---
library_name: transformers
license: other
license_name: lfm1.0
license_link: LICENSE
language:
- ar
- zh
- en
- fr
- de
- hi
- id
- it
- ja
- ko
- pl
- pt
- ru
- es
- th
- vi
pipeline_tag: text-generation
tags:
- liquid
- lfm2.5
- edge
base_mode: LiquidAI/LFM2.5-2.6B-Base
---
# LFM2.5-2.6B
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](https://www.liquid.ai/blog/lfm2-5-2-6b).
## 🗒️ Model Details
| Model | Parameters | Description |
| -------------------------------------------------------------------- | ---------- | -------------------------------------- |
| [LFM2.5-2.6B-Base](https://huggingface.co/LiquidAI/LFM2.5-2.6B-Base) | 2.6B | Pre-trained base model for fine-tuning |
| **[LFM2.5-2.6B](https://huggingface.co/LiquidAI/LFM2.5-2.6B)** | 2.6B | Post-trained for agentic workloads |
LFM2.5-2.6B-Base is the pre-trained text-only checkpoint, used to create all the LFM2.5-2.6B variants. It has the following features:
- **Total parameters**: 2.69B
- **Number of layers**: 30 (22 double-gated short convolution blocks + 8 GQA)
- **Training budget**: 34 trillion tokens
- **Vocabulary size**: 128,000
- **Context length**: 131,072 tokens
- **Languages**: English, Arabic, Chinese, French, German, Italian, Japanese, Korean, Portuguese, Spanish, Vietnamese, Thai, Indonesian, Hindi, Russian, Polish
| Model | Description |
| ----------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------- |
| **[LFM2.5-2.6B](https://huggingface.co/LiquidAI/LFM2.5-2.6B)** | Original model checkpoint in native format. Best for fine-tuning or inference with Transformers, vLLM, and SGLang. |
| **[LFM2.5-2.6B-GGUF](https://huggingface.co/LiquidAI/LFM2.5-2.6B-GGUF)** | Quantized format for llama.cpp and compatible tools. Optimized for CPU inference and local deployment with reduced memory usage. |
| **[LFM2.5-2.6B-ONNX](https://huggingface.co/LiquidAI/LFM2.5-2.6B-ONNX)** | ONNX Runtime format for cross-platform deployment. Enables hardware-accelerated inference across diverse environments (cloud, edge, mobile). |
| **[LFM2.5-2.6B-MLX](https://huggingface.co/LiquidAI/LFM2.5-2.6B-MLX)** | MLX format for Apple Silicon. Optimized for fast inference on Mac devices using the MLX framework. |
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](https://docs.liquid.ai/lfm/inference/transformers) for the full list.
| Name | Description | Docs | Notebook |
|------|-------------|------|:--------:|
| [Transformers](https://github.com/huggingface/transformers) | Simple inference with direct access to model internals. | Link | 
|
| [vLLM](https://github.com/vllm-project/vllm) | High-throughput production deployments with GPU. | Link | |
| [llama.cpp](https://github.com/ggml-org/llama.cpp) | Cross-platform inference with CPU offloading. | Link | |
| [MLX](https://github.com/ml-explore/mlx) | Apple's machine learning framework optimized for Apple Silicon. | Link | — |
| [LM Studio](https://lmstudio.ai/) | Desktop application for running LLMs locally. | Link | — |
| [SGLang](https://github.com/sgl-project/sglang) | High-throughput production deployments with GPU. | Link | - |
Quick start with Transformers (compatible with `transformers>=5.0.0`):
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_id = "LiquidAI/LFM2.5-2.6B-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,
)["input_ids"].to(model.device)
output = model.generate(
input_ids,
do_sample=True,
temperature=0.2,
top_k=80,
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](https://github.com/unslothai/unsloth)) | Continued Pre-Training using Unsloth for text completion. | Link | |
| CPT ([Unsloth](https://github.com/unslothai/unsloth)) | Continued Pre-Training using Unsloth for translation. | Link | |
| SFT ([Unsloth](https://github.com/unslothai/unsloth)) | Supervised Fine-Tuning with LoRA using Unsloth. | Link | |
| SFT ([TRL](https://github.com/huggingface/trl)) | Supervised Fine-Tuning with LoRA using TRL. | Link | |
| DPO ([TRL](https://github.com/huggingface/trl)) | Direct Preference Optimization with LoRA using TRL. | Link | |
| GRPO ([Unsloth](https://github.com/unslothai/unsloth)) | GRPO with LoRA using Unsloth. | Link | |
| GRPO ([TRL](https://github.com/huggingface/trl)) | GRPO with LoRA using TRL. | Link | |
## 📬 Contact
- Got questions or want to connect? [Join our Discord community](https://discord.com/invite/liquid-ai)
- If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact).
## Citation
```bibtex
@article{liquidAI202626B,
author = {Liquid AI},
title = {LFM2.5-2.6B: Agents Everywhere},
journal = {Liquid AI Blog},
year = {2026},
note = {www.liquid.ai/blog/lfm2-5-2-6b},
}
```
```bibtex
@article{liquidai2025lfm2,
title = {LFM2 Technical Report},
author = {Liquid AI},
journal = {arXiv preprint arXiv:2511.23404},
year = {2025}
}
```