2507	72.25	52.31	34.85	85.62	30.28	75.54
granite-4.0-h-tiny	66.79	32.03	26.46	81.06	18.37	52.99

Model	GSM8K	GSMPlus	MATH 500	MATH Lvl 5	MGSM	MMMLU
LFM2-8B-A1B	84.38	64.76	74.2	62.38	72.4	55.26
LFM2-2.6B	82.41	60.75	63.6	54.38	74.32	55.39
Llama-3.2-3B-Instruct	75.21	38.68	41.2	24.06	61.68	47.92
SmolLM3-3B	81.12	58.91	73.6	51.93	68.72	50.02
gemma-3-4b-it	89.92	68.38	73.2	52.18	87.28	50.14
Qwen3-4B-Instruct-2507	68.46	56.16	85.6	73.62	81.76	60.67
granite-4.0-h-tiny	82.64	59.14	58.2	36.11	73.68	56.13

Model	Active params	LCB v6	LCB v5	HumanEval+	Creative Writing v3
LFM2-8B-A1B	1.5B	21.04%	21.36%	69.51%	44.22%
Gemma-3-1b-it	1B	4.27%	4.43%	37.20%	41.67%
Granite-4.0-h-tiny	1B	26.73%	27.27%	73.78%	32.60%
Llama-3.2-1B-Instruct	1.2B	4.08%	3.64%	23.17%	31.43%
Qwen2.5-1.5B-Instruct	1.5B	11.18%	10.57%	48.78%	22.18%
Qwen3-1.7B (/no_think)	1.7B	24.07%	26.48%	60.98%	31.56%
LFM2-2.6B	2.6B	14.41%	14.43%	57.93%	38.79%
SmolLM3-3B	3.1B	19.05%	19.20%	60.37%	36.44%
Llama-3.2-3B-Instruct	3.2B	11.47%	11.48%	24.06%	38.84%
Qwen3-4B (/no_think)	4B	36.11%	38.64%	71.95%	37.49%
Qwen3-4B-Instruct-2507	4B	48.72%	50.80%	82.32%	51.71%
Gemma-3-4b-it	4.3B	18.86%	19.09%	62.8%	68.56%

2. Inference

LFM2-8B-A1B is significantly faster than models with a similar number of active parameters, like Qwen3-1.7B.

The following plots showcase the performance of different models under int4 quantization with int8 dynamic activations on the AMD Ryzen AI 9 HX 370 CPU, using 16 threads. The results are obtained using our internal XNNPACK-based inference stack, and a custom CPU MoE kernel.

📬 Contact

—Got questions or want to connect? Join our Discord community
—If you are interested in custom solutions with edge deployment, please contact our sales team.

Citation

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