Instructions to use OpenOneRec/KSA-V2-4B-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenOneRec/KSA-V2-4B-base with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="OpenOneRec/KSA-V2-4B-base", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OpenOneRec/KSA-V2-4B-base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("OpenOneRec/KSA-V2-4B-base", trust_remote_code=True, device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OpenOneRec/KSA-V2-4B-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenOneRec/KSA-V2-4B-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenOneRec/KSA-V2-4B-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OpenOneRec/KSA-V2-4B-base

SGLang

How to use OpenOneRec/KSA-V2-4B-base with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "OpenOneRec/KSA-V2-4B-base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenOneRec/KSA-V2-4B-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "OpenOneRec/KSA-V2-4B-base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenOneRec/KSA-V2-4B-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OpenOneRec/KSA-V2-4B-base with Docker Model Runner:
```
docker model run hf.co/OpenOneRec/KSA-V2-4B-base
```

KSA-V2-4B-base / README.md

OpenOneRec

Release KSA-V2-4B-base weights and inference runtime

a1111f3 verified 2 days ago

preview code

Raw History Blame Contribute Delete

9.92 kB

---
license: apache-2.0
language:
- en
- zh
library_name: transformers
pipeline_tag: text-generation
tags:
- ksa
- ksa-v2
- custom_code
- long-context
---


<div align="center">
  <h1>KSA V2 4B</h1>
  <p><strong>Summary tokens as long-range memory and sparse indexes</strong></p>
  <p>
    <a href="https://arxiv.org/abs/2610.18654"><img alt="KSA V2 Paper" src="https://img.shields.io/badge/KSA_V2-arXiv%3A2610.18654-b31b1b?logo=arxiv" /></a>
    <a href="https://arxiv.org/abs/2604.24432"><img alt="KSA Paper" src="https://img.shields.io/badge/KSA-arXiv%3A2604.24432-b31b1b?logo=arxiv" /></a>
    <a href="https://github.com/Kuaishou-OneRec/KSA"><img alt="GitHub" src="https://img.shields.io/badge/GitHub-Kuaishou--OneRec-black?logo=github" /></a>
    <a href="LICENSE"><img alt="License" src="https://img.shields.io/badge/License-Apache%202.0-green" /></a>
  </p>
  <p><a href="README.md">English</a> | <a href="README_zh.md">中文</a></p>
</div>
## 📖 Introduction

**Kwai Summary Attention (KSA)** is our attention architecture for efficient
long-context modeling. Learnable Summary tokens at fixed chunk boundaries
preserve a compressed representation of distant history.

**KSA V2** gives these Summaries two complementary roles:

- **Compressed KSA (C-KSA)** aggregates Summary KV into compact Meta KV for
  more efficient long-range memory.
- **Sparse KSA (S-KSA)** uses Summaries to build block-level routing keys.
  A lightweight Indexer selects relevant blocks, whose original KV is read
  directly by the main attention.

We retain Hybrid KSA's 3:1 layer layout, replacing KSA layers with C-KSA and
Full layers with S-KSA.

<p align="center"><img src="assets/figures/ksa_v2_overview.png" width="100%" alt="From Hybrid KSA to KSA V2: the same Summary layout, with C-KSA compression and S-KSA block routing." /></p>
<p align="center"><em>From Hybrid KSA to KSA V2: shared Summaries, layer-local KV compression and sparse original-token access.</em></p>

This base model builds on our **4B Hybrid KSA backbone continually pretrained from Qwen3-4B**. It is not an instruction-tuned model.

## 🔥 News

- **2026-10-13** — [KSA V2 technical report](https://arxiv.org/abs/2610.18654) released.
- **2026-05-08** — [KSA-4B-base](https://huggingface.co/OpenOneRec/KSA-4B-base) weights released.
- **2026-04-28** — [KSA technical report](https://arxiv.org/abs/2604.24432) and training code released.

## ✨ Method

**C-KSA: Summary → Meta KV.** Learnable dimension-wise pooling compresses groups
of Summary KV within each attention layer. Summary tokens continue through
the backbone at their original chunk granularity; stronger KV compression does
not require a new large-chunk backbone. Each query attends to remote Meta,
boundary Summary and local text KV. Meta entries remain local to the layer
that constructs them; they are not propagated as new tokens.

