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The Sterling BINI ICONS Spiral Notebooks are now available ! Each notebook features an icon that represents each Bini babe. Collect them all by solo blind box purchase or by purchasing the set . Solo ( Random design in blind box) Set (8 designs featuring 8 Bini members) #SterlingxBINI... show more
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![🎬Whimsical Reverie | Furniture Preview 🛋️Home Sentiments In celebration of the Home system's launch, stylists can obtain free furniture items via the [Home Sentiments] event after the Version 1.9 update on September 1, 2025 (UTC-7). When the event starts, unlock 1 free furniture piece each day. The event lasts 3 days—claim up to 3 free items in total! The event has 3 rounds, giving you plenty of chances to decorate your home! 🛋️Dreamland Showcase Stylists can earn Dreamland Stones by completing weekly Chronicle Tasks. Dreamland Stones can be used to exchange for items in the Dreamland Shop, including the [Detective's Study] series of furniture and Diamonds. Spend 680 Stellarites to sign the [Dreamland Pact]. Once signed, you can instantly claim the new furniture [Loving Fufu & Flowers] and its Furniture Sketch. Complete the current Chronicle Tasks to earn bonus Dreamland Stones. After signing the [Dreamland Pact] and accumulating a certain number of Dreamland Stones in this phase of Dreamland Showcase, 680 Stellarites will be refunded. You can claim the Stellarites in Dreamland Pact interface. 🛋️Chorus of Stars Purchase the "Stellar Poems Pack" for 1,480 Diamonds to receive 12 types of Diamond-purchased furniture and 8 types of Astralite exchangeable furniture, totaling 20 types and 38 pieces. This pack can only be purchased once. Each type of furniture can be purchased separately with no purchase limit, but it will not be available at the discounted set price. 🛋️Furniture Set Series Purchase the "Leisure Tea Brewing Set I" for 680 Stellarites to receive 6 types of furniture, totaling 14 pieces, including the [Eternal Years Screen]. This set can only be purchased once. Each type of furniture can be purchased separately with no purchase limit, but it will not be available at the discounted set price. Purchase the "Leisure Tea Brewing Set II" for 420 Stellarites to receive 6 types of furniture, totaling 11 pieces, including the [Twilight Incense Burner]. This set can only be purchased once. Each type of furniture can be purchased separately with no purchase limit, but it will not be available at the discounted set price. Purchase the "Leisure Tea Brewing Set III" for 180 Stellarites to receive 3 types of furniture, totaling 8 pieces, including the [Rolling Screen]. This set can only be purchased once. Each type of furniture can be purchased separately with no purchase limit, but it will not be available at the discounted set price. 🛋️Stargazing Reveries Purchase the [Astral Radiance Pack] to receive selected furniture pieces such as [Stellar Cascade], [Echo Waterwheel], and [Seasons Couch]. These selected items are tagged as both "Home Furniture" and "Decoration," and may be placed in your Home or in Miraland. The set is limited to one purchase. If you prefer to buy items individually: [Stellar Cascade] is available for purchase, [Seasons Couch] for 680 Stellarites, and [Echo Waterwheel] for 450 Stellarites. Individual pieces of furniture can be purchased with no purchase limit. —— The Coziest Open-World Game! Infinity Nikki Version 1.9 "Music Season" launches globally on September 1st (UTC-7)! ➤ Download Now: ➤ Join us on Facebook Group: #InfinityNikki](https://image.24vids.com/tw-1962002559859908730/amplify_video_thumb/1961997449943547904/img/u8JpVcMjyKL6WvZ4.jpg)
