LiquidAI/LFM2.5-24B-A2B-MLX-5bit
This model LiquidAI/LFM2.5-24B-A2B-MLX-5bit was converted to MLX format from LiquidAI/LFM2.5-24B-A2B using mlx-lm version 0.32.0.
About this quantization
Quantized to 5 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-5bit")
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}
}