<p align="center"><img src="assets/figures/ksa_v2_compression.png" width="80%" alt="C-KSA compresses complete remote Summary groups into Meta KV while retaining boundary Summaries and local text KV." /></p>
<p align="center"><em>C-KSA hierarchical compression, illustrated with chunk size 4 and two Summaries per Meta entry.</em></p>

**S-KSA: Summary → block index.** A Summary query aggregates each text block
into a representation for low-dimensional routing. Main attention reads the
selected blocks' original KV together with sinks and local context. A
blockwise-max dense-attention teacher supervises the Indexer.

<p align="center"><img src="assets/figures/ksa_v2_sparse.png" width="80%" alt="S-KSA uses Summary-guided block aggregation and an Indexer to select remote text blocks, then attends to their original KV together with sinks and local context." /></p>
<p align="center"><em>S-KSA block routing, illustrated with chunk size 4 and block size 2. Released model configurations are listed below.</em></p>

**Three adaptation stages.** Compression-module warmup → full-parameter C-KSA
CPT → S-KSA aggregation and Indexer warmup. The final stage freezes inherited
parameters; its checkpoint is exported directly for sparse inference.

## 🤖 Model configuration

This directory contains **KSA-v2-4B**.

| Configuration | KSA-v2-1.9B | KSA-v2-4B |
| :--- | :--- | :--- |
| Number of layers | 24 | 36 |
| Hidden size | 2048 | 2560 |
| Intermediate size | 6144 | 9728 |
| Attention heads (Q / KV) | 16 / 16 (MHA) | 32 / 8 (GQA) |
| Head dimension | 128 | 128 |
| Hybrid ratio (C-KSA : S-KSA) | 3 : 1 | 3 : 1 |
| Maximum context (text tokens) | 131072 | 131072 |
| Summary chunk size | 8 | 8 |
| C-KSA sliding chunk number | 128 | 128 |
| C-KSA group size | 128 | 32 |
| S-KSA block size | 4 | 4 |
| S-KSA remote token budget | 4096 | 4096 |
| Sink tokens | 8 | 8 |
| S-KSA local window (text tokens) | 1024 | 1024 |
| Indexer heads × head dimension | 8 × 128 | 8 × 128 |
| Indexer query rank | 512 | 512 |
| Indexer RoPE dimension | 64 | 64 |
| Indexer Q/K RMSNorm | False | True |
| Teacher pooling | max | max |
| Tied embeddings | False | True |

The remote budget includes eight sink tokens; the local window is additional.

Weights are stored in FP32 and loaded in BF16.

## 📈 Results

<p align="center"><img src="assets/figures/ksa_v2_results.png" width="85%" alt="The technical report's first-page 1.9B results: long-context retrieval and general capabilities, and theoretical per-token inference FLOPs and KV cache size." /></p>
<p align="center"><em>1.9B results: long-context and general capabilities (left), theoretical per-token inference FLOPs and KV cache size (right).</em></p>

We evaluate KSA V2 on two Hybrid KSA backbones: a 1.9B model trained from
scratch and a 4B model continually pretrained from Qwen3-4B. Both undergo
three-stage adaptation; original baseline results are reused from KSA V1.

### 1.9B: Long-context retrieval

C-KSA uses group128 and S-KSA uses block4. Hybrid KSA is the parent model before adaptation.

| Model | 4K | 8K | 16K | 32K | 64K | 128K | Mean |
| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
| Full | 76.08 | 72.85 | 73.24 | 69.06 | 65.32 | 48.75 | 67.55 |
| Hybrid SWA | 74.54 | 71.69 | 69.54 | 67.86 | 63.03 | 56.64 | 67.22 |
| Hybrid SCA | 77.72 | 75.22 | 72.55 | 67.74 | 63.54 | 58.01 | 69.13 |
| Hybrid GDN | 79.83 | 76.01 | 74.04 | 70.41 | 69.39 | 59.87 | 71.59 |
| Pure KSA | 70.44 | 65.91 | 66.74 | 62.54 | 57.13 | 39.29 | 60.34 |
| Hybrid KSA | 80.65 | 73.35 | 74.07 | 72.30 | 69.95 | 65.35 | 72.61 |
| C-KSA + Full | 81.52 | 72.24 | 74.44 | 71.93 | 69.61 | 66.31 | 72.68 |
| **C-KSA + S-KSA** | 81.48 | 72.42 | 75.67 | 70.78 | 68.99 | 65.01 | 72.39 |

