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HISTORY created in Sher-e-Punjab❗️ 🌟𝙉𝘼𝙈𝘼𝙉 𝘿𝙃𝙄𝙍 scores 173(64) -second highest-ever individual t20 score. -almost beats Chris Gayle's record, misses out by just 2 runs.
34,794 Aufrufe • vor 13 Tagen •via X (Twitter)
23 Kommentare
Incorrect. These state leagues will not be counted in T20s. If that was the case, then Krishnappa Gowtham's 8-15 would've been best T20 spell (stats-wise).
Fuck the commentators in that case they specifically said it the other way round😭.
No worries. I also thought it the same way when Krishnappa took a 8-fer in a T20, which is crazy, forget the level of cricket.
Bowler da fark e bhrawa nale bahla fuddu match lgda 220-230 score de baad koi video game ni a. Agge saale 250 khaa hi jnde
Just 3 runs away from breaking Chris Gayle's record 😔
Power-hitting ka proper masterclass 👏
😳😳😳
Captain Dhir didn’t play an innings. He launched a statement. 173 (64) | 12 sixes. MI, you seeing this? 👀🔥
One more candidate for mi captaincy 🔥 😀
Bhai Naman Dhir ne sach mein tabaahi macha di hai💀
270 strike rate… insane stuff 🫡
Boundaries to bhnchod 40 meter ki h yaha
Bhai ye banda rukne ka naam hi nahi le raha tha
Rakheem kornvell 200 in T20
Someone stop Naman please 😂🔥
Perfect example of absolute carnage broo👏
Saare domestic mein itna tagada khelte Hain IPL international mein aate hi hag dete Hain
When it comes to scouting, you guys are the GOAT, @mipaltan 🛐🔥😎
Bhai ne Shere punjab ko personal training playground bana diya 😂
Absolute carnage by naman dhir salute you bro 🫡
MI fans should be excited for this guy
28 boundaries in one innings 💀
Crazy bhai 😳
![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)
![RLHF by hand ✍️ ~ 15 steps walkthrough below Train a model on human text and it inherits human bias. It will assume a doctor is a "him", because the data says so. RLHF is the correction. A human marks one preference, doc is them over doc is him, and the weights move. But one correction is not the point. The hope is that the model learns the value behind it, gender neutrality, and applies it to professions nobody ever mentioned. How does it work? Goal: train a reward model from a single human comparison about doctors, then turn it on CEOs, filling in every cell yourself. = 1. Given = A reward model, an LLM, and two (prompt, next) pairs. = 2. Preferences = A human reads both pairs and picks a winner: (doc is, them) beats (doc is, him). The loser is not bad grammar, it is gender bias, and that is the whole signal. = 3. Word embeddings = Let us look up each word of the loser pair. These vectors are the reward model's input. = 4. Linear layer = We multiply by the reward model's weights and add its biases. Out come feature vectors, one per position. = 5. Mean pool = Let us multiply by [1/3, 1/3, 1/3], which averages the three positions into one sentence embedding. = 6. Output layer = We map that sentence down to a single number. Reward = 3. = 7. The winner, the same way = Let us repeat steps 3 to 6 on the winning pair. Reward = 5. = 8. Winner minus loser = We take the gap: 5 - 3 = 2. The reward model wants this positive and as large as it can make it. = 9. Loss gradient = Let us squash the gap into a probability, σ(2) ≈ 0.9, and subtract the target of 1. The gradient is -0.1, and it goes back through the purple weights. The reward model is now trained. = 10. A prompt it has never seen = We start the second half with "[S] CEO is". The feedback in step 2 was about doctors. Nothing connects a CEO to a doctor except what the reward model generalised. = 11. Transformer = Let us push it through attention and a feed forward layer, one vector per position. = 12. Output probabilities = We map each vector to a score over the vocabulary. = 13. Sample = Let us take the highest score. The model completes "CEO is" with "him", which is the same bias the human penalised in step 2. = 14. Score it with the reward model = We feed the new pair (CEO is, him) through steps 3 to 6. Reward = 3, exactly the score it gave "doc is him" in step 6. Nobody taught it about CEOs. The value transferred. = 15. Loss gradient = Let us set the loss to the negative of the reward, so minimising the loss maximises the reward. The gradient is a constant -1, and it goes back through the red weights. The outputs: Loser reward = 3, winner reward = 5 Reward gap = 2, predicted σ ≈ 0.9, reward model gradient = -0.1 LLM samples "him", reward = 3, LLM gradient = -1 Congrats! You just calculated RLHF by hand. And you watched a value generalise: one comparison about doctors, and the model marks down "CEO is him" unprompted. 💾 Save this post!](https://image.24vids.com/tw-2081745292051845420/ext_tw_video_thumb/2081745247344676864/pu/img/ahcUqHG6T2iBoOlI.jpg)
