createthis

diff --git a/Assets/Plugins/RootMotion/FinalIK/IK Solvers/IKSolverVRLocomotion.cs b/Assets/Plugins/RootMotion/FinalIK/IK So
lvers/IKSolverVRLocomotion.cs
index 122de1b..0b16128 100644
--- a/Assets/Plugins/RootMotion/FinalIK/IK Solvers/IKSolverVRLocomotion.cs
+++ b/Assets/Plugins/RootMotion/FinalIK/IK Solvers/IKSolverVRLocomotion.cs
@@ -12,12 +12,6 @@ namespace RootMotion.FinalIK {
                [System.Serializable]
                public class Locomotion {

-                       [Tooltip("The toe/foot target.")]

createthis

We have some code:  
  
**bjj_calculate_match_difficulty.js**  
  
```js  
function calculateMatchDifficulty(practitioner1, practitioner2) {
    const beltRanks = ['white', 'blue', 'purple', 'brown', 'black'];
    // Calculate the difference in belt ranks
    const beltDiff = beltRanks.indexOf(practitioner2.belt) - beltRanks.indexOf(practitioner1.belt);
    // Calculate the cumulative effect of age, weight, and height

createthis

{% if not add_generation_prompt is defined %}
    {% set add_generation_prompt = false %}
{% endif %}
{% if not thinking is defined %}
    {% set thinking = false %}
{% endif %}
{% set ns = namespace(is_first=false, is_tool=false, system_prompt='', is_first_sp=true, is_last_user=false) %}
{%- for message in messages %}
    {%- if message['role'] == 'system' %}
        {%- if ns.is_first_sp %}

createthis

{#- Unsloth template fixes #}
{% if not add_generation_prompt is defined %}
    {% set add_generation_prompt = false %}
{% endif %}
{% if enable_thinking is defined and enable_thinking is false %}
    {% set thinking = false %}
{% elif enable_thinking is defined and enable_thinking is true %}
    {% set thinking = true %}
{% elif not thinking is defined %}
    {% set thinking = false %}

createthis

import React from "react";
import { ResponsiveContainer, LineChart, Line, XAxis, YAxis, CartesianGrid, Tooltip, Legend } from "recharts";

const showPassRate1 = false;
const data = [
  { name: "TQ1_0", unsloth: undefined, aider: 51.6, pass_rate_1: 19.1 },
  { name: "IQ1_M", unsloth: 79.8, aider: 56.9, pass_rate_1: 24.0 },
  { name: "TQ1_0-thinking", unsloth: undefined, aider: 60.4, pass_rate_1: 26.2 },
  { name: "IQ2_XXS", unsloth: 80.3, aider: undefined, pass_rate_1: undefined },
  { name: "IQ2_M", unsloth: 80.78, aider: 61.3, pass_rate_1: 36.4 },

createthis

Input number line (per character)

<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>get_time<｜tool▁sep｜>{"city": "Tokyo"}<｜tool▁call▁end｜><｜tool▁calls▁end｜>
|　                　| 　               　|        |　        　|                 |　             　| 　              　|
0　                　19　               　38       47　       　58                76　            　92　            　110

Input number line (per byte)

createthis

{% if not add_generation_prompt is defined %}
  {% set add_generation_prompt = false %}
{% endif %}
{% if not thinking is defined %}
  {% set thinking = false %}
{% endif %}
{% set ns = namespace(is_first=false, is_tool=false, system_prompt='', is_first_sp=true, is_last_user=false, is_only_sys=false, is_prefix=false) %}
{%- for message in messages %}
  {%- if message['role'] == 'system' %}
    {%- if ns.is_first_sp %}

createthis

1. Architecture

Compared with DeepSeek-V3.1-Terminus, the last version of DeepSeek-V3.1, the only architectural modification of DeepSeek-V3.2-Exp is the introduction of DeepSeek Sparse Attention (DSA) through continued training.

Prototype of DSA. The prototype of DSA primarily consists of two components: a lightning indexer and a fine-grained token selection mechanism.

The lightning indexer computes an index score $I_{t,s}$ between the query token $\mathbf{h}_t\in\mathbb{R}^d$ and a preceding token $\mathbf{h}_s\in\mathbb{R}^d$, determining which tokens to be selected by the query token:

$$

createthis

This code implements the DeepSeek Sparse Attention (DSA) lightning indexer, which computes index scores for efficient attention using FP8 precision. I'll explain it line by line, breaking it into logical sections. The code uses TileLang (a DSL for GPU kernels) and PyTorch for high-performance computation.

1. Imports and Utility Functions

# ruff: noqa
import itertools
import tilelang
from tilelang import language as T
import torch

createthis

This code implements a high-performance Top-K selection algorithm using TileLang for GPU acceleration. I'll explain it line by line, focusing on the radix-based selection approach.

1. Imports and Configuration

import torch
import tilelang
import tilelang.language as T
pass_configs = {
 tilelang.PassConfigKey.TL_DISABLE_THREAD_STORAGE_SYNC: True,