### 4B: Scaling to a GQA backbone

C-KSA uses group32 and S-KSA uses block4, preserving the backbone's GQA structure.

| Model | 4K | 8K | 16K | 32K | 64K | 128K | Mean |
| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
| Full | 92.88 | 91.38 | 89.12 | 84.74 | 78.16 | 65.86 | 83.69 |
| Hybrid SWA | 91.30 | 88.03 | 82.87 | 78.94 | 73.88 | 66.27 | 80.22 |
| Hybrid SCA | 86.02 | 84.28 | 80.67 | 76.89 | 68.88 | 60.94 | 76.28 |
| Hybrid Linear | 86.39 | 83.86 | 78.06 | 76.48 | 73.50 | 67.98 | 77.71 |
| Pure KSA | 91.55 | 86.78 | 84.78 | 80.30 | 76.09 | 66.81 | 81.05 |
| Hybrid KSA | 92.97 | 90.53 | 88.86 | 86.65 | 76.04 | 71.67 | 84.45 |
| Hybrid KSA + CPT | 92.22 | 89.85 | 87.20 | 83.09 | 74.74 | 72.03 | 83.19 |
| C-KSA + Full | 92.77 | 88.12 | 86.27 | 82.68 | 78.58 | 73.92 | 83.72 |
| **C-KSA + S-KSA** | 93.38 | 87.78 | 85.45 | 83.60 | 80.28 | 74.41 | 84.15 |

Hybrid KSA + CPT is a continuation control with no architectural changes.

See the technical report for further ablations and inference efficiency.

## Quick start

Linux, Python 3.12 and H100/H800. Download the **entire model directory**,
including `kernel/`, and run from it:

```bash
python -m pip install torch==2.6.0 --index-url https://download.pytorch.org/whl/cu126
python -m pip install -r requirements.txt
CUDA_VISIBLE_DEVICES=0 python generate.py \
    --prompt "The main advantage of long-context language models is" \
    --max-new-tokens 128
```

Omit `--prompt` for interactive completion. The optional `--apply-chat-template`
only applies the tokenizer template; it does not add instruction tuning.

Transformers 5.0.0 and the bundled `ksa_attn` / FlashAttention CuTe sources are
used. No training checkout, kernel build or FlashAttention 2 CUDA extension
is required.

### Transformers usage

Set the bundled kernel paths before importing Transformers:

```bash
export PYTHONPATH="$PWD/kernel:$PWD/kernel/deps${PYTHONPATH:+:$PYTHONPATH}"
```

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(".", local_files_only=True)
model = AutoModelForCausalLM.from_pretrained(
    ".",
    trust_remote_code=True,
    local_files_only=True,
    dtype=torch.bfloat16,
    device_map={"": "cuda:0"},
    attn_implementation="sdpa",
).eval()
inputs = tokenizer("The main advantage of long-context models is", return_tensors="pt")
inputs = inputs.to(model.device)
with torch.inference_mode():
    output = model.generate(
        **inputs,
        max_new_tokens=128,
        do_sample=False,
        pad_token_id=tokenizer.eos_token_id,
    )
print(tokenizer.decode(output[0, inputs.input_ids.shape[1]:], skip_special_tokens=True))
```

## License and citation

Weights and our runtime: [Apache-2.0](LICENSE). Qwen-derived files retain their
copyright notices. Bundled FlashAttention CuTe retains its
[BSD-3-Clause license](kernel/deps/flash_attn/LICENSE) and
[AUTHORS](kernel/deps/flash_attn/AUTHORS); its initializer exposes CuTe without
the separate FlashAttention 2 extension.

