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Support generation with caching for SSMs and SSM–Transformer hybrids.

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还没有人认领这个 Issue。

评估

难度
5/5
预计耗时
一周以上
新手友好度
8/100
Issue 类型
功能
描述清晰度
需要澄清
活跃度
停滞
技术栈
python
领域
machine-learning

调研方向

The issue is a placeholder: Step 1 and Step 2 are both 'tbd', and it depends on #278 and #279, which are not in the payload. Start by reading those two issues to learn what they establish, then ask the maintainers to define the smallest working version. Done means generation caching works for SSM and hybrid models under data and tensor parallelism, with tests and a performance summary.

由索引模型根据 Issue 内容生成。

描述

enhancement need update

🎯 Goal (What & Why)

Support generation with caching for SSMs and SSM–Transformer hybrids. Depends on #278 and #279
Must support data and tensor parallelism.

🚀 Execution Plan

Step 1: What is the smallest working version?

tbd

Step 2: What additional optimizations are possible (but optional)?

tbd

📌 Acceptance Criteria (Must-Haves for Completion)

  • The feature must be functional and tested.
  • The implementation must be documented in practical terms.
  • The PR must include a performance/impact summary.
  • No refactors unless directly necessary for feature completion.

🛠️ Project Management

  • Assign the project to the Fast-LLM project.
  • Set the Estimate field (in days) in the GitHub project.
  • Use the Size field to categorize the PR size (Small/Medium/Large).
  • Assign an owner when opening the issue.
主要语言
Python
星标
337
派生
52
30 天内没有已合并 PR

环境准备

从这里开始

  1. 先读完整个 Issue，再读项目的贡献指南。
  2. 在 Issue 下留言说明你要接手 —— 这能避免两个人做同样的事。
  3. Fork 仓库，在一个分支上完成修改。
  4. 提交 Pull Request，并在描述里引用这个 Issue 编号。

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