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Added context to my tiny diffusion model to enable sequential generation of longer outputs! Currently the context is a quarter of the sequence length (seq_len=256, context_len=64). I have a theory that the less semantic-value-per-token, the worse the “curse of parallel decoding” is. With parallel decoding, we independently predict multiple...

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Nathan Barry

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89,040 Aufrufe • vor 7 Monaten •via X (Twitter)

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Most recent diffusion language model research (that I’ve seen) seems to be using masking as the noising process. It looks like, however, most closed-source models (Google Gemini Diffusion and possibly Inception Labs’ Mercury) use a different noising process, where instead of masking tokens, they replace them with different tokens (either with a random token or a semantically similar token). I wondered how they were getting such high throughput with the latter noising process, since I believed that optimizing inference with KVCache approximation would be more difficult (for various reasons). I visualized this noising process with tiny-diffusion and compared it to normal unmasking, and was very surprised to see how fast the generation “settles” into a reasonable output, and then only slightly refines afterwards, requiring much fewer steps in total. Unmasking (where tokens are never remasked, the typical implementation) is inherently limited in generation speed by the fact that an increase in tokens decoded per step leads to more errors due to the mismatch between individual and marginal token probability distributions we sample from. The token replacement noising process seems to have a much different set of characteristics. Because we sample each token per step, every token makes “progress” towards the final output each iteration (in addition to *potentially* giving other tokens more information in future steps). Generally, masking has outperformed other noising processes, which is probably why most research focused on it (using smaller models). But the paper referred to in the retweet shows that random replacement as a noising process may scale better as model size increases. Big labs might have noticed these results much earlier (due to having drastically more training resources and being able to test larger models), which may explain the discrepancy in the choice of noising process. I’m gonna test this with larger models, since tiny-diffusion only has 10M parameters.
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Most recent diffusion language model research (that I’ve seen) seems to be using masking as the noising process. It looks like, however, most closed-source models (Google Gemini Diffusion and possibly Inception Labs’ Mercury) use a different noising process, where instead of masking tokens, they replace them with different tokens (either with a random token or a semantically similar token). I wondered how they were getting such high throughput with the latter noising process, since I believed that optimizing inference with KVCache approximation would be more difficult (for various reasons). I visualized this noising process with tiny-diffusion and compared it to normal unmasking, and was very surprised to see how fast the generation “settles” into a reasonable output, and then only slightly refines afterwards, requiring much fewer steps in total. Unmasking (where tokens are never remasked, the typical implementation) is inherently limited in generation speed by the fact that an increase in tokens decoded per step leads to more errors due to the mismatch between individual and marginal token probability distributions we sample from. The token replacement noising process seems to have a much different set of characteristics. Because we sample each token per step, every token makes “progress” towards the final output each iteration (in addition to *potentially* giving other tokens more information in future steps). Generally, masking has outperformed other noising processes, which is probably why most research focused on it (using smaller models). But the paper referred to in the retweet shows that random replacement as a noising process may scale better as model size increases. Big labs might have noticed these results much earlier (due to having drastically more training resources and being able to test larger models), which may explain the discrepancy in the choice of noising process. I’m gonna test this with larger models, since tiny-diffusion only has 10M parameters.

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We are excited to introduce Mercury, the first commercial-grade diffusion large language model (dLLM)! dLLMs push the frontier of intelligence and speed with parallel, coarse-to-fine text generation.
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The Hidden Language of Diffusion Models paper page: tackle the challenge of understanding concept representations in text-to-image models by decomposing an input text prompt into a small set of interpretable elements. This is achieved by learning a pseudo-token that is a sparse weighted combination of tokens from the model's vocabulary, with the objective of reconstructing the images generated for the given concept. Applied over the state-of-the-art Stable Diffusion model, this decomposition reveals non-trivial and surprising structures in the representations of concepts. For example, we find that some concepts such as "a president" or "a composer" are dominated by specific instances (e.g., "Obama", "Biden") and their interpolations. Other concepts, such as "happiness" combine associated terms that can be concrete ("family", "laughter") or abstract ("friendship", "emotion"). In addition to peering into the inner workings of Stable Diffusion, our method also enables applications such as single-image decomposition to tokens, bias detection and mitigation, and semantic image manipulation

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as an intro to mechanistic interpretability, i decided to look into the formation of induction heads, which are circuits that allow LLMs to perform in-context learning by searching for previous occurrences of a sequence to predict the next token to form these circuits i trained attention-only transformers to repeat varying sequence lengths of random tokens. by randomizing the sequence length, i prevented the models from relying on rote memorization, forcing them to instead develop a generalizable circuit during this dive, i recorded some really cool findings and saw some interesting visual patterns emerging:
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as an intro to mechanistic interpretability, i decided to look into the formation of induction heads, which are circuits that allow LLMs to perform in-context learning by searching for previous occurrences of a sequence to predict the next token to form these circuits i trained attention-only transformers to repeat varying sequence lengths of random tokens. by randomizing the sequence length, i prevented the models from relying on rote memorization, forcing them to instead develop a generalizable circuit during this dive, i recorded some really cool findings and saw some interesting visual patterns emerging:

