Built with mlx-optiq, the MLX-native toolkit to quantize, fine-tune, and serve LLMs locally on Apple Silicon, no PyTorch and no cloud. All OptiQ quants · Docs · LFM2.5 family
An OptiQ mixed-precision quant of LiquidAI/LFM2.5-8B-A1B, the sparse mixture-of-experts member of the LFM2.5 family. 5.46 GB on disk, down from 15.8 GB at bf16, with a 128k context.
LFM2.5-8B-A1B pairs the family's hybrid backbone — convolutional blocks interleaved with full attention, so only a few blocks carry a KV cache — with 32 routed experts and 4 active per token. OptiQ measures each layer's sensitivity and assigns per-layer bit-widths across both.
The routed experts carry most of the parameters and mostly tolerate 4-bit: 60 of the 66 fused expert projections land there, with 6 kept at 8-bit alongside attention, the router and the layer edges.
Grade-school maths is the strongest result here and the best in the family. Coding, world knowledge and long-context multi-hop retrieval are the weak spots.
That gives you an OpenAI and Anthropic compatible endpoint with mixed-precision KV cache, tool-call healing and prompt caching.
Liquid's recommended sampling for LFM2.5 is temperature 0.1, top_k 50, repetition_penalty 1.1. Those ship in generation_config.json in this repo and optiq serve applies them automatically, so you get the publisher's settings without passing any flags.
Or from Python:
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/LFM2.5-8B-A1B-OptiQ-4bit")
prompt = tokenizer.apply_chat_template(
[{"role": "user", "content": "Extract every date from this email."}],
add_generation_prompt=True, tokenize=False,
)
print(generate(model, tokenizer, prompt=prompt, max_tokens=512))
# custom terms: read the license before you download$ fw get mlx-community/LFM2.5-8B-A1B-OptiQ-4bit
# manifest sha256 fbb819743186… · 5.47 GB · 12 files$ fw verify ./LFM2.5-8B-A1B-OptiQ-4bit mlx-community/LFM2.5-8B-A1B-OptiQ-4bit
Terms that come with these files. LFM 1.0: commercial use only for organisations under US$10M annual revenue. Keep a copy of the license with the files.
Custom license (custom).Read the license text first. Open this panel to show the commands once you accept its terms.
fw.pyChecks the signature and every sha256, tries each source in order, resumes.
import os
os.environ["HF_ENDPOINT"] = "https://freeweights.allternit.com"
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("mlx-community/LFM2.5-8B-A1B-OptiQ-4bit", revision="39bb7d49cc92118db10e30e9421c7f8f1f23bfe8")
model = AutoModelForCausalLM.from_pretrained("mlx-community/LFM2.5-8B-A1B-OptiQ-4bit", revision="39bb7d49cc92118db10e30e9421c7f8f1f23bfe8", device_map="auto")
mlx-community <huggingface.co/mlx-community>Newsgroups: alt.freeweights.lfmSubject: 
