metadata
license: other
license_name: lfm-open-license-v1.0
license_link: https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M/blob/main/LICENSE
library_name: minima-lfm
base_model: LiquidAI/LFM2.5-Encoder-350M
tags:
- lfm2
- encoder
- ternary
- bitnet
- quantization
Minima
A strict W1.58A8 adaptation of LiquidAI/LFM2.5-Encoder-350M, built with SSHDotCodes/minima.
- Logical matrix values:
{-1, 0, +1}(1.585 bits) - Physical artifact format: I2_S, four trits per byte
- Dynamic int8 activations
- Group size 128, no recovery adapters
- Full 8,192-token encoder context
- Packed weight file: 89.9 MB (94,298,568 bytes)
- Release status: release candidate (six-task gated retention 85.45% vs FP32)
Use
pip install "minima-lfm @ git+https://github.com/SSHDotCodes/minima.git"
from minima import MinimaModel
model = MinimaModel.from_pretrained("ProCreations/minima", device="cpu")
outputs = model(input_ids=input_ids, attention_mask=attention_mask)
CPU inference defaults to a one-time FBGEMM dynamic-int8 packing of each
ternary matrix. Set MINIMA_CPU_BACKEND=i2s for the direct packed 2-bit
AVX2/ARM NEON kernel.
Encoder quality
The six-task downstream gate compares matched 800-step fine-tunes. Packed ternary matrices stay frozen; only the task head and non-matrix parameters adapt. Required relative mean: >= 96%.
| Task | FP32 | Minima | Capped retention |
|---|---|---|---|
| SST2 | 0.79817 | 0.75459 | 94.54% |
| QNLI | 0.60608 | 0.59491 | 98.16% |
| MNLI | 0.40621 | 0.38074 | 93.73% |
| MRPC | 0.81694 | 0.81873 | 100.00% |
| STSB | 0.54812 | 0.40660 | 74.18% |
| COLA | 0.11382 | 0.05927 | 52.07% |
| Mean | 85.45% |
License
The weights remain subject to the LFM Open License v1.0 shipped in this repository. The Minima runtime code is MIT licensed.