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  "path": "/t/would-this-concept-model-work/175056#post_2",
  "publishedAt": "2026-04-08T00:12:09.000Z",
  "site": "https://discuss.huggingface.co",
  "tags": [
    "BitNet",
    "arXiv",
    "GitHub"
  ],
  "textContent": "Since BitNet works, I suppose it’s conceptually possible…\n\n* * *\n\n**It can work as a research model**. I would not expect the first full run to be the easiest or safest way to get the best 1B model from 40B tokens. The concept is technically plausible because each major piece has public precedent: native ternary training in BitNet, hybrid 8/4-bit activation handling plus 3-bit KV support in BitNet a4.8, strong masked diffusion language modeling in MDLM, and block diffusion for arbitrary-length semi-parallel generation with KV caching. The risk is that your design combines **four hard optimizations at once** , and the public literature does not yet show a mature, standard recipe for that exact stack in one training run. (arXiv)\n\n## The short answer\n\nMy judgment for **your exact case** is:\n\n  * **Conceptually sound:** yes.\n  * **Likely to train at all:** yes, if staged carefully.\n  * **Likely to be stable from step 1 with messy code:** no, that is the weak point.\n  * **Likely to beat a simpler 1B baseline on quality-per-training-compute immediately:** probably not.\n  * **Likely to become an interesting efficient system if you sequence the hard parts carefully:** yes. (arXiv)\n\n\n\n## Why the idea is reasonable\n\nYour design is coherent. It is trying to reduce cost in four different places:\n\n  1. **Weights** with ternary values. BitNet b1.58 explicitly uses ternary `{-1,0,1}` weights and argues that native low-bit training can match full-precision models of similar scale and token budget. Microsoft’s later 2B4T report extends that claim to a larger open model. (arXiv)\n\n  2. **Runtime activations** with a hybrid 8/4-bit path. BitNet a4.8 is the closest public match here. It does not just say “4-bit activations everywhere.” It says 4-bit for selected inputs to attention and FFN, while sparsifying and 8-bit-quantizing intermediate states to control outliers. (arXiv)\n\n  3. **Attention state memory** with low-bit KV cache. KIVI shows 2-bit KV compression can preserve quality well while cutting memory a lot, QServe shows practical W4A8KV4 serving, and Google’s TurboQuant claims 3-bit KV compression without retraining or fine-tuning and without quality loss on its reported benchmarks. (arXiv)\n\n  4. **Generation order** with diffusion instead of fully sequential AR decoding. MDLM shows masked diffusion language models can get much closer to AR than older diffusion NLP work, and block diffusion adds arbitrary-length generation, KV reuse, and parallel token sampling. (arXiv)\n\n\n\n\nSo the high-level idea is not random. It is a real systems thesis. (arXiv)\n\n## Why I am still cautious\n\nThe public evidence is favorable to **each ingredient separately** , but much less favorable to **all of them being hard at once**.\n\nThe main warning sign is low-bit attention. Attn-QAT says reliable 4-bit attention is difficult because FP4 has tiny dynamic range and attention activations are heavy-tailed. It also reports that naive “drop-in” QAT leads to instability if the backward pass assumes higher precision than the forward pass actually used. That is extremely relevant to your hybrid 8/4-bit activation design. (arXiv)\n\nThe second warning sign is diffusion-specific quantization. A recent systematic study on quantizing diffusion LLMs says dLLMs have activation outliers, that W8A8 is usually close to lossless, and that W4A4 is still hard, especially for harder tasks. Another diffusion-LLM quantization paper says dynamic masking, iterative generation, and bidirectional attention all clash with standard quantization assumptions. That is exactly the place where your model is most exposed: **low-bit activations inside a diffusion-style attention stack**. (arXiv)\n\nSo the architecture is plausible, but the fragile junction is very specific: **diffusion + low-bit attention activations** , not ternary weights alone and not KV compression alone. (arXiv)\n\n## What each ingredient means in your setup\n\n### 1. MDLM plus block diffusion\n\nThis is the most novel modeling part of your stack. MDLM is now a serious baseline, not just an experiment. It showed masked diffusion can approach AR perplexity with a strong training recipe. Block diffusion then extended that line by interpolating AR and diffusion behavior, adding variable-length generation, KV caching, and parallel blockwise