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
An OptiQ mixed-precision quant of LiquidAI/LFM3-1.7B, the first model in Liquid's LFM3 family. It is 1.3 GB on disk, down from 3.5 GB at bf16, with a 256k context.
LFM3 keeps Liquid's hybrid backbone of short convolutions and attention, and adds sliding-window attention, so only 6 of its 24 layers keep a full-length KV cache. OptiQ measures each layer's sensitivity and assigns its bit-width from that.
Six-metric mean, the standard OptiQ eval, run in reasoning mode because the model thinks before it answers by default. The baseline is a uniform 4-bit quant of the same model, scored the same way.
Metric
OptiQ
Uniform 4-bit
MMLU (generative)
77.6%
76.3%
GSM8K
92.4%
92.4%
IFEval (strict)
72.6%
66.5%
BFCL-V3 simple (200 calls)
91.5%
93.0%
HumanEval (164 problems, pass@1)
72.6%
64.0%
HashHop (long-context retrieval)
21.0%
14.0%
Capability Score (mean of 6)
71.29
67.70
The OptiQ quant is ahead by 3.6 points overall. The gains are on instruction following (+6.1) and code (+8.6). BFCL is 1.5 points behind, inside its ±3.5 point margin on 200 calls.
That gives you an OpenAI and Anthropic compatible endpoint with mixed-precision KV cache (kv_config.json in this repo), tool calling, and prompt caching.
The model reasons before it answers, so give it a generous max_tokens. Liquid's recommended sampling (temperature 0.1, top_k 50, repetition_penalty 1.1) ships in generation_config.json, and optiq serve applies it automatically.
Or from Python. mlx-lm does not know the LFM3 architecture yet, so register OptiQ's implementation first:
from optiq.models import install_vendored
from mlx_lm import load, generate
install_vendored()
model, tokenizer = load("mlx-community/LFM3-1.7B-OptiQ-4bit")
prompt = tokenizer.apply_chat_template(
[{"role": "user", "content": "Summarise this contract clause in two sentences."}],
add_generation_prompt=True, tokenize=False,
)
print(generate(model, tokenizer, prompt=prompt, max_tokens=4096))
# custom terms: read the license before you download$ fw get mlx-community/LFM3-1.7B-OptiQ-4bit
# manifest sha256 34c4ae554fea… · 1.36 GB · 15 files$ fw verify ./LFM3-1.7B-OptiQ-4bit mlx-community/LFM3-1.7B-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/LFM3-1.7B-OptiQ-4bit", revision="82d0e45972a9343db2ab5534b257b2d6fb3edd46")
model = AutoModelForCausalLM.from_pretrained("mlx-community/LFM3-1.7B-OptiQ-4bit", revision="82d0e45972a9343db2ab5534b257b2d6fb3edd46", device_map="auto")
mlx-community <huggingface.co/mlx-community>Newsgroups: alt.freeweights.lfmSubject: 
