AI - Artificial intelligence

• Clone any voice with just a 3-second audio sample
• Fine-tune parameters (temperature, top-k, top-p) with quality presets
• Generate complete podcasts from just a topic – AI writes the script, assigns voices, and synthesizes everything
• 10 languages supported (Korean, English, Chinese, Japanese, etc.

Currently uses gpt5.2 for script generation, but the architecture is modular – you can swap in any local LLM (Qwen, Llama, etc.) if you want fully local.

cross-posted from: https://lemmy.bestiver.se/post/891009

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If you’ve ever trained or fine-tuned an LLM, you’ve likely hit a wall at the very last step: the Cross-Entropy Loss.

The culprit is the logit bottleneck. To predict the next token, we project a hidden state into a massive vocabulary space. For Llama 3 (128,256 tokens), the weight matrix alone is over 525 million parameters. While that’s only ~1GB in bfloat16, the intermediate logit tensor is the real issue. For large batches, it can easily exceed 80GB of VRAM just to compute a single scalar loss.

Optimising this layer is how libraries like Unsloth and Liger-Kernel achieve such massive memory reductions. In this article, we’ll build a fused Linear + Cross Entropy kernel from scratch in Triton. We will derive the math and implement a tiled forward and backward pass that slashes peak memory usage by 84%.

The paper argues that we have been wasting a lot of expensive GPU cycles by forcing transformers to relearn static things like names or common phrases through deep computation. Standard models do not have a way to just look something up so they end up simulating memory by passing tokens through layer after layer of feed forward networks. DeepSeek introduced a module called Engram which adds a dedicated lookup step for local N-gram patterns. It acts like a new way to scale a model that is separate from the usual compute heavy Mixture of Experts approach.

The architecture uses multi head hashing to grab static embeddings for specific token sequences which are then filtered through a context aware gate to make sure they actually fit the current situation. They found a U shaped scaling law where the best performance happens when you split your parameter budget between neural computation and this static memory. By letting the memory handle the simple local associations the model can effectively act like it is deeper because the early layers are not bogged down with basic reconstruction.

One of the best bits is how they handle hardware constraints by offloading the massive lookup tables to host RAM. Since these lookups are deterministic based on the input tokens the system can prefetch the data from the CPU memory before the GPU even needs it. This means you can scale to tens of billions of extra parameters with almost zero impact on speed since the retrieval happens while the previous layers are still calculating.

The benchmarks show that this pays off across the board especially in long context tasks where the model needs its attention focused on global details rather than local phrases. It turns out that even in math and coding the model gets a boost because it is no longer wasting its internal reasoning depth on things that should just be in a lookup table. Moving forward this kind of conditional memory could be a standard part of sparse models because it bypasses the physical memory limits of current hardware.

cross-posted from: https://lemmy.bestiver.se/post/866278

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Crossposted from https://fedia.io/m/pcmasterrace@lemmit.online/t/3295923

This is an automated archive made by the Lemmit Bot.

The original was posted on /r/pcmasterrace by /u/Department_Legal on 2026-01-15 15:24:12+00:00.

This paper is one of the more interesting takes on context extension I have seen in a while because it challenges the assumption that we need explicit positional encodings during inference. The authors make a case that embeddings like RoPE act more like scaffolding during construction rather than a permanent load bearing wall. The idea is that these embeddings are crucial for getting the model to converge and learn language structure initially, but they eventually turn into a hard constraint that prevents the model from generalizing to sequence lengths it has never seen before.

The methodology is surprisingly straightforward since they just take a pretrained model and completely drop the positional embeddings before running a very quick recalibration phase. This process essentially converts the architecture into a NoPE or No Positional Embedding model where the attention mechanism has to rely on the latent positioning it learned implicitly. It turns out that once you remove the explicit constraints of RoPE the model can extrapolate to context windows significantly longer than its training data without the perplexity