LiquidAI / LFM2.5-350M-Base

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LFM2.5-350M-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.

Find more information about LFM2.5-350M in our blog post .

🗒️ Model Details

LFM2.5-350M is a general-purpose text-only model with the following features:

• Number of parameters : 350M
• 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
• Knowledge cutoff : Mid-2024
• Languages : English, Arabic, Chinese, French, German, Japanese, Korean, Portuguese, Spanish

This pre-trained checkpoint is only recommended for tasks that require heavy fine-tuning, like language-specific (e.g., Japanese) or domain-specific (e.g., medical) assistants, training on proprietary data, or experimenting with novel post-training approaches.

🏃 Inference

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

Name	Description	Docs	Notebook
Transformers	Simple inference with direct access to model internals.	Link
vLLM	High-throughput production deployments with GPU.	Link
llama.cpp	Cross-platform inference with CPU offloading.	Link
MLX	Apple's machine learning framework optimized for Apple Silicon.	Link	—
LM Studio	Desktop application for running LLMs locally.	Link	—

Here's a quick start example with Transformers:

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

model_id = "LiquidAI/LFM2.5-350M-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=0.1,
    top_k=50,
    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
CPT ( Unsloth )	Continued Pre-Training using Unsloth for text completion.	Link
CPT ( Unsloth )	Continued Pre-Training using Unsloth for translation.	Link
SFT ( Unsloth )	Supervised Fine-Tuning with LoRA using Unsloth.	Link
SFT ( TRL )	Supervised Fine-Tuning with LoRA using TRL.	Link
DPO ( TRL )	Direct Preference Optimization with LoRA using TRL.	Link
GRPO ( Unsloth )	GRPO with LoRA using Unsloth.	Link
GRPO ( TRL )	GRPO with LoRA using TRL.	Link

📬 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{liquidAI2026350M,
  author = {Liquid AI},
  title = {LFM2.5-350M: No Size Left Behind},
  journal = {Liquid AI Blog},
  year = {2026},
  note = {www.liquid.ai/blog/lfm2-5-350m-no-size-left-behind},
}

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