```bibtex
@techreport{kwai2026ksav2,
  title       = {Kwai Summary Attention V2 Technical Report},
  author      = {OneRec Team},
  year        = {2026},
  institution = {Kuaishou Technology},
  url         = {https://arxiv.org/abs/2610.18654}
}
```

	---
	license: apache-2.0
	language:
	- en
	- zh
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- ksa
	- ksa-v2
	- custom_code
	- long-context
	---



	<div align="center">
	<h1>KSA V2 4B</h1>
	<p><strong>Summary tokens as long-range memory and sparse indexes</strong></p>
	<p>
	<a href="https://arxiv.org/abs/2610.18654"><img alt="KSA V2 Paper" src="https://img.shields.io/badge/KSA_V2-arXiv%3A2610.18654-b31b1b?logo=arxiv" /></a>
	<a href="https://arxiv.org/abs/2604.24432"><img alt="KSA Paper" src="https://img.shields.io/badge/KSA-arXiv%3A2604.24432-b31b1b?logo=arxiv" /></a>
	<a href="https://github.com/Kuaishou-OneRec/KSA"><img alt="GitHub" src="https://img.shields.io/badge/GitHub-Kuaishou--OneRec-black?logo=github" /></a>
	<a href="LICENSE"><img alt="License" src="https://img.shields.io/badge/License-Apache%202.0-green" /></a>
	</p>
	<p><a href="README.md">English</a> \| <a href="README_zh.md">中文</a></p>
	</div>

	## 📖 Introduction

	Kwai Summary Attention (KSA) is our attention architecture for efficient
	long-context modeling. Learnable Summary tokens at fixed chunk boundaries
	preserve a compressed representation of distant history.

	KSA V2 gives these Summaries two complementary roles:

	- Compressed KSA (C-KSA) aggregates Summary KV into compact Meta KV for
	more efficient long-range memory.
	- Sparse KSA (S-KSA) uses Summaries to build block-level routing keys.
	A lightweight Indexer selects relevant blocks, whose original KV is read
	directly by the main attention.

	We retain Hybrid KSA's 3:1 layer layout, replacing KSA layers with C-KSA and
	Full layers with S-KSA.

	<p align="center"><img src="assets/figures/ksa_v2_overview.png" width="100%" alt="From Hybrid KSA to KSA V2: the same Summary layout, with C-KSA compression and S-KSA block routing." /></p>
	<p align="center"><em>From Hybrid KSA to KSA V2: shared Summaries, layer-local KV compression and sparse original-token access.</em></p>

	This base model builds on our 4B Hybrid KSA backbone continually pretrained from Qwen3-4B. It is not an instruction-tuned model.

	## 🔥 News

	- 2026-10-13 — [KSA V2 technical report](https://arxiv.org/abs/2610.18654) released.
	- 2026-05-08 — [KSA-4B-base](https://huggingface.co/OpenOneRec/KSA-4B-base) weights released.
	- 2026-04-28 — [KSA technical report](https://arxiv.org/abs/2604.24432) and training code released.

	## ✨ Method

	C-KSA: Summary → Meta KV. Learnable dimension-wise pooling compresses groups
	of Summary KV within each attention layer. Summary tokens continue through
	the backbone at their original chunk granularity; stronger KV compression does
	not require a new large-chunk backbone. Each query attends to remote Meta,
	boundary Summary and local text KV. Meta entries remain local to the layer
	that constructs them; they are not propagated as new tokens.

	<p align="center"><img src="assets/figures/ksa_v2_compression.png" width="80%" alt="C-KSA compresses complete remote Summary groups into Meta KV while retaining boundary Summaries and local text KV." /></p>
	<p align="center"><em>C-KSA hierarchical compression, illustrated with chunk size 4 and two Summaries per Meta entry.</em></p>

	S-KSA: Summary → block index. A Summary query aggregates each text block
	into a representation for low-dimensional routing. Main attention reads the
	selected blocks' original KV together with sinks and local context. A
	blockwise-max dense-attention teacher supervises the Indexer.

	<p align="center"><img src="assets/figures/ksa_v2_sparse.png" width="80%" alt="S-KSA uses Summary-guided block aggregation and an Indexer to select remote text blocks, then attends to their original KV together with sinks and local context." /></p>
	<p align="center"><em>S-KSA block routing, illustrated with chunk size 4 and block size 2. Released model configurations are listed below.</em></p>

	Three adaptation stages. Compression-module warmup → full-parameter C-KSA
