Family guide · LFM2.5

LFM2.5 on Apple Silicon

LFM2.5 is Liquid AI's on-device series, from 230M up to an 8B sparse MoE. It is a hybrid: short convolutional blocks alternate with full-attention blocks, and only the attention blocks carry a KV cache. On the 1.2B that means six layers hold cache out of the whole stack, so memory grows slowly as context does. That is the property that makes the family interesting on a laptop or a phone, and it is why the bundled KV config is short.

The quants

Every row is a six-metric Capability Score, not a perplexity number. Sizes are the artifact on disk.

Model	bf16 size	OptiQ size	Compression	Capability Score
LFM2.5-230M-OptiQ-4bit	443 MB	180 MB	2.5×	24.83
LFM2.5-350M-OptiQ-4bit	681 MB	269 MB	2.5×	26.60
LFM2.5-1.2B-Instruct-OptiQ-4bit	2.34 GB	825 MB	2.8×	47.82
LFM2.5-1.2B-Thinking-OptiQ-4bit	2.34 GB	820 MB	2.9×	54.14
LFM2.5-1.2B-JP-202606-OptiQ-4bit	2.34 GB	838 MB	2.8×	47.83
LFM2.5-2.6B-OptiQ-4bit	5.2 GB	1.93 GB	2.7×	35.19

Which one to reach for

The Thinking variant is the one to use if you can spare the tokens. It scores 64.7% on MMLU against the Instruct model's 43.3%, and 82.8% on GSM8K against 69.7%. Reasoning is doing real work at this size, not decorating the answer.

The Japanese variant is the interesting one. Its overall score lands at 47.83 against the Instruct model's 47.82, near enough to call identical, but the capability sits in different places: seven points more on MMLU, nearly ten more on HumanEval, and roughly eight less on both GSM8K and IFEval. Same total, redistributed.

Below a gigabyte the 230M and 350M are useful for classification, routing and extraction rather than open-ended chat. Both score at chance on long-context retrieval.

Hello world

hello.pypython

from mlx_lm import load, generate

model, tok = load("mlx-community/LFM2.5-1.2B-Thinking-OptiQ-4bit")
prompt = tok.apply_chat_template(
    [{"role": "user", "content": "What is 17 * 23? Think briefly then answer."}],
    tokenize=False, add_generation_prompt=True,
)
print(generate(model, tok, prompt=prompt, max_tokens=2048))

Give the Thinking model room It spends its first few hundred tokens inside the reasoning block, and that comes out of max_tokens. Set the cap too low and the entire allowance is consumed before the answer starts, so you get an empty string back rather than a short reply. 2048 is a sensible floor.

Tool calling

LFM2.5 does not emit JSON tool calls. It writes Python, between two special tokens:

tool calltext

<|tool_call_start|>[get_weather(city="Paris")]<|tool_call_end|>

Pass tools= to apply_chat_template and the catalog is rendered into the system prompt. OptiQ parses this form in both the evaluator and the agent loop, so a model that calls tools correctly scores as though it does.

Serving

Each quant ships a kv_config.json sized for its handful of attention layers. On the 1.2B that is six layers at roughly four bits.

servebash

optiq serve --model mlx-community/LFM2.5-1.2B-Thinking-OptiQ-4bit \
  --kv-config kv_config.json