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#Logits Reel by @deeply.ai - The softmax activation function is used in machine learning, especially in classification problems, to convert a model's raw output values (also calle
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@deeply.ai
The softmax activation function is used in machine learning, especially in classification problems, to convert a model's raw output values (also called logits) into probabilities. It helps the model understand the most likely class by turning those logits into values between 0 and 1, where all the values add up to 1, representing probabilities. Here’s how it works in simple terms: - Input Values (Logits): Imagine the model gives several output values (logits), one for each possible class. These values could be any number, positive or negative. - Exponentiation: The softmax function takes these output values, applies an exponential function to each (which makes them all positive and larger), and then normalizes them. - Normalization: To convert these into probabilities, it divides each exponentiated value by the sum of all the exponentiated values. This ensures that the sum of the outputs is 1, making them valid probabilities. For example: If a model predicts three classes with logits [2.0, 1.0, 0.1], softmax will convert these into probabilities like [0.65, 0.24, 0.11]. The highest probability (0.65) shows the model thinks the first class is the most likely. In short, softmax assigns probabilities to different classes so the model can decide which class is most likely. C: 3blue1brown (YT) Unleash the future with AI. Our latest videos explore using machine learning and deep learning to boost your productivity or create mind-blowing AI art. Check them out and see what the future holds 🤖 #ai #chatgpt #aitools #openai #aitips #machinelearning #deeplyai
#Logits Reel by @aiproductlabs - Temperature doesn't change what an LLM knows. It changes how confidently it chooses. Low T → sharp probability distribution → one dominant token → pr
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@aiproductlabs
Temperature doesn’t change what an LLM knows. It changes how confidently it chooses. Low T → sharp probability distribution → one dominant token → predictable, reliable output. High T → flatter distribution → multiple viable tokens → higher variation, higher hallucination risk. Under the hood: temperature scales logits before softmax, reshaping the probability curve. Best practice: start low (0.1–0.3) for accuracy, increase gradually if you need more creativity. Control the distribution. Control the output. #ArtificialIntelligence #LLM #LargeLanguageModels #GenerativeAI #MachineLearning #DeepLearning #AIPrompting #PromptEngineering #AIExplained #AITips #DataScience #AIForDevelopers #TechEducation #AIEducation #AIIndia #AINorthAmerica #AICommunity #OpenAI #LLMTips #AIReels #TechReels #LearnAI #AIInsights #AIInnovation #FutureOfAI #ContentCreators #Developers #TechCreators
#Logits Reel by @unfoldedai - Follow for more @unfoldedai The softmax function transforms a set of numbers (logits) into probabilities that add up to 1, making them useful for ge
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@unfoldedai
Follow for more @unfoldedai The softmax function transforms a set of numbers (logits) into probabilities that add up to 1, making them useful for generation. When we add temperature, it’s like adding a control to adjust how “decisive” these choices become. A low temperature (like 0.1) makes the model choose definite higher values– if it sees logits showing slight preferences, it will strongly commit to the highest ones. High temperature (like 2.0) does the opposite – it makes the model more uncertain and willing to consider options more equally, like a judge who sees shades of gray in every decision. C: @3blue1brown #computerscience #computerengineering #math #machinelearning #coding #datascience #statistics
#Logits Reel by @dailydoseofds_ - "Explain KV caching in LLMs" 🧠 (a popular LLM interview question) KV caching is a technique used to speed up LLM inference. To understand KV cachi
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@dailydoseofds_
