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Absolute Encoder Vs Incremental Encoder

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S.T.E.M Explorer

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622,297 görüntüleme • 4 ay önce •via X (Twitter)

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Daha önce hiç Absolute Encoder ürününe denk gelmemiştim. Bir video ile inanılmaz bir aydınlanma yaşadım. Benim gibi karanlıkta olanlara gelsin...
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Daha önce hiç Absolute Encoder ürününe denk gelmemiştim. Bir video ile inanılmaz bir aydınlanma yaşadım. Benim gibi karanlıkta olanlara gelsin...

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Crazy how transformers work! It’s the encoder part

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The Vision Encoder: Our Secret Weapon 🔥 An ultra-accurate vision encoder plate, combined with 5μm-resolution optical feedback, calibrates the H2D’s motion system to achieve consistent 50μm accuracy across the entire build plate. #BambuH2D

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Training Wan 2.1-1.3B to use Qwen3-VL-2B text encoder. Doing 33% text only, 33% VL only, and 33% both. Just pretraining the two text input linear layers for now. It is amazing how quickly a model can adapt to a different text encoder. This is 25,750 steps, BS of 10.

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manual control of the monster mwir lens achieved. gonna hook into the magnetic end stop + encoder to replace the proprietary Telic Optics board.
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manual control of the monster mwir lens achieved. gonna hook into the magnetic end stop + encoder to replace the proprietary Telic Optics board.

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i just ran Google's brand new Unsloth Gemma4 12B dense GGUF on my RTX 4060 using llama.cpp + CUDA 13.2 21 tokens per second. on a budget consumer GPU. locally. no API. no cloud. no subscription. and the benchmarks are absolutely cooked # first let's talk architecture because this is genuinely different every multimodal model you've used has a frozen vision encoder + frozen audio encoder + LLM backbone glued together Gemma 4 12B is different it's a single decoder only transformer. that's it. vision? raw 48×48 pixel patches → one matmul → projected directly into the LLM audio? raw 16kHz signal sliced into 40ms frames → linear projection → same LLM input space no encoder tax. no latency penalty. no fragmented memory to put the encoder savings in perspective: old Gemma 4 26B approach: - 550M param vision encoder (frozen) - 300M param audio encoder (frozen) - LLM backbone Gemma 4 12B: - 35M param vision embedder (a single matmul) - no audio encoder at all - LLM backbone handles EVERYTHING 550M → 35M for vision alone. that's a 15x reduction this is why the gemma-4-12b-it-Q4_K_M.gguf is just 6.6 GBs!!! and it has 256K native context context # Benchmarks: AIME 2026 (math olympiad): 77.5% GPQA Diamond (expert science): 78.8% LiveCodeBench v6 (real code): 72% Codeforces ELO: 1659 MMLU Pro: 77.2% MATH-Vision: 79.7% BigBench Extra Hard: 53% inference → llama.cpp, LM Studio, vLLM, SGLang llamacpp flags: -m "gemma-4-12b-it-Q4_K_M.gguf" -ngl 99 -c 8000 -v --port 8080 Available on huggingface now! Link below
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i just ran Google's brand new Unsloth Gemma4 12B dense GGUF on my RTX 4060 using llama.cpp + CUDA 13.2 21 tokens per second. on a budget consumer GPU. locally. no API. no cloud. no subscription. and the benchmarks are absolutely cooked # first let's talk architecture because this is genuinely different every multimodal model you've used has a frozen vision encoder + frozen audio encoder + LLM backbone glued together Gemma 4 12B is different it's a single decoder only transformer. that's it. vision? raw 48×48 pixel patches → one matmul → projected directly into the LLM audio? raw 16kHz signal sliced into 40ms frames → linear projection → same LLM input space no encoder tax. no latency penalty. no fragmented memory to put the encoder savings in perspective: old Gemma 4 26B approach: - 550M param vision encoder (frozen) - 300M param audio encoder (frozen) - LLM backbone Gemma 4 12B: - 35M param vision embedder (a single matmul) - no audio encoder at all - LLM backbone handles EVERYTHING 550M → 35M for vision alone. that's a 15x reduction this is why the gemma-4-12b-it-Q4_K_M.gguf is just 6.6 GBs!!! and it has 256K native context context # Benchmarks: AIME 2026 (math olympiad): 77.5% GPQA Diamond (expert science): 78.8% LiveCodeBench v6 (real code): 72% Codeforces ELO: 1659 MMLU Pro: 77.2% MATH-Vision: 79.7% BigBench Extra Hard: 53% inference → llama.cpp, LM Studio, vLLM, SGLang llamacpp flags: -m "gemma-4-12b-it-Q4_K_M.gguf" -ngl 99 -c 8000 -v --port 8080 Available on huggingface now! Link below

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Modern tech tends to focus on “more”. More data. More bandwidth. More compute. The best software engineering happens in the “less” sphere; codec engineering. Listen to this stream with 90% packet loss. The Opus Encoder team is cracked:
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Modern tech tends to focus on “more”. More data. More bandwidth. More compute. The best software engineering happens in the “less” sphere; codec engineering. Listen to this stream with 90% packet loss. The Opus Encoder team is cracked:

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Autoencoder by hand✍️Excel~ I designed this exercise to show how an Encoder-Decoder network convert input to code and reconstruct input from code. It is annotated with equations, PyTorch, and graphs. 👇Join the 'AI Math' community. Download xlsx.

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We introduce TurboEdit -- simple text-based image editing in 1/2 sec! We leverage few-step diffusion models, mapping real images into noise with an encoder. Please see our #ECCV2024 paper: Work w/ Zongze Wu, Nick Kolkin, Jon Brandt, Eli Shechtman 1/
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We introduce TurboEdit -- simple text-based image editing in 1/2 sec! We leverage few-step diffusion models, mapping real images into noise with an encoder. Please see our #ECCV2024 paper: Work w/ Zongze Wu, Nick Kolkin, Jon Brandt, Eli Shechtman 1/

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Autoencoder by hand✍️Excel~ I designed this exercise to show how an Encoder-Decoder network convert input to code and reconstruct input from code. It is annotated with equations, PyTorch, and graphs. I also made a medium version.👇Join the 'AI Math' community. Download xlsx.
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Autoencoder by hand✍️Excel~ I designed this exercise to show how an Encoder-Decoder network convert input to code and reconstruct input from code. It is annotated with equations, PyTorch, and graphs. I also made a medium version.👇Join the 'AI Math' community. Download xlsx.

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Can a VLM see without a vision encoder? We trained one for $100, inspired by Gemma 4 12B. Latency on an M3 Pro MacBook: 112 ms -> 1.1 ms for the image path 30% lower end-to-end image+LLM The architecture is just: patchify the image -> linear projection with pos embeddings -> LLM Writeup:

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Protein language models just got an upgrade. Meet Profluent-E1: a free, flexible, frontier protein sequence encoder. E1 is built with retrieval augmentation to learn from multiple sequences. Models trained over 4T tokens with only 150M-600M params, E1 is SOTA for zero-shot functional and unsupervised structural tasks. It raises the bar for protein representation learning and is freely available today.

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THE BEST visual explainer of how information propagates through a transformer. If you want to have more than intuition about how the Transformer architecture is ruling the LLM world - open-source project explains everything about LLM Transformer Models! - A great resource for anyone looking to gain a deeper understanding of how Transformer-based AI models like GPT work, including: - Self-attention mechanisms - Encoder-decoder architecture - Positional encoding - Multi-head attention

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absolute icons.
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absolute icons.

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