	CPT → S-KSA aggregation and Indexer warmup. The final stage freezes inherited
	parameters; its checkpoint is exported directly for sparse inference.

	## 🤖 Model configuration

	This directory contains KSA-v2-4B.

	\| Configuration \| KSA-v2-1.9B \| KSA-v2-4B \|
	\| :--- \| :--- \| :--- \|
	\| Number of layers \| 24 \| 36 \|
	\| Hidden size \| 2048 \| 2560 \|
	\| Intermediate size \| 6144 \| 9728 \|
	\| Attention heads (Q / KV) \| 16 / 16 (MHA) \| 32 / 8 (GQA) \|
	\| Head dimension \| 128 \| 128 \|
	\| Hybrid ratio (C-KSA : S-KSA) \| 3 : 1 \| 3 : 1 \|
	\| Maximum context (text tokens) \| 131072 \| 131072 \|
	\| Summary chunk size \| 8 \| 8 \|
	\| C-KSA sliding chunk number \| 128 \| 128 \|
	\| C-KSA group size \| 128 \| 32 \|
	\| S-KSA block size \| 4 \| 4 \|
	\| S-KSA remote token budget \| 4096 \| 4096 \|
	\| Sink tokens \| 8 \| 8 \|
	\| S-KSA local window (text tokens) \| 1024 \| 1024 \|
	\| Indexer heads × head dimension \| 8 × 128 \| 8 × 128 \|
	\| Indexer query rank \| 512 \| 512 \|
	\| Indexer RoPE dimension \| 64 \| 64 \|
	\| Indexer Q/K RMSNorm \| False \| True \|
	\| Teacher pooling \| max \| max \|
	\| Tied embeddings \| False \| True \|

	The remote budget includes eight sink tokens; the local window is additional.

	Weights are stored in FP32 and loaded in BF16.

	## 📈 Results

	<p align="center"><img src="assets/figures/ksa_v2_results.png" width="85%" alt="The technical report's first-page 1.9B results: long-context retrieval and general capabilities, and theoretical per-token inference FLOPs and KV cache size." /></p>
	<p align="center"><em>1.9B results: long-context and general capabilities (left), theoretical per-token inference FLOPs and KV cache size (right).</em></p>

	We evaluate KSA V2 on two Hybrid KSA backbones: a 1.9B model trained from
	scratch and a 4B model continually pretrained from Qwen3-4B. Both undergo
	three-stage adaptation; original baseline results are reused from KSA V1.

	### 1.9B: Long-context retrieval

	C-KSA uses group128 and S-KSA uses block4. Hybrid KSA is the parent model before adaptation.

	\| Model \| 4K \| 8K \| 16K \| 32K \| 64K \| 128K \| Mean \|
	\| --- \| ---: \| ---: \| ---: \| ---: \| ---: \| ---: \| ---: \|
	\| Full \| 76.08 \| 72.85 \| 73.24 \| 69.06 \| 65.32 \| 48.75 \| 67.55 \|
	\| Hybrid SWA \| 74.54 \| 71.69 \| 69.54 \| 67.86 \| 63.03 \| 56.64 \| 67.22 \|
	\| Hybrid SCA \| 77.72 \| 75.22 \| 72.55 \| 67.74 \| 63.54 \| 58.01 \| 69.13 \|
	\| Hybrid GDN \| 79.83 \| 76.01 \| 74.04 \| 70.41 \| 69.39 \| 59.87 \| 71.59 \|
	\| Pure KSA \| 70.44 \| 65.91 \| 66.74 \| 62.54 \| 57.13 \| 39.29 \| 60.34 \|
	\| Hybrid KSA \| 80.65 \| 73.35 \| 74.07 \| 72.30 \| 69.95 \| 65.35 \| 72.61 \|
	\| C-KSA + Full \| 81.52 \| 72.24 \| 74.44 \| 71.93 \| 69.61 \| 66.31 \| 72.68 \|
	\| C-KSA + S-KSA \| 81.48 \| 72.42 \| 75.67 \| 70.78 \| 68.99 \| 65.01 \| 72.39 \|

	### 4B: Scaling to a GQA backbone

	C-KSA uses group32 and S-KSA uses block4, preserving the backbone's GQA structure.

	\| Model \| 4K \| 8K \| 16K \| 32K \| 64K \| 128K \| Mean \|
	\| --- \| ---: \| ---: \| ---: \| ---: \| ---: \| ---: \| ---: \|
	\| Full \| 92.88 \| 91.38 \| 89.12 \| 84.74 \| 78.16 \| 65.86 \| 83.69 \|
	\| Hybrid SWA \| 91.30 \| 88.03 \| 82.87 \| 78.94 \| 73.88 \| 66.27 \| 80.22 \|
	\| Hybrid SCA \| 86.02 \| 84.28 \| 80.67 \| 76.89 \| 68.88 \| 60.94 \| 76.28 \|
	\| Hybrid Linear \| 86.39 \| 83.86 \| 78.06 \| 76.48 \| 73.50 \| 67.98 \| 77.71 \|
	\| Pure KSA \| 91.55 \| 86.78 \| 84.78 \| 80.30 \| 76.09 \| 66.81 \| 81.05 \|
	\| Hybrid KSA \| 92.97 \| 90.53 \| 88.86 \| 86.65 \| 76.04 \| 71.67 \| 84.45 \|
	\| Hybrid KSA + CPT \| 92.22 \| 89.85 \| 87.20 \| 83.09 \| 74.74 \| 72.03 \| 83.19 \|
	\| C-KSA + Full \| 92.77 \| 88.12 \| 86.27 \| 82.68 \| 78.58 \| 73.92 \| 83.72 \|
	\| C-KSA + S-KSA \| 93.38 \| 87.78 \| 85.45 \| 83.60 \| 80.28 \| 74.41 \| 84.15 \|

	Hybrid KSA + CPT is a continuation control with no architectural changes.

	See the technical report for further ablations and inference efficiency.

	## Quick start

	Linux, Python 3.12 and H100/H800. Download the entire model directory,
	including `kernel/`, and run from it:

