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ngocbh/
TrimKV-DeepSeek-R1-Distill-Llama-8B

Name: TrimKV-DeepSeek-R1-Distill-Llama-8B
Availability: InStock

open-r1/OpenR1-Math-220k

2512.03324

deepseek-ai/DeepSeek-R1-Distill-Llama-8B

finetune

apache-2.0

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TRIM-KV is an efficient and learnable key–value eviction strategy designed to improve the efficiency of large language models (LLMs) in long-horizon inference.

The core idea behind TRIM-KV is to learn the intrinsic importance of each key–value pair at creation time, which we call token retention, and then decay this importance exponentially over time to mimic the standard inference running with eviction.

The retention score is query-agnostic and captures the long-term utility of tokens. This is different from attention scores, which are query-dependent: they capture the short-term utility for predicting the next token and are recomputed at every step, making them local, myopic, and highly dependent on the transient decoding state.

Getting Started

Requirements

Python 3.11 or higher (tested with 3.12)
PyTorch 2.7.0 or higher (tested with 2.8.0)
FlashAttention 2.7.2.post1 or higher (tested with 2.8.0)
Transformers 4.57.1

pip install -r requirements.txt

This is a minimal set of requirements for training purposes. Additional dependencies may be needed for running specific experiments. We provided a full example of the environment used in our experiments in examples/env.yaml.

Installation

From the root of the repo:

git clone https://github.com/ngocbh/trimkv.git
cd trimkv
pip install -e .

Quick Start

python

import torch
from trimkv.models.qwen3 import TrimKVQwen3ForCausalLM
from trimkv.cache_utils import TrimKVCache
from transformers import AutoTokenizer

model_path = "<TrimKV model_path here>"
download_from = "huggingface"  # options: "wandb", "local", "huggingface"

model = TrimKVQwen3ForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.bfloat16,
    load_trimkv_weights=True,
    download_from=download_from,
    use_cache=True,
    device_map="cuda",
)

# Configure TRIM-KV settings
model.config._attn_implementation = "flash_attention_2"
model.config.compress_memory = True
model.config.memory_size = 512
model.config.buffer_size = 128

tokenizer = AutoTokenizer.from_pretrained(
    model.config.base_model,
    use_fast=True,
    padding_side="left",
)

# Use model.generate as normal.
# Note: TRIM-KV uses TrimKVCache under the hood. So please pass TrimKVCache to model.generate

For a runnable end-to-end example, see examples/test_qwen3.py.

Citation

bibtex

@article{bui2025cache,
  title={Cache what lasts: Token retention for memory-bounded kv cache in llms},
  author={Bui, Ngoc and Sharma, Shubham and Lamba, Simran and Mishra, Saumitra and Ying, Rex},
  journal={arXiv preprint arXiv:2512.03324},
  year={2025}
}
@article{bui2025make,
  title={Make Each Token Count: Towards Improving Long-Context Performance with KV Cache Eviction},
  author={Bui, Ngoc and Nguyen, Hieu Trung and Cohan, Arman and Ying, Rex},
  journal={arXiv preprint arXiv:2512.03324},
  year={2025}
}

ngocbh/TrimKV-DeepSeek-R1-Distill-Llama-8B