LFM2.5-1.2B-Base

Pre-trained 1.2B parameter base model for fine-tuning and custom applications

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LFM2.5-1.2B-Base is the pre-trained foundation model for the LFM2.5 series. Ideal for fine-tuning on custom datasets or building specialized checkpoints. Not instruction-tuned—use LFM2.5-1.2B-Instruct for chat applications.

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Specifications

Property	Value
Parameters	1.2B
Context Length	32K tokens
Architecture	LFM2.5 (Dense)

Fine-tuning

TRL compatible (SFT, DPO, GRPO)

Custom Training

Build domain-specific models

32K Context

Extended context for long documents

Quick Start

Transformers

Install:

pip install "transformers>=5.2.0" torch accelerate

Download & Run:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "LiquidAI/LFM2.5-1.2B-Base"
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    dtype="bfloat16",
)
tokenizer = AutoTokenizer.from_pretrained(model_id)

# Base model uses raw text completion (not chat template)
inputs = tokenizer("The future of AI is", return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(output[0], skip_special_tokens=True))

vLLM

Install:

pip install vllm==0.14

Run:

from vllm import LLM, SamplingParams

llm = LLM(model="LiquidAI/LFM2.5-1.2B-Base")

sampling_params = SamplingParams(
    temperature=0.8,
    min_p=0.05,
    max_tokens=512,
)

# Base model uses raw text completion
output = llm.generate("The future of AI is", sampling_params)
print(output[0].outputs[0].text)

SGLang

Install:

uv pip install "sglang>=0.5.10"

Launch server:

sglang serve \
    --model-path LiquidAI/LFM2.5-1.2B-Base \
    --host 0.0.0.0 \
    --port 30000

Query (OpenAI-compatible):

from openai import OpenAI

client = OpenAI(base_url="http://localhost:30000/v1", api_key="None")

# Base model uses raw text completion (not chat)
response = client.completions.create(
    model="LiquidAI/LFM2.5-1.2B-Base",
    prompt="The future of AI is",
    temperature=0.8,
    max_tokens=512,
)

print(response.choices[0].text)