![Self Attention by hand ✍️ ~ 9 steps walkthrough below Self-attention is what enables LLMs to understand context. How does it work? So I drew and calculated one entirely by hand. Goal: turn four 6D features into four 3D attention weighted features, filling in every cell yourself. = 1. Given = Four feature vectors, six dimensions each, one per position. = 2. Query, key, value = Let us multiply the features by WQ, WK and WV. Queries, keys and values all come out of the same four features, and that is what the word "self" is doing in self-attention. = 3. Prepare for MatMul = We copy the queries across the top and the transposed keys down the side. Lining the two up is half the work. = 4. MatMul = Let us multiply K transpose by Q. Every cell is the dot product of one key with one query, which we use as a matching score. That works because the dot product is the numerator of cosine similarity: it is how alike two vectors are, before anyone divides by their lengths. = 5. Scale = We divide by the square root of dk, the dimension of a key vector, here 3. Without it the scores grow with the dimension and a 64-wide head would swamp the softmax. To keep the page doable in pen, the drawing approximates dividing by root 3 with halving. = 6. e to the power = Let us raise e to the power of each score. This is the first half of softmax, and the drawing uses 3 in place of e, which is close enough to do in your head. = 7. Sum = We add up each column: 16, 6, 7 and 12. = 8. Normalize = Let us divide every cell by its column sum. That gives the attention weight matrix in yellow, and each of its four columns is now a probability distribution over the four positions. The decimals are nudged as they are rounded, so every column still sums to exactly 1. = 9. MatMul = We multiply the value vectors by those weights. Each output is a blend of all four values, mixed in the proportion the attention matrix just decided, and it goes to the position-wise feed forward network in the next layer: the FFN box at the bottom of the page. The outputs: Attention weights (A), by column = [.2, .6, 0, .2], [.2, .4, .2, .2], [.4, .2, 0, .4], [.1, .7, .1, .1] Attention weighted features (Z) = [8, 2, 6], [8, 4, 4], [16, 4, 2], [4, 2, 7] The takeaway: attention is a weighted average, and everything before step 9 exists to decide the weights. Compare every position with every other, turn the scores into one distribution per position, then blend. 💾 Save this post!](https://image.24vids.com/tw-2079933315990057242/ext_tw_video_thumb/2079933302048219136/pu/img/q3wr-ymUWG3Ai-ij.jpg)
![[Self-Attention] by Hand ✍️ Self-attention is what enables LLMs to understand context. How does it work? This exercise demonstrates how to calculate a 6-3 attention head by hand. Note that if we have two instances of this, we get 6-6 attention (i.e., multi-head attention, n=2). -- 𝗚𝗼𝗮𝗹 -- Transform [6D Features 🟧] to [3D Attention Weighted Features 🟦] -- 𝗪𝗮𝗹𝗸𝘁𝗵𝗿𝗼𝘂𝗴𝗵 -- [1] Given ↳ A set of 4 feature vectors (6-D): x1,x2,x3,x4 [2] Query, Key, Value ↳ Multiply features x's with linear transformation matrices WQ, WK, and WV, to obtain query vectors (q1,q2,q3,q4), key vectors (k1,k2,k3,k4), and value vectors (v1,v2,v3,v4). ↳ "Self" refers to the fact that both queries and keys are derived from the same set of features. [3] 🟪 Prepare for MatMul ↳ Copy query vectors ↳ Copy the transpose of key vectors [4] 🟪 MatMul ↳ Multiply K^T and Q ↳ This is equivalent to taking dot product between every pair of query and key vectors. ↳ The purpose is to use dot product as an estimate of the "matching score" between every key-value pair. ↳ This estimate makes sense because dot product is the numerator of Cosine Similarity between two vectors. [5] 🟨 Scale ↳ Scale each element by the square root of dk, which is the dimension of key vectors (dk=3). ↳ The purpose is to normalize the impact of the dk on matching scores, even if we scale dk to 32, 64, or 128. ↳ To simplify hand calculation, we approximate [ □/sqrt(3) ] with [ floor(□/2) ]. [6] 🟩 Softmax: e^x ↳ Raise e to the power of the number in each cell ↳ To simplify hand calculation, we approximate e^□ with 3^□. [7] 🟩 Softmax: ∑ ↳ Sum across each column [8] 🟩 Softmax: 1 / sum ↳ For each column, divide each element by the column sum ↳ The purpose is normalize each column so that the numbers sum to 1. In other words, each column is a probability distribution of attention, and we have four of them. ↳ The result is the Attention Weight Matrix (A) (yellow) [9] 🟦 MatMul ↳ Multiply the value vectors (Vs) with the Attention Weight Matrix (A) ↳ The results are the attention weighted features Zs. ↳ They are fed to the position-wise feed forward network in the next layer.](https://image.24vids.com/tw-1797249951325434315/ext_tw_video_thumb/1797249707795697665/pu/img/bRQFS-RcgUFbmOnk.jpg)