LFM2.5-8B-A1B-Base

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LFM2.5-8B-A1B-Base#

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.

• On-device personal assistant: Designed to power real-life applications, chaining tool calls, and following complex instructions on all devices.
• Compressed performance: Competitive with much larger dense and MoE models on instruction following and agentic tasks.
• Unmatched throughput: Fastest in its size class on both CPU and GPU inference, with day-one support for llama.cpp, MLX, vLLM, and SGLang.

Find more information about LFM2.5-8B-A1B in our blog post.

Image: image

**AA-Omniscience Index (higher is better) rewards correct answers and penalizes hallucinations. Scores range from -100 to 100. See more results on Artificial Analysis.*

🗒️ Model Details#

LFM2.5-8B-A1B is a general-purpose text-only model with the following features:

• Total parameters: 8.3B
• Active parameters: 1.5B
• Number of layers: 24 (18 double-gated LIV conv + 6 GQA)
• Training budget: 38 trillion tokens
• Context length: 131,072
• Vocabulary size: 128,000
• Languages: English, Arabic, Chinese, French, German, Japanese, Korean, Portuguese, Spanish
• Generation parameters: We recommend the following parameters:
  • temperature: 0.2
  • top_p: 80
  • repetition_penalty: 1.05

We recommend using LFM2.5-8B-A1B for agentic workflows, tool use, structured outputs, multilingual assistants, and on-device personal-assistant applications. It is not the best fit for heavy programming or knowledge-intensive question answering without retrieval.

🏃 Inference#

LFM2.5-8B-A1B is supported by many inference frameworks. See the Inference documentation for the full list.

Quick start with Transformers (compatible with transformers>=5.0.0):

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

model_id = "LiquidAI/LFM2.5-8B-A1B-Base"
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=0.2,
    top_k=80,
    repetition_penalty=1.05,
    max_new_tokens=8192,
    streamer=streamer,
)

🔧 Fine-Tuning#

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

📬 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{liquidAI20268BA1B,
  author  = {Liquid AI},
  title   = {LFM2.5-8B-A1B: Personal Assistant On Your Laptop},
  journal = {Liquid AI Blog},
  year    = {2026},
  note    = {www.liquid.ai/blog/lfm2-5-8b-a1b},
}

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