Llama-3.2-1B-DMS-8x

Description

Llama-3.2-1B-DMS-8x is a derivative of meta-llama/Llama-3.2-1B that integrates Dynamic Memory Sparsification (DMS) for KV-cache compression research and serving experiments.

DMS learns per-KV-head token eviction decisions that interpolate between a recent-token sliding window and full attention. The checkpoint stores those decisions in the borrowed-neuron convention used by the DMS paper and can be served by DMS-aware runtimes to reduce KV-cache memory at inference time.

This checkpoint is part of Shisa AI's FastDMS work. Standard Transformers can load the model as a normal LlamaForCausalLM, but compact KV-cache behavior requires a runtime that consumes the packaged DMS metadata and eviction signal.

This model is for research and development.

DMS Metadata

The runtime parameters are included both in config.json and in dms_metadata.json:

Parameter	Value
DMS window	256 tokens
DMS alpha scale	100.0
DMS alpha offset	5.0
Target CR	8x
Base model	meta-llama/Llama-3.2-1B

The full retained training log is included as training_log.json.

Training

The checkpoint was trained with the two-phase DMS procedure:

1. Borrowed-neuron zeroing for 2000 steps.
2. DMS retrofitting with logit distillation and compression loss through CR2 to CR8 checkpoints.

Training used WikiText-2 text chunks, context length 4096, DMS window 256, learning rate 3e-5, and compression weight 100.

The retained run took 1219.7s (20.3 minutes) of DMS training wall time on a single RTX PRO 6000 Blackwell GPU.

Evaluation

On the local WikiText-2 512 x 2 strict-mask gate, the final checkpoint measured:

Metric	Value
Strict-mask PPL	7.2171
PPL delta vs base	-1.31%
KLD vs base	0.021957 nats/token
Eviction rate	0.5767
Effective compression	2.36x

In Shisa AI's compact-DMS serving experiments, this checkpoint is the canonical Llama-3.2-1B corrected-mask v5 source used for the FastDMS/nano-vLLM serving rows.

Quick Start

Dense Transformers loading:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "shisa-ai/Llama-3.2-1B-DMS-8x"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

DMS-aware serving:

See FastDMS for the runtime path that uses dms_metadata.json and the checkpoint's learned eviction signal for compact KV-cache inference.

License and Terms

This checkpoint is a derivative of Meta Llama 3.2 and is released under the Llama 3.2 license terms inherited from the base model.

References

• Llama-3.2-1B base model
• FastDMS implementation
• Inference-Time Hyper-Scaling with KV Cache Compression