ngocbh/TrimKV-Qwen3-4B-Instruct-2507

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.

Why TRIM-KV?

It's fast

It's smart

And it's interpretable

---

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

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.

Released Models

Base Model	TRIM-KV Checkpoints	Training Datasets	Max Context Len	Training $M$
Qwen3-1.7B	TRIM-KV-Qwen3-1.7B-Math	OpenR1-Math-220k	16K	512
Qwen3-4B	TRIM-KV-Qwen3-4B-Math	OpenR1-Math-220k	16K	512
Qwen3-8B	TRIM-KV-Qwen3-8B-Math	OpenR1-Math-220k	16K	512
Qwen3-14B	TRIM-KV-Qwen3-14B-Math	OpenR1-Math-220k	16K	512
Qwen3-4B-Instruct-2507	TrimKV-Qwen3-4B-Instruct-2507	Synth-Long, BookSum, Buddhi	128K	4096
Phi-3-mini-128k-instruct	TrimKV-Phi-3-mini-128k-instruct	LongAlpaca	128K	2048

---