alt.freeweights.kimi / Kimi-Linear-48B-A3B-Base
From: moonshotai <huggingface.co/moonshotai>Newsgroups: alt.freeweights.kimiSubject:

Kimi-Linear-48B-A3B-Base

Date: 2026-11-14Commit: 3b171c1Signed: 2DC28CC7B26A283B ✓

Text generation model with 49.1B parameters. Safetensors weights, 98.3 GB in all. Published by moonshotai under MIT.

49.1B params98.3GBKimi-Linear-48B-A3B-Basesafetensorspermissive
FactsParams49.1BSize98.3 GBFormatssafetensorsTasktext generationLicenseMITFrom sourcepermissive
$ fw get moonshotai/Kimi-Linear-48B-A3B-Base
# manifest sha256 4a050be6f302… · 98.3 GB · 38 files
$ fw verify ./Kimi-Linear-48B-A3B-Base moonshotai/Kimi-Linear-48B-A3B-Base
From sourceHosted (signed)moonshotai/Kimi-Linear-48B-A3B-BaseLast check: 2026-11-14 06:32 UTC

Get help: custom fine-tune or private deploy. We review your setup before we quote.

Works with

Overview

Size
98.3 GB
Weights
98.2 GB (safetensors)
Parameters
49.1B
Formats
safetensors
Task
text generation
License
MIT (permissive)
Files
38, each with a sha256
Publisher
moonshotai
Added
2026-11-14
Section
alt.freeweights.kimi
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Model card

From the publisher at commit 3b171c1. Shown as plain text with links; images open at the publisher.

Image: image Kimi Linear: An Expressive, Efficient Attention Architecture


  Paper   Code   Model

Image: image (a) On MMLU-Pro (4k context length), Kimi Linear achieves 51.0 performance with similar speed as full attention. On RULER (128k context length), it shows Pareto-optimal performance (84.3) and 3.98x speedup. (b) Kimi Linear achieves 6.3x faster TPOT compared to MLA, offering significant speedups at long sequence lengths (1M tokens).

Overview#

Kimi Linear is a hybrid linear attention architecture that outperforms traditional full attention methods across various contexts, including short, long, and reinforcement learning (RL) scaling regimes. At its core is Kimi Delta Attention (KDA)—a refined version of Gated DeltaNet that introduces a more efficient gating mechanism to optimize the use of finite-state RNN memory.

Kimi Linear achieves superior performance and hardware efficiency, especially for long-context tasks. It reduces the need for large KV caches by up to 75% and boosts decoding throughput by up to $6\times$ for contexts as long as 1M tokens.

We open-source the KDA kernel in FLA, and release two versions model checkpoints trained with 5.7T tokens.

Model#Total Params#Activated ParamsContext LengthDownload Link
Kimi-Linear-Base48B3B1M🤗 Hugging Face
Kimi-Linear-Instruct48B3B1M🤗 Hugging Face

Key Features#

  • Kimi Delta Attention (KDA): A linear attention mechanism that refines the gated delta rule with finegrained gating.
  • Hybrid Architecture: A 3:1 KDA-to-global MLA ratio reduces memory usage while maintaining or surpassing the quality of full attention.
  • Superior Performance: Outperforms full attention in a variety of tasks, including long-context and RL-style benchmarks on 1.4T token training runs with fair comparisons.
  • High Throughput: Achieves up to $6\times$ faster decoding and significantly reduces time per output token (TPOT).

Image: image

Usage#

Inference with Hugging Face Transformers#

To use the Kimi Linear model, we recommend the following environment:

  • python >= 3.10
  • torch >= 2.6
  • fla-core >= 0.4.0
pip install -U fla-core

Example Code:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "moonshotai/Kimi-Linear-48B-A3B-Instruct"
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

messages = [
    {"role": "system", "content": "You are a helpful assistant provided by Moonshot-AI."},
    {"role": "user", "content": "Is 123 a prime?"}
]
input_ids = tokenizer.apply_chat_template(
    messages, 
    add_generation_prompt=True, 
    return_tensors="pt"
).to(model.device)
generated_ids = model.generate(inputs=input_ids, max_new_tokens=500)
response = tokenizer.batch_decode(generated_ids)[0]
print(response)
Deployment#

For deployment, you can use the latest vllm to create an OpenAI-compatible API endpoint.

vllm serve moonshotai/Kimi-Linear-48B-A3B-Instruct \
  --port 8000 \
  --tensor-parallel-size 4 \
  --max-model-len 1048576 \
  --trust-remote-code