	```bash
	python -m pip install torch==2.6.0 --index-url https://download.pytorch.org/whl/cu126
	python -m pip install -r requirements.txt

	CUDA_VISIBLE_DEVICES=0 python generate.py \
	--prompt "The main advantage of long-context language models is" \
	--max-new-tokens 128
	```

	Omit `--prompt` for interactive completion. The optional `--apply-chat-template`
	only applies the tokenizer template; it does not add instruction tuning.

	Transformers 5.0.0 and the bundled `ksa_attn` / FlashAttention CuTe sources are
	used. No training checkout, kernel build or FlashAttention 2 CUDA extension
	is required.

	### Transformers usage

	Set the bundled kernel paths before importing Transformers:

	```bash
	export PYTHONPATH="$PWD/kernel:$PWD/kernel/deps${PYTHONPATH:+:$PYTHONPATH}"
	```

	```python
	import torch
	from transformers import AutoModelForCausalLM, AutoTokenizer

	tokenizer = AutoTokenizer.from_pretrained(".", local_files_only=True)
	model = AutoModelForCausalLM.from_pretrained(
	".",
	trust_remote_code=True,
	local_files_only=True,
	dtype=torch.bfloat16,
	device_map={"": "cuda:0"},
	attn_implementation="sdpa",
	).eval()

	inputs = tokenizer("The main advantage of long-context models is", return_tensors="pt")
	inputs = inputs.to(model.device)
	with torch.inference_mode():
	output = model.generate(
	**inputs,
	max_new_tokens=128,
	do_sample=False,
	pad_token_id=tokenizer.eos_token_id,
	)
	print(tokenizer.decode(output[0, inputs.input_ids.shape[1]:], skip_special_tokens=True))
	```

	## License and citation

	Weights and our runtime: [Apache-2.0](LICENSE). Qwen-derived files retain their
	copyright notices. Bundled FlashAttention CuTe retains its
	[BSD-3-Clause license](kernel/deps/flash_attn/LICENSE) and
	[AUTHORS](kernel/deps/flash_attn/AUTHORS); its initializer exposes CuTe without
	the separate FlashAttention 2 extension.

	```bibtex
	@techreport{kwai2026ksav2,
	title = {Kwai Summary Attention V2 Technical Report},
	author = {OneRec Team},
	year = {2026},
	institution = {Kuaishou Technology},
	url = {https://arxiv.org/abs/2610.18654}
	}
	```