"Explain KV caching in LLMs" 🧠 (a popular LLM interview question) KV caching is a technique used to speed up LLM inference. To understand KV caching, we must know how LLMs output tokens: → Transformer produces hidden states for all tokens → Hidden states are projected to vocab space → Logits of the last token generate the next token → Repeat for subsequent tokens Thus, to generate a new token, we only need the hidden state of the most recent token. How the last hidden state is computed: During attention, the last row of query-key-product involves: → The last query vector → All key vectors Also, the last row of the final attention result involves: → The last query vector → All key & value vectors Key insight: To generate a new token, every attention operation only needs: ✅ Query vector of the last token ✅ All key & value vectors But here's the crucial part: As we generate new tokens, the KV vectors for ALL previous tokens do not change. Thus, we just need to generate a KV vector for the token generated one step before. The rest of the KV vectors can be retrieved from a cache to save compute and time. This is KV caching! To reiterate: Instead of redundantly computing KV vectors of all context tokens, cache them. To generate a token: 1️⃣ Generate QKV vector for the token generated one step before 2️⃣ Get all other KV vectors from the cache 3️⃣ Compute attention KV caching saves time during inference (see video below). In fact, this is why ChatGPT takes time to generate the first token - it's computing the KV cache of the prompt. The tradeoff: KV cache also takes a lot of memory. Consider Llama3-70B: → Total layers = 80 → Hidden size = 8K → Max output size = 4K Here: → Every token takes ~2.5 MB in KV cache → 4K tokens = 10.5 GB More users → more memory. 👉 Over to you: Does KV caching make LLMs more practically useful? #ai #llm #transformers
#Logits Reel by @xyz (verified account) - Ambient is a Layer-1 blockchain designed to support artificial intelligence as part of its core infrastructure. It combines the technical foundation o
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@xyz
Ambient is a Layer-1 blockchain designed to support artificial intelligence as part of its core infrastructure. It combines the technical foundation of Solana—a fast and scalable blockchain platform—with a different kind of validation method called “Proof of Logits.” This system rewards users not just for securing the network, but also for running and improving a large AI model. Instead of solving random puzzles, participants help run and train the network’s shared model. In March 2025, Ambient announced it raised $7.2 million in seed funding from a16z’s crypto accelerator program, Delphi Digital, and Amber Group. Visit the #AIMonday link in our bio to learn more. #AI #Blockchain #CryptoAI
#Logits Reel by @getintoai - The softmax function transforms a set of numbers (logits) into probabilities that add up to 1, making them useful for generation. When we add tempera
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@getintoai
The softmax function transforms a set of numbers (logits) into probabilities that add up to 1, making them useful for generation. When we add temperature, it’s like adding a control to adjust how “decisive” these choices become. A low temperature (like 0.1) makes the model choose definite higher values– if it sees logits showing slight preferences, it will strongly commit to the highest ones. High temperature (like 2.0) does the opposite – it makes the model more uncertain and willing to consider options more equally, like a judge who sees shades of gray in every decision. C: @3blue1brown #computerscience #computerengineering #math #machinelearning #coding #datascience #statistics
#Logits Reel by @jganesh.ai (verified account) - Temperature doesn't change reasoning. It changes how logits are scaled before softmax. Here's what that means in practice: 1️⃣ Where temperature app
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@jganesh.ai
Temperature doesn’t change reasoning. It changes how logits are scaled before softmax. Here’s what that means in practice: 1️⃣ Where temperature applies ➤ After the model computes logits (raw token scores) ➤ Before softmax converts them into probabilities ⸻ Temperature = logit scaling factor 2️⃣ Low temperature (≈ 0.1) ➤ Logits are effectively amplified ➤ Small score differences become large ➤ Top token often gets 90%+ probability ⸻ Result: near-deterministic output Same input → same answer Use when: Summarization, extraction, code, eval-heavy paths 3️⃣ Temperature = 1.0 ➤ Logits are left mostly unchanged ➤ Probability mass spreads out ➤ Lower-ranked tokens stay viable ⸻ Result: more diversity, more variance 4️⃣ High temperature (> 1.0) ➤ Logit differences are compressed ➤ Long-tail tokens get boosted ➤ Creativity rises, hallucination risk rises too ⸻ Useful for ideation, not precision 5️⃣ What does not change ➤ The model’s knowledge ➤ The representations ➤ The reasoning path ⸻ Only the sampling distribution changes BOTTOM LINE: Temperature doesn’t change what the model knows. It changes how confidently it picks the next token. That’s why 0.1 feels deterministic and 1.0 feels creative — even though the model itself is the same. TAGS: #llm #ai #engineering #trend
