Update model card: add pipeline tag, paper and code links

README.md

@@ -1,9 +1,10 @@
 ---
-license: apache-2.0
-datasets:
-- open-r1/OpenR1-Math-220k
 base_model:
 - Qwen/Qwen3-8B
+datasets:
+- open-r1/OpenR1-Math-220k
+license: apache-2.0
+pipeline_tag: text-generation
 tags:
 - math
 - trimkv
@@ -12,61 +13,42 @@ tags:
 - Compression
 ---
 
-> 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.
+# TrimKV-Qwen3-8B-Math
 
-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. 
+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 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.
+This model is a Qwen3-8B checkpoint trained with TRIM-KV on math reasoning tasks, as presented in the paper [Make Each Token Count: Towards Improving Long-Context Performance with KV Cache Eviction](https://huggingface.co/papers/2605.09649).
 
+- **Paper:** [https://huggingface.co/papers/2512.03324](https://huggingface.co/papers/2512.03324)
+- **GitHub:** [https://github.com/ngocbh/trimkv](https://github.com/ngocbh/trimkv)
 
 <a href="https://arxiv.org/pdf/2512.03324"><img src="https://img.shields.io/badge/arxiv-2512.03324-red?style=for-the-badge"></a>
 
+### About TRIM-KV
 
-### Why TRIM-KV?
-
-It's fast
-
-<div align="center">
-    <img width="1000" alt="teaser" src="https://github.com/ngocbh/trimkv/blob/main/assets/speed.png?raw=true"/>
-</div>
-
-It's smart
+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. 
 
-<div align="center">
-    <img width="1000" alt="teaser" src="https://github.com/ngocbh/trimkv/blob/main/assets/performance.png?raw=true"/>
-</div>
+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?
 
-And it's interpretable
+It's fast, smart, and interpretable.
 
 <div align="center">
-    <img width="1000" alt="teaser" src="https://github.com/ngocbh/trimkv/blob/main/assets/eviction.png?raw=true"/>
+    <img width="1000" alt="speed" src="https://github.com/ngocbh/trimkv/blob/main/assets/speed.png?raw=true"/>
 </div>
 
 <div align="center">
-    <img width="1000" alt="teaser" src="https://github.com/ngocbh/trimkv/blob/main/assets/vis.png?raw=true"/>
+    <img width="1000" alt="performance" src="https://github.com/ngocbh/trimkv/blob/main/assets/performance.png?raw=true"/>
 </div>
 
 ---
 
-## 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
-
-```sh
-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`](examples/env.yaml).
+## Quick Start
 
 ### Installation
 
-From the root of the repo:
+To use this model, you need to install the `trimkv` library:
 
 ```sh
 git clone https://github.com/ngocbh/trimkv.git
@@ -74,9 +56,7 @@ cd trimkv
 pip install -e .
 ````
 
----
-
-## Quick Start
+### Inference
 
 ```python
 import torch
@@ -84,14 +64,13 @@ 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_path = "ngocbh/TrimKV-Qwen3-8B-Math"
 
 model = TrimKVQwen3ForCausalLM.from_pretrained(
     model_path,
     torch_dtype=torch.bfloat16,
     load_trimkv_weights=True,
-    download_from=download_from,
+    download_from="huggingface",
     use_cache=True,
     device_map="cuda",
 )
@@ -112,18 +91,13 @@ tokenizer = AutoTokenizer.from_pretrained(
 # 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`](examples/test_qwen3.py).
-
-## Released Models
-
-| Base Model                    | TRIM-KV Checkpoints                           | Training Datasets        | Training Context Len | Training $M$ |
-|------------------------------|-----------------------------------------------|--------------------------|-------------------------|--------------|
-| Qwen3-1.7B                     | [TRIM-KV-Qwen3-1.7B-Math](https://huggingface.co/ngocbh/TrimKV-Qwen3-1.7B-Math)            | OpenR1-Math-220k          | 16K   | 256     |
-| Qwen3-4B                     | [TRIM-KV-Qwen3-4B-Math](https://huggingface.co/ngocbh/TrimKV-Qwen3-4B-Math)            | OpenR1-Math-220k          | 16K   | 256     |
-| Qwen3-8B                     | [TRIM-KV-Qwen3-8B-Math](https://huggingface.co/ngocbh/TrimKV-Qwen3-8B-Math)            |  OpenR1-Math-220k          | 16K   | 256     |
-| Qwen3-14B                    | [TRIM-KV-Qwen3-14B-Math](https://huggingface.co/ngocbh/TrimKV-Qwen3-14B-Math)           |  OpenR1-Math-220k         | 16K   | 256     |
-| Qwen3-4B-Instruct-2507 | [TrimKV-Qwen3-4B-Instruct-2507](https://huggingface.co/ngocbh/TrimKV-Qwen3-4B-Instruct-2507) | Synth-Long, BookSum, Buddhi      |  128K      | 1024     |
-| Phi-3-mini-128k-instruct | [TrimKV-Phi-3-mini-128k-instruct](https://huggingface.co/ngocbh/TrimKV-Phi-3-mini-128k-instruct) | LongAlpaca          |  128K  | 512 |
-| DeepSeek-R1-Distill-Llama-8B                    | [TrimKV-DeepSeek-R1-Distill-Llama-8B](https://huggingface.co/ngocbh/TrimKV-DeepSeek-R1-Distill-Llama-8B)           |  OpenR1-Math-220k         | 32K   | 256     |
+## Citation
 
----
+```bibtex
+@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}
+}
+```