teresadelgadoheras commited on
Commit
45633af
路
verified 路
1 Parent(s): e06a529

Add organization card

Browse files
Files changed (1) hide show
  1. README.md +42 -4
README.md CHANGED
@@ -1,10 +1,48 @@
1
  ---
2
  title: README
3
- emoji: 馃寲
4
- colorFrom: purple
5
- colorTo: pink
6
  sdk: static
7
  pinned: false
8
  ---
9
 
10
- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  ---
2
  title: README
3
+ emoji: 馃
4
+ colorFrom: blue
5
+ colorTo: indigo
6
  sdk: static
7
  pinned: false
8
  ---
9
 
10
+ # LowRAM
11
+
12
+ **Why spend memory on what you can predict?**
13
+
14
+ LowRAM compresses the KV cache of long-context LLMs. It reconstructs values from their keys, stores corrections for the hardest-to-predict values, and compresses keys across layers. The base model's weights stay frozen, and lightweight predictors adapt to each prompt at test time. LowRAM can be combined with quantisation and token eviction.
15
+
16
+ 馃搫 [Paper](https://huggingface.co/papers/2611.07025) 路 馃捇 [Code](https://github.com/lowram-model/lowram) 路 馃寪 [Website](https://lowram-model.github.io/)
17
+
18
+ ## Highlights
19
+
20
+ - **Up to 26.9脳 KV-cache compression** while keeping 94.5% of the uncompressed LongBench score (Mistral-7B-Instruct-v0.3, prompts over 10K tokens, with quantisation and eviction)
21
+ - **4脳 value-cache compression** while keeping 99.3% of the uncompressed LongBench score (Llama-3.1-8B-Instruct)
22
+ - **Up to 5脳 higher peak decode throughput** at 262K-token context (Llama-3.1-8B-Instruct)
23
+
24
+ These results come from separate experimental settings; see the paper for details.
25
+
26
+ ## Checkpoints
27
+
28
+ | Base model | Repository |
29
+ | --- | --- |
30
+ | Llama-3.1-8B-Instruct | [lowram-model/lowram-llama-3.1-8b](https://huggingface.co/lowram-model/lowram-llama-3.1-8b) |
31
+ | Mistral-7B-Instruct-v0.3 | [lowram-model/lowram-mistral-7b](https://huggingface.co/lowram-model/lowram-mistral-7b) |
32
+
33
+ Each repository contains the meta-learned value-MLP initialisation, pretrained value MLPs and the attention predictor for that model.
34
+
35
+ ## Citation
36
+
37
+ ```bibtex
38
+ @article{lowram2026,
39
+ title = {{LowRAM}: Low-Rank Keys and Amortised Values for
40
+ Composable {KV} Cache Compression at Test-Time},
41
+ author = {Delgado, Teresa and Hazard, Hugo and
42
+ Benfeghoul, Martin and Wang, Jun and
43
+ Fountas, Zafeirios},
44
+ journal = {arXiv preprint arXiv:2611.07025},
45
+ year = {2026},
46
+ url = {https://arxiv.org/abs/2611.07025}
47
+ }
48
+ ```