LFM2.5-24B-A2B

← Back to Text Models LFM2.5-24B-A2B is Liquid AI’s largest Mixture-of-Experts model, combining 24B total parameters with only 2B active parameters per token and a 131K context window. It is a reasoning model that always thinks before it answers, post-trained for tool calling and multi-step agentic tasks on-device.

HF Base GGUF MLX ONNX

Specifications

Property	Value
Parameters	24B (2B active)
Context Length	131K tokens
Architecture	LFM2.5 (MoE), 40 layers (30 gated short convolution + 10 GQA), 64 experts per MoE block with top-4 routing
Vocabulary Size	128K
Languages	English, Arabic, Chinese, French, German, Italian, Japanese, Korean, Portuguese, Spanish, Vietnamese, Thai, Indonesian, Hindi, Russian, Polish
Generation Parameters	`temperature=0.1`, `top_k=50`, `repetition_penalty=1.05`

Local Agents

Post-trained inside agent harnesses for reliable tool calling

Reasoning

Thinks before it answers, at 2B active cost per token

131K Context

Long agent trajectories, documents, and conversations

Quick Start

Transformers
llama.cpp
MLX
vLLM
SGLang

Quick start with Transformers (compatible with transformers>=5.0.0):

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

model_id = "LiquidAI/LFM2.5-24B-A2B"
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=0.1,
    top_k=50,
    repetition_penalty=1.05,
    max_new_tokens=512,
    streamer=streamer,
)

Install (requires mlx-lm>=0.32.0, earlier versions route the experts incorrectly):

pip install -U "mlx-lm>=0.32.0"

Run:

from mlx_lm import load, generate
from mlx_lm.sample_utils import make_sampler, make_logits_processors

model, tokenizer = load("LiquidAI/LFM2.5-24B-A2B-MLX-4bit")

prompt = "What is C. elegans?"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True
    )

sampler = make_sampler(temp=0.1, top_k=50)
logits_processors = make_logits_processors(repetition_penalty=1.05)

response = generate(
    model, tokenizer, prompt=prompt, max_tokens=4096,
    sampler=sampler, logits_processors=logits_processors,
    verbose=True,
)

5-bit, 6-bit, and 8-bit builds are also available: MLX-5bit, MLX-6bit, MLX-8bit.

Reasoning

Unlike LFM2-24B-A2B, LFM2.5-24B-A2B always thinks before it answers. The chat template opens every assistant turn with a <think> tag, so responses contain an explicit chain of thought before the final answer. Leave enough room in your max token budget for the reasoning trace. See Chat Template and Tool Use for the message format.

Models

Fine-tuning

Edge Inference

GPU Inference

Cloud Inference

Guides

Help

Specifications

Local Agents

Reasoning

131K Context

Quick Start

Reasoning

​Specifications

Local Agents

Reasoning

131K Context

​Quick Start

​Reasoning

Specifications

Quick Start

Reasoning