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LiquidAI/LFM2.5-24B-A2B-MLX-4bit

This model LiquidAI/LFM2.5-24B-A2B-MLX-4bit was converted to MLX format from LiquidAI/LFM2.5-24B-A2B using mlx-lm version 0.32.0.

About this quantization

Quantized to 4 bits per weight (group size 64, affine mode). Token embedding (tied to the output head) kept at 6 bits. MoE routing gates kept unquantized in float32.

For base model details, capabilities, and recommended generation settings, see the original model card.

Use with mlx

Requires mlx-lm 0.32.0 or later. Earlier versions load the model but route the experts incorrectly.

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

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,
)

Chat Template

LFM2.5 uses a ChatML-like format. See the Chat Template documentation for details. Example:

<|startoftext|><|im_start|>system
You are a helpful assistant trained by Liquid AI.<|im_end|>
<|im_start|>user
What is C. elegans?<|im_end|>
<|im_start|>assistant
<think>

Because LFM2.5-24B-A2B is a reasoning model, assistant turns contain an explicit chain of thought before the final answer. tokenizer.apply_chat_template() formats messages automatically.

Tool Use

LFM2.5 supports function calling. Pass tools to tokenizer.apply_chat_template(..., tools=...). See the Tool Use documentation for the full guide.

License

LFM Open License v1.0 — see the LICENSE file or the original model card for terms.

Citation

@article{liquidAI202624BA2B,
  author  = {Liquid AI},
  title   = {LFM2.5-24B-A2B: Built for Fast On-Device Agents},
  journal = {Liquid AI Blog},
  year    = {2026},
  note    = {www.liquid.ai/blog/lfm2-5-24b-a2b},
}

@article{liquidai2025lfm2,
  title   = {LFM2 Technical Report},
  author  = {Liquid AI},
  journal = {arXiv preprint arXiv:2511.23404},
  year    = {2025}
}