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🚨BRAD GARLINGHOUSE CONFIRMS #XRP WILL REACH THE TOP SPOT!! ONCE XRP SURPASSES BTC AND CLAIMS THE #1 POSITION, IT'S EXPECTED TO REACH A MINIMUM OF $29.37 PER COIN!! ‼️ THE LEADING DEFI TOKENS ON THE XRP LEDGER ARE POISED FOR A MASSIVE RISE!! IMAGINE A MARKET CAP OF $1.65 TRILLION! THE CTF TOKEN, WITH ONLY $20 BILLION IN MARKET VALUE, COULD SOAR FROM $0.80 TO $748.50 PER COIN!! CTF TOKEN HAS A CIRCULATING SUPPLY OF JUST 119 MILLION!! HOW CAN A SUPPLY SHORTAGE NOT SPUR A SURGE!!! THE CTF TOKEN IS DESTINED FOR A HUGE UPSWING!!! Trade CTF token here: Trade CTF token on MEXC: Official Website:

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Vintage Camera Experts 🎥 We appreciate all the camera operator experts and enthusiast that have been reaching out. Ideally, we would love a robust army of imagers helping us gather as much imagery as possible - from different angles, distances, and altitudes with a variety of equipment. Two of the primary challenges for our camera operators right now are digital interference and the extreme speed and movement of the objects. For the former, there seems to be something about the phenomenon that interferes with digital camera technology. Because of this we are looking to also add a vintage camera suite. There’s lots of evidence that suggests analog imagery of the phenomenon produces better results in some circumstances. In regards to the speed and movement challenge, please see the below video clip from episode two. This is what we are dealing with. Note the speed and movement of the Tic Tacs at 100% vs. 15% . If you feel you’re up to the challenge and have something of value to add, please continue to reach out and offer solutions. 🙏🇺🇸

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PAYING PER MODEL IS THE DUMBEST THING IN TECH RIGHT NOW i was paying 3x what i needed to for AI inference the grid lets you buy a quality spec instead of a specific model.. it routes every request in real time to the cheapest option that qualifies swap one url and your code keeps working exactly the same openai-compatible, one line to switch, 200M free tokens to start

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Amazon’s machine learning model collects 300 million data points per season and can now predict which players are likely to blitz before the snap. This is a look into the future of broadcasting.

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The infinity mirror is a configuration of two or more parallel or angled mirrors, which are arranged to create a series of smaller and smaller reflections that appear to recede to infinity [📹 internationalfactory]
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The infinity mirror is a configuration of two or more parallel or angled mirrors, which are arranged to create a series of smaller and smaller reflections that appear to recede to infinity [📹 internationalfactory]

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alright we are not announcing this publicly on AssetDash until tomorrow but $UFD is a perfect case study so here we go many of you are using Whale Watch to discover new coins daily (thank you) and we wanted to release a feature that allows YOU to sell the AssetDash audience on your favorite memes now - anyone on AssetDash can upgrade a token to Pro and add two key aspects to the token page: 1. The Bull Case - 3 to 4 bullet points on why you are bullish on the meme plus additional context 2. The Meme Gallery - a gallery of pictures of the meme TLDR: this is a great way to work for your bags! Upgrade your favorite token here ->

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A New Era with V3🪄 V3's new engine introduces significant advancements in output generation. Unlike V2, where the multi-model system processed prompts to produce a single output, V3 is designed to generate multiple outputs and logically link them together. This enhancement effectively removes limitations on output size, enabling more complex and expansive results. Key Features Seamless Multi-Output Generation: V3 has been trained to generate separate outputs and connect them logically. This advancement ensures that there are no longer any limitations on output size. Intelligent Image Creation: V3 improves image generation with better tools, allowing the AI to create as many images as needed and place them within the project’s context. It supports various formats like PNG, JPEG, SVG, and GLB. Web-Integrated Intelligence: V3 can now search the web for documentation and data, providing real-time context and up-to-date references. For example, if you run a restaurant and want to update your website, simply ask Alchemist AI to “generate this website in a more modern style,” and it will update all content accordingly. Improved Creativity and Output Quality: V3's creative capacity has significantly increased. Simple prompts now generate more complete and refined results, with the system efficiently combining multiple elements into cohesive outputs.

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