Citation#

If you found our work useful, please cite:

@misc{team2025kimi,
    title         = {Kimi Linear: An Expressive, Efficient Attention Architecture},
    author        = {Zhang, Yu  and Lin, Zongyu  and Yao, Xingcheng  and Hu, Jiaxi  and Meng, Fanqing  and Liu, Chengyin  and Men, Xin  and Yang, Songlin  and Li, Zhiyuan  and Li, Wentao  and Lu, Enzhe  and Liu, Weizhou  and Chen, Yanru  and Xu, Weixin  and Yu, Longhui  and Wang, Yejie  and Fan, Yu  and Zhong, Longguang  and Yuan, Enming  and Zhang, Dehao  and Zhang, Yizhi  and T. Liu, Y.  and Wang, Haiming  and Fang, Shengjun  and He, Weiran  and Liu, Shaowei  and Li, Yiwei  and Su, Jianlin  and Qiu, Jiezhong  and Pang, Bo  and Yan, Junjie  and Jiang, Zhejun  and Huang, Weixiao  and Yin, Bohong  and You, Jiacheng  and Wei, Chu  and Wang, Zhengtao  and Hong, Chao  and Chen, Yutian  and Chen, Guanduo  and Wang, Yucheng  and Zheng, Huabin  and Wang, Feng  and Liu, Yibo  and Dong, Mengnan  and Zhang, Zheng  and Pan, Siyuan  and Wu, Wenhao  and Wu, Yuhao  and Guan, Longyu  and Tao, Jiawen  and Fu, Guohong  and Xu, Xinran  and Wang, Yuzhi  and Lai, Guokun  and Wu, Yuxin  and Zhou, Xinyu  and Yang, Zhilin  and Du, Yulun},
    year          = {2025},
    eprint        = {2510.26692},
    archivePrefix = {arXiv},
    primaryClass  = {cs.CL}
}

Files

38 files · 98.3 GBWeights 98.2 GB (safetensors). Sizes and sha256 from the signed manifest.
Show all 38 files with their sha256
FileSizesha256
.gitattributes1.63 KB70711e003ecefde7929ed9b1e49770523a0a2b539c437498acf3f58d7da67c1d
README.md6.71 KB49edd7069a066c665582cecc51d4028df51ccb64da4301a4ede5017873a2ca8b
chat_template.jinja1.85 KB75e3cd76bd26b77c02507eb35073b66c8ffd0c88eab185ebc6f8c6e425161809
config.json1.75 KB732c453928a14df15ad7b7bf8c626a02236d10eee80ce63a36aa05df73bc17eb
configuration_kimi.py4.95 KB79422aca3ee6c89d201e0c15c4c9a6db517ba83d87ecdc4e41fa0f71297238d9
figures/arch.png238 KB132ae021fa4661ed39e7be784d46f05f22b82aabb9afd2bab8dbdc0a5a61cba0
figures/arxiv.png2.07 KB8a9b6f6422b16d06b4feada756072382a3d9bb0f016e1af646fabb5b79b1950e
figures/banner.png63.2 KB860813b16f049ba7540fe99568729ac62a12cc651d523b4be534ead4fb5a7716
figures/github.png6.39 KB367d5cb2adc609451cf9127250edfe893837485e7c779d7dcb4e31234f59b001
figures/logo.png13.1 KB7870b48105beb49cdb29bb3090abb7bbca688bef862507904c23d9c472df221c
figures/perf_speed.png160 KBf8951e618db41ae57fa0cec4845d7b275dffbd7f9db12c6496bfea536c625aea
generation_config.json147 B8a0f6804e5644a48677317913c4cae06f9df788cb98f6cc90cbc595bd27f6ac2
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special_tokens_map.json5.58 KB16786e586e9ac2ebad36dc32023f055dc3a8682a85c8f274ff07988ab31758b9
tiktoken.model2.80 MBb6c497a7469b33ced9c38afb1ad6e47f03f5e5dc05f15930799210ec050c5103
tokenization_kimi.py12.5 KBb6b304f63d355f29f5f5aa27694337c824c90e1ac80559ca537909824729761f
tokenizer_config.json3.69 KB5ba77d5822137ac3cb9337d2b84561d10425562cf8b8f2c74c91a832516a778d
VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.
Upstream commitDateContents
3b171c17bf pinned now2026-11-14 06:32 UTC38 files · 98.3 GB

Use it

$ fw get moonshotai/Kimi-Linear-48B-A3B-Base
# manifest sha256 4a050be6f302… · 98.3 GB · 38 files
$ fw verify ./Kimi-Linear-48B-A3B-Base moonshotai/Kimi-Linear-48B-A3B-Base
fw.pyChecks the signature and every sha256, tries each source in order, resumes.
$ python3 fw.py get https://freeweights.allternit.com/manifests/moonshotai/Kimi-Linear-48B-A3B-Base.json --dir ./Kimi-Linear-48B-A3B-Base $ python3 fw.py verify ./Kimi-Linear-48B-A3B-Base ./moonshotai__Kimi-Linear-48B-A3B-Base.json
hfThe Hugging Face CLI with one setting. Same repo id.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download moonshotai/Kimi-Linear-48B-A3B-Base --revision 3b171c17bfc4ee348599b6781a2ca8715c21c8dc --local-dir ./Kimi-Linear-48B-A3B-Base
transformersLoad the weights in Python.
import os os.environ["HF_ENDPOINT"] = "https://freeweights.allternit.com" from transformers import AutoModelForCausalLM, AutoTokenizer tok = AutoTokenizer.from_pretrained("moonshotai/Kimi-Linear-48B-A3B-Base", revision="3b171c17bfc4ee348599b6781a2ca8715c21c8dc") model = AutoModelForCausalLM.from_pretrained("moonshotai/Kimi-Linear-48B-A3B-Base", revision="3b171c17bfc4ee348599b6781a2ca8715c21c8dc", device_map="auto")
vLLMServe an OpenAI-compatible API.
$ HF_ENDPOINT=https://freeweights.allternit.com vllm serve moonshotai/Kimi-Linear-48B-A3B-Base --revision 3b171c17bfc4ee348599b6781a2ca8715c21c8dc

License

Name
MIT License
Id
MIT
Class
Permissive
Redistribution
yes
Listing
Hosted (signed)
Decided
Checked and recorded in the license audit. 2026-11-14
Text in
README.md
Text sha256
49edd7069a066c665582cecc51d4028df51ccb64da4301a4ede5017873a2ca8b
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Provenance

From: moonshotai <huggingface.co/moonshotai>Newsgroups: alt.freeweights.kimiSubject: Kimi-Linear-48B-A3B-BaseDate: 2026-11-14Commit: 3b171c1Signed: 2DC28CC7B26A283B ✓
Upstream
moonshotai/Kimi-Linear-48B-A3B-Base
Pinned commit
3b171c17bfc4ee348599b6781a2ca8715c21c8dc
Manifest
Kimi-Linear-48B-A3B-Base.json · .minisig
Manifest sha256
4a050be6f302e37bde4268e257f1399142df44cea0b0ea382524031b5d950be8
Signature
minisign, Ed25519, key 2DC28CC7B26A283B
Created
2026-11-14 06:32 UTC
Generator
freeweights-manifest/0.1.0
Takedowns
None.

How to verify

$ curl -O https://freeweights.allternit.com/manifests/moonshotai/Kimi-Linear-48B-A3B-Base.json $ curl -O https://freeweights.allternit.com/manifests/moonshotai/Kimi-Linear-48B-A3B-Base.json.minisig $ minisign -Vm Kimi-Linear-48B-A3B-Base.json -P RWQ7KGqyx4zCLXtUFOqvfv8L3g+8fpKJ1FsuxPyOYMF+4KufbUxKq1Oq $ shasum -a 256 <file> # compare with the Files tab

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Signed manifest
minisign, key 2DC28CC7B26A283B. Kimi-Linear-48B-A3B-Base.json. Provenance. Takedown history: none.
From: laion <huggingface.co/laion>Subject: kimi-k2-r2egym_sandboxes-maxeps-32k
From: laion <huggingface.co/laion>Subject: kimi-k2t-freelancer-32ep-32k
From: laion <huggingface.co/laion>Subject: Kimi-K2T-ling-coder-sft-sandboxes-1-maxeps-32k
From: laion <huggingface.co/laion>Subject: Kimi-K2T-neulab-agenttuning-kg-sandboxes-maxeps-32k
From: laion <huggingface.co/laion>Subject: Kimi-K2T-neulab-agenttuning-mind2web-sandboxes-maxeps-32k
From: laion <huggingface.co/laion>Subject: Kimi-K2T-neulab-agenttuning-webshop-sandboxes-maxeps-32k