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LiquidAI/LFM2.5-24B-A2B-MLX-6bit

This model LiquidAI/LFM2.5-24B-A2B-MLX-6bit was converted to MLX format from LiquidAI/LFM2.5-24B-A2B using mlx-lm version 0.32.0.

About this quantization

Quantized to 6 bits per weight (group size 64, affine mode). 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-6bit")

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}
}