LFM2-2.6B-Exp

RL-only post-trained 2.6B model with improved math and reasoning capabilities

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LFM2-2.6B-Exp is an experimental checkpoint of LFM2-2.6B with RL-only post-training, delivering improved performance on math and reasoning benchmarks. Use this model when you need stronger analytical capabilities.

HFGGUF

Specifications

Property	Value
Parameters	2.6B
Context Length	32K tokens
Architecture	LFM2 (Dense)

Math & Logic

Strong arithmetic and logical reasoning

On-Device

Runs on phones and laptops

Fine-tunable

TRL compatible (SFT, DPO, GRPO)

Quick Start

Transformers

Install:

pip install transformers torch

Download & Run:

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("LiquidAI/LFM2-2.6B-Exp", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("LiquidAI/LFM2-2.6B-Exp")

input_ids = tokenizer.apply_chat_template(
    [{"role": "user", "content": "What is 15% of 240?"}],
    add_generation_prompt=True, return_tensors="pt"
).to(model.device)

output = model.generate(input_ids, max_new_tokens=256)
print(tokenizer.decode(output[0], skip_special_tokens=True))

llama.cpp

Install:

brew install llama.cpp
pip install huggingface-hub

Download:

huggingface-cli download LiquidAI/LFM2-2.6B-Exp-GGUF \
  lfm2-2.6b-exp-q4_k_m.gguf --local-dir .

Run:

llama-cli -m lfm2-2.6b-exp-q4_k_m.gguf \
  -p "What is 15% of 240?" -n 256

vLLM

Install:

pip install vllm

Run:

from vllm import LLM, SamplingParams

llm = LLM(model="LiquidAI/LFM2-2.6B-Exp")
output = llm.chat("What is 15% of 240?", SamplingParams(max_tokens=256))
print(output[0].outputs[0].text)