sampling. (arXiv)\n\nBut diffusion language modeling still carries a tax. A 2026 scaling study says masked diffusion can be made about 12% more FLOPs-efficient with a simpler cross-entropy objective, yet also argues that perplexity is not sufficient across diffusion families and that some interpolating methods have different speed-quality tradeoffs. Another controlled comparison found AR and MDLM had similar raw training throughput on the tested setup, but AR converged faster while MDLM kept improving longer. That means diffusion is viable, but it is not yet the easy default. (arXiv)\n\nFor you, that means block diffusion is a **real feature** , not a gimmick. But it also means you are starting from a training regime that is less forgiving than plain AR. (arXiv)\n\n### 2. Ternary weights\n\nThis is the strongest part of your plan. BitNet and BitNet b1.58 are the cleanest public evidence that native ternary training can work at scale. Microsoft’s 2B4T technical report strengthens that case further. If I had to choose one part of your concept to trust the most, it would be the ternary-weight core. (arXiv)\n\nThe caution is not “ternary is fake.” The caution is that the official public BitNet stack is much more mature on **inference** than on open training. The official repo is an inference framework, and public requests about training code and training behavior are still visible. That suggests native ternary training is real, but not yet as operationally standardized as ordinary Transformer pretraining. (GitHub)\n\n### 3. Hybrid q8 / q4 activations\n\nThis is the most important word in your description: **hybrid**.\n\nThat makes your idea much more plausible than “all 4-bit activations everywhere.” BitNet a4.8 is effectively telling you that selective low-bit activation handling is the viable route. It keeps some paths at 4-bit, some in sparsified 8-bit form, and frames the whole thing as a strategy to mitigate quantization errors from outlier channels. (arXiv)\n\nThe public literature around low-bit training also points toward **staging** rather than all-hard-mode-from-start. ParetoQ reports that, in its experiments, the best results come from doing most of training in higher precision and only a smaller final portion in QAT. That is not your exact setup, but it supports the same practical lesson: the hardest quantization should usually enter late, not dominate the entire run from the first step. (arXiv)\n\n### 4. 3-bit KV cache\n\nThis part is increasingly plausible, but it is mostly an **inference-side win** , not a reason your pretraining will be easier. KIVI and TurboQuant are both about reducing serving memory and improving throughput, not improving base optimization. QServe makes the same general point from another angle: efficient low-bit serving depends heavily on systems co-design, not just on math. (arXiv)\n\nSo I would not use “3-bit KV cache” as part of the justification for training stability. I would treat it as a deployment feature that you want the architecture to tolerate well. (arXiv)\n\n## What 1B with 40B tokens means\n\nFor a 1B model, 40B tokens is **40 tokens per parameter**. By older AR scaling heuristics, that is not absurdly low. Chinchilla’s headline example was 70B parameters trained on 1.4T tokens, which is about 20 tokens per parameter. So in plain AR terms, 40B tokens for 1B parameters is not obviously undertraining. (arXiv)\n\nBut that does **not** mean your setup is comfortably overprovisioned. Diffusion-language work still suggests that some diffusion families need more compute than AR to match likelihood, and scaling results show different diffusion families trade off perplexity and generation speed in nontrivial ways. So your 40B-token budget is enough for a meaningful run, but not enough to absorb many simultaneous training pathologies for free. (arXiv)\n\nMy translation of that into plain language is:\n\n  * 40B tokens is enough to train a **real** 1B model.\n  * 40B tokens is **not** enough to be careless about numerics, schedules, or ablations when you are stacking diffusion and aggressive low-bit behavior. (arXiv)\n\n\n\n## What I think will happen if you try this exactly as stated\n\nIf you turn on all difficult ingredients from the start, the most likely outcomes are:\n\n  1. **It trains, but underperforms a simpler baseline.**\nThis is the most probable result. The model may remain coherent and useful, but lose too much optimization headroom to match a simpler AR or safer MDLM baseline trained on the same budget. That is consistent with current diffusion scaling results and low-bit attention warnings. (arXiv)\n\n  2. **The loss looks mostly normal, then destabilizes late.