Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

Private coaching divides students — Star Batch for 2%, Crowd Batch for 98%. Best teachers for toppers only. Others get average tutors and crowded halls. Abhyudaya has no star batch, no crowd batch. Every student — son of a Bundelkhand farmer or city officer — sits equally in the...

241,565 Aufrufe • vor 4 Monaten •via X (Twitter)

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

🚨 FRAUD COACHING CLASS BUSTED BY MNS! 🚨 In Kalyan, a man posing as a GST officer was exposed by the MNS Student Wing for running fake coaching classes under the name “Siddharth Logic” for UPSC, MPSC , banking and other competative exams. ❌ No real teachers ❌ No training ❌ Fake credentials ✅ Fees collected: ₹30K–₹50K from over 400 students!💰 Estimated scam: ₹20–30 lakh! In Kalyan, near the railway station, a man named Siddharth Singh Chandel, posing as a GST officer, was exposed by the student wing of Maharashtra Navnirman Sena (MNS) for running fraudulent coaching classes under the name “Siddharth Logic”, claiming to offer coaching for UPSC, MPSC, banking, and other competitive exams. Following numerous complaints from students, MNVS District president Mr. Paresh Chaudhary and MNVS City president Mr. Vinod Kene confronted the director of the classes. When Siddharth Chandel failed to give any satisfactory answers nor documents, MNS took strict action against him. It was discovered that through two branches in Kalyan and Navi Mumbai, an estimated 400 to 500 students were charged fees ranging from ₹30,000 to ₹50,000 each. In reality, there were no qualified teachers, no proper training, and not even a single experienced faculty member. Once students realized they had been scammed and demanded refunds, they were allegedly threatened. The total fraud is estimated to be around ₹20 to ₹30 lakh. Furthermore, Siddharth Chandel, who falsely claimed to be a GST officer, had no official documentation or credentials to prove his claim. Upon discovering the scam, MNS student wing officials took immediate action and handed him over to the police. Maharashtra Navnirman Vidyarthi Sena has appealed to students to thoroughly verify any coaching class before enrolling, in order to avoid falling victim to such frauds. 📢 Students, BE AWARE!Always verify before enrolling in any coaching class. Raju Patil ( प्रमोद (राजू) रतन पाटील ) MNVS Adhikrut - मनविसे अधिकृत #MNS #StudentRights #CoachingFraud #KalyanNews #EducationScam #MNSStudentWing #UPSC #MPSC #FraudAlert #Manse #Exposed

MNS Social | मनसे सोशल

48,869 Aufrufe • vor 1 Jahr

Continuous batching in LLMs, clearly explained: (a popular LLM interview question; bookmark this) In traditional ML inference, a batch is a matrix. Every input is padded to the same length, one forward pass runs, and every row finishes at the same moment. LLM decoding does not work that way. One forward pass produces one token per sequence, so a request needs as many passes as it has output tokens, and nobody knows that count until the model emits a stop token. Under static batching, membership is fixed when the batch starts. A request that finishes in 30 tokens holds its slot until the slowest request in the same batch finishes at 400. The GPU keeps paying the full weight read for a batch that is mostly empty. Loading model weights out of HBM costs the same whether four slots are producing tokens or one. Continuous batching moves the decision boundary. Instead of scheduling once per batch, the scheduler runs a single forward pass, gets control back, and decides again. A finished request leaves at the next iteration boundary, and a queued request takes its slot right there. No slot stays reserved for work that is already done. Anyscale benchmarked both OPT-13B on a single A100. With uniform generation lengths, the two policies came out about level (as expected), and as output length variance rose, static batching fell to around 81 tokens per second while vLLM reached 23x the throughput of naive Hugging Face serving. Variance drives the entire gap. Production traffic mixes 30-token replies with 400-token ones, which is exactly the condition static batching handles worst. None of this alters the model. vLLM, SGLang, TGI, and TensorRT-LLM all run it by default, and NVIDIA ships the same mechanism under the name in-flight batching. The animation below runs both policies on the same 16 requests and the same 4 slots, stepping in lockstep. The only difference is when a new request is allowed in. To dive deeper into continuous batching specifically, I wrote a full breakdown of the scheduler underneath it. It covers what happens between two forward passes, how tokens get handed out against a fixed budget, why the scheduler needs no separate path for prefill and decode, and what preemption costs you when the KV cache fills up mid-generation. Read it below.

Avi Chawla

16,140 Aufrufe • vor 1 Monat