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-VL-2B, Liquid's LFM3 vision-language model. It reads images and text and answers in text. It is 1.7 GB on disk, down from 3.9 GB at bf16, with a 128k context.
The language model is the LFM3 backbone: short convolutions and attention, with 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. The vision tower stays at bf16 in a sidecar, optiq_vision.safetensors.
lfm3_vl: LFM3 language model (24 layers: 13 convolution, 5 sliding-window attention of 256 tokens, 6 full attention) plus a vision tower
Method
OptiQ mixed-precision, sensitivity-driven (bf16 reference); vision tower at bf16
On disk
1.7 GB (bf16: 3.9 GB)
Context
128k
About half of the language model's weight matrices stay at 8-bit (75 of 153), the ones whose quantization moved the output most. The rest are at 4-bit.
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)
68.9%
70.6%
GSM8K
83.2%
76.4%
IFEval (strict)
44.4%
35.7%
BFCL-V3 simple (200 calls)
91.0%
90.5%
HumanEval (164 problems, pass@1)
55.5%
43.9%
HashHop (long-context retrieval)
3.0%
0.0%
Capability Score (mean of 6)
57.67
52.85
The OptiQ quant is ahead by 4.8 points overall, with the largest gains on code (+11.6), instruction following (+8.7) and math (+6.8).
That gives you an OpenAI and Anthropic compatible endpoint that takes images as well as text, with mixed-precision KV cache (kv_config.json in this repo), tool calling, and prompt caching. Send an image as an image_url content part, as with any OpenAI vision request.
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.
# custom terms: read the license before you download$ fw get mlx-community/LFM3-VL-2B-OptiQ-4bit
# manifest sha256 5ced23897e95… · 1.71 GB · 22 files$ fw verify ./LFM3-VL-2B-OptiQ-4bit mlx-community/LFM3-VL-2B-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.
mlx-community <huggingface.co/mlx-community>Newsgroups: alt.freeweights.lfmSubject: 