**\nPublic BitNet issue reports and the low-precision attention literature both point to the possibility of training that appears healthy, then degrades suddenly once the model enters a more sensitive region of optimization. (GitHub)\n\n  3. **You misdiagnose engineering trouble as algorithmic failure.**\nBecause the public ecosystems here are still rough, a messy training codebase makes it harder to tell whether you hit a real modeling limit or just a bad kernel path, masking bug, or inconsistent attention implementation. Public issues on bd3lms and BitNet reinforce that the tooling is still not boring and mature. (GitHub)\n\n\n\n\n## What I would do in your case\n\n### The main recommendation\n\nI would **not** run the full intended stack from step 1.\n\nI would instead aim for this order:\n\n  1. **Get the MDLM or block-diffusion backbone stable without the hardest activation regime.**\nDiffusion is already one research variable. Make that one variable first. MDLM and block diffusion both have strong public recipes, and block diffusion itself emphasizes variance reduction and data-driven schedules. (arXiv)\n\n  2. **Use ternary weights early if that is central to the thesis.**\nThis is the most defensible low-bit choice you have. The public evidence for native ternary training is much stronger than the evidence for full early 4-bit activation training. (arXiv)\n\n  3. **Keep activations safer early, then introduce the harder q4 path later.**\nThis matches the spirit of BitNet a4.8 and the general QAT evidence from ParetoQ. (arXiv)\n\n  4. **Evaluate 3-bit KV mainly as an inference layer.**\nThat is where the literature is strongest. (arXiv)\n\n\n\n\n### Why this ordering makes sense\n\nThis ordering separates the risks:\n\n  * If the model fails before q4 activations enter, the problem is likely diffusion or ternary numerics.\n  * If it only fails after q4 enters, the culprit is probably the low-bit activation path.\n  * If pretraining succeeds but long-context inference quality drops, the problem is likely the KV compression layer.\n\n\n\nThat gives you information instead of a single ambiguous failure. The public literature strongly supports doing this kind of separation, because the failure modes are not all in the same place. (arXiv)\n\n## If your code is messy, this matters even more\n\nYou said the training code is messy and you do not want to share it. That is fine. It just changes the best strategy.\n\nWith clean code and heavy instrumentation, you can afford a more aggressive stack because you can localize failures quickly. With messy code, you want **fewer simultaneous sources of instability**. The literature does not explicitly say “messy code is bad,” but the state of the public repos and issues strongly implies that these methods are still engineering-sensitive. (GitHub)\n\nThere is also a stronger alternative route if your goal is simply “get a diffusion-style model without rewriting everything.” LLaDA’s guidelines say their backbone can be derived from an AR model by simply removing the causal mask, and DiffuLLaMA explicitly argues that training diffusion models from scratch at scale is challenging and that adapting AR models is an effective route. For a messy codebase, that is a major hint: **reduce the amount of architectural novelty you inject at once**. (GitHub)\n\n## My actual verdict\n\nHere is the plain verdict.\n\n**Would the concept model work?**\nYes, **probably** , if “work” means “can be trained into a real model and can demonstrate the intended efficiency ideas.” (arXiv)\n\n**Would I expect the first end-to-end 1B / 40B-token run, with messy code, to cleanly validate the full concept as stated?**\nNo. The most likely blocker is not ternary weights and not 3-bit KV. It is the combination of **diffusion training and low-bit attention activations** , especially if q4 is active too early or too broadly. (arXiv)\n\n**What is the highest-confidence version of your idea?**\nA staged one: ternary core first, safer activation path first, q4 introduced later and selectively, 3-bit KV treated mostly as an inference feature, and block diffusion adopted only after the simpler masked-diffusion path is behaving. (arXiv)\n\n**What is the single sentence summary?**\nYour concept is **plausible but over-coupled**. The strongest public support is for the ternary core and KV compression, while the strongest public warnings are about low-bit activations inside attention, especially in diffusion-style models. (arXiv)",
  "title": "Would this concept model work?"
}