#Logits Reel by @rajistics - MuonClip, used by Moonshot AI during the training of their trillion-parameter Kimi 2 model, addresses a core instability in large-scale transformers:
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@rajistics
MuonClip, used by Moonshot AI during the training of their trillion-parameter Kimi 2 model, addresses a core instability in large-scale transformers: exploding attention logits. Unlike traditional optimizers like Adam or AdamW that adjust step sizes based on gradient slopes, MuonClip actively rescales the query and key matrices after each update, preventing sharp logit growth within attention layers. This innovation allowed Moonshot AI to pre-train Kimi on 15.5 trillion tokens without a single training spike, producing an unusually smooth, stable loss curve. Muon is Scalable for LLM Training — https://arxiv.org/abs/2502.16982 Mounclip from Keller Jordan - https://github.com/KellerJordan/Muon
#Logits Reel by @aibutsimple - The softmax function transforms a set of numbers (logits) into probabilities that add up to 1, making them useful for generation. When we add temper
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@aibutsimple
The softmax function transforms a set of numbers (logits) into probabilities that add up to 1, making them useful for generation. When we add temperature, it’s like adding a control to adjust how “decisive” these choices become. A low temperature (like 0.1) makes the model choose definite higher values– if it sees logits showing slight preferences, it will strongly commit to the highest ones. High temperature (like 2.0) does the opposite – it makes the model more uncertain and willing to consider options more equally, like a judge who sees shades of gray in every decision. C: @3blue1brown Join our AI community for more posts like this @aibutsimple 🤖 #computerscience #computerengineering #math #machinelearning #coding #datascience #statistics
#Logits Reel by @infusewithai - The softmax function transforms a set of numbers (logits) into probabilities that add up to 1, making them useful for generation. When we add tempera
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@infusewithai
The softmax function transforms a set of numbers (logits) into probabilities that add up to 1, making them useful for generation. When we add temperature, it’s like adding a control to adjust how “decisive” these choices become. A low temperature (like 0.1) makes the model choose definite higher values– if it sees logits showing slight preferences, it will strongly commit to the highest ones. High temperature (like 2.0) does the opposite – it makes the model more uncertain and willing to consider options more equally, like a judge who sees shades of gray in every decision. C: @3blue1brown #computerscience #computerengineering #math #machinelearning #coding #datascience #statistics
#Logits Reel by @missgandhi.tech - Temperature doesn't change reasoning. It changes how logits are scaled before softmax. Here's what that means in practice: 1. Where temperature appl
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@missgandhi.tech
Temperature doesn’t change reasoning. It changes how logits are scaled before softmax. Here’s what that means in practice: 1. Where temperature applies » After the model computes logits (raw token scores) » Before softmax converts them into probabilities 👉 Temperature = logit scaling factor 2. Low temperature (= 0.1) » Logits are effectively amplified » Small score differences become large » Top token often gets 90%+ probability 👉 Result: near-deterministic output Same input → same answer ✅ Use when: Summarization, extraction, code, eval-heavy paths 3. Temperature = 1.0 » Logits are left mostly unchanged » Probability mass spreads out » Lower-ranked tokens stay viable 👉 Result: more diversity, more variance 4. High temperature (>1.0) » Logit differences are compressed » Long-tail tokens get boosted » Creativity rises, hallucination risk rises too 👉 Useful for ideation, not precision 5. What doesn’t change? » The model’s knowledge » The representations » The reasoning path 👉 Only the sampling distribution changes BOTTOM LINE: Temperature doesn’t change what the model knows. It changes how confidently it picks the next token. That’s why 0.1 feels deterministic and 1.0 feels creative, even though the model itself is the same. #ai #llm #aiengineering #aiengineer #tech

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