LFM2.5-1.2B-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 in our blog post.
🗒️ Model Details
| Model | Parameters | Description |
|---|---|---|
| LFM2.5-1.2B-Base | 1.2B | Pre-trained base model for fine-tuning |
| LFM2.5-1.2B-Instruct | 1.2B | General-purpose instruction-tuned model |
| LFM2.5-1.2B-Thinking | 1.2B | General-purpose reasoning model |
| LFM2.5-1.2B-JP | 1.2B | Japanese-optimized chat model |
| LFM2.5-VL-1.6B | 1.6B | Vision-language model with fast inference |
| LFM2.5-Audio-1.5B | 1.5B | Audio-language model for speech and text I/O |
LFM2.5-1.2B-Base is the pre-trained text-only checkpoint, used to create all the LFM2.5-1.2B variants. It has the following features:
- Number of parameters: 1.17B
- 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, Spanish
| Model | Description |
|---|---|
| LFM2.5-1.2B-Base | Original model checkpoint in native format. Best for fine-tuning or inference with Transformers and vLLM. |
| LFM2.5-1.2B-Base-GGUF | Quantized format for llama.cpp and compatible tools. Optimized for CPU inference and local deployment with reduced memory usage. |
| LFM2.5-1.2B-Base-ONNX | ONNX Runtime format for cross-platform deployment. Enables hardware-accelerated inference across diverse environments (cloud, edge, mobile). |
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 |
Here's a quick start example with transformers:
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_id = "LiquidAI/LFM2.5-1.2B-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)
🔧 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 |
📬 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}
}