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Gemini Executive Synthesis

Hosting the Kimi Linear AttnRes model checkpoint on Hugging Face.

Technical Positioning
Maximizing visibility, discoverability, and ease of access for the Kimi Linear AttnRes model by leveraging the Hugging Face platform, thereby accelerating adoption and community engagement.
SaaS Insight & Market Implications
Hugging Face is actively soliciting MoonshotAI to host their Kimi Linear AttnRes model checkpoint. This highlights the critical role of model hubs in the AI ecosystem for discoverability and adoption. The Kimi Linear AttnRes model, with its 48B parameters and 1.4T token pre-training, represents a significant asset. Hosting on Hugging Face would provide immediate visibility, standardized access via `from_pretrained`, and community engagement. For B2B SaaS, leveraging established platforms like Hugging Face is a strategic imperative for distributing models, validating research, and building a developer community. It reduces friction for potential users and partners, accelerating market penetration and demonstrating commitment to open science.
Proprietary Technical Taxonomy
Attention Residuals Kimi Linear architecture 48B total / 3B activated parameters pre-training 1.4T tokens downstream performance pre-trained model checkpoint Hugging Face models

Raw Developer Origin & Technical Request

Source Icon GitHub Issue Mar 17, 2026
Repo: MoonshotAI/Attention-Residuals
Release Kimi Linear AttnRes model on Hugging Face

Hi @yzhangcs 🤗

I'm Niels and work as part of the open-source team at Hugging Face. I discovered your work through Hugging Face's daily papers as yours got featured: huggingface.co/papers/2603.15031
The paper page lets people discuss about your paper and lets them find artifacts about it (your models for instance),
you can also claim the paper as yours which will show up on your public profile at HF, add Github and project page URLs.

Your paper introduces "Attention Residuals" and mentions integrating it into the Kimi Linear architecture (48B total / 3B activated parameters), pre-training it on 1.4T tokens, and evaluating its improved downstream performance. The paper also states that the weights for this model are available at your GitHub repository. It would be fantastic if you would consider hosting this pre-trained Kimi Linear AttnRes model checkpoint on huggingface.co/models!

Hosting on Hugging Face will give you more visibility/enable better discoverability. We can add tags in the model cards so that people find the models easier, link it to the paper page, etc.

If you're interested, here's a guide: huggingface.co/docs/hub/models-u... If it's a custom PyTorch model, you can use the [PyTorchModelHubMixin](huggingface.co/docs/huggingface_...
class which adds `from_pretrained` and `push_to_hub` to the model, allowing users to download and use models right away.
Alternat...

Developer Debate & Comments

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Adjacent Repository Pain Points

Other highly discussed features and pain points extracted from MoonshotAI/Attention-Residuals.

Extracted Positioning
Academic integrity and proper citation practices in MoonshotAI's research papers.
Addressing concerns about the originality and proper attribution of research by ensuring all relevant prior work is cited, particularly when similarities to other published papers are noted.
Top Replies
chuanyang-Zheng • Mar 17, 2026
> https://arxiv.org/abs/2502.06785 和这篇几乎一样,但是文章中一点也不提及 之前也是这样 [MoonshotAI/Kimi-Linear](https://github.com/MoonshotAI/Kimi-Linear/issues/4) Attention Residual是Layer Dimensi...
xxyh1993 • Mar 31, 2026
啊?咱们下载的不是同一篇技术报告?
cho104 • Mar 31, 2026
I’m a bit confused by the flow of this thread. The OP originally linked to the "DeepCrossAttention paper" (published Feb 10, 2025). Since that paper's concepts seem very closely related to this rep...
Extracted Positioning
Community engagement/acknowledgment for MoonshotAI's Attention-Residuals.
Fostering community interaction and acknowledging interest in the Attention-Residuals project, even through informal 'check-in' comments.
Extracted Positioning
Compatibility and synergistic benefits of Attention Residuals with mHC (presumably a memory or caching mechanism).
Exploring the potential for combining Attention Residuals with mHC to achieve superior performance or efficiency, indicating a focus on architectural integration and optimization.
Extracted Positioning
Implementation code for Full Attention Residuals.
Providing concrete implementation code for Full Attention Residuals to validate theoretical understanding and ensure correct application of the technique, especially where only pseudocode for Block Attention Residuals is available.
Extracted Positioning
Code availability for the 'Attention Residuals' technique.
Providing practical implementation code to enable developers to utilize the 'Attention Residuals' technique, moving beyond theoretical descriptions.

Engagement Signals

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Issue Status

Cross-Market Term Frequency

Quantifies the cross-market adoption of foundational terms like Attention Residuals and Kimi Linear architecture by tracking occurrence frequency across active SaaS architectures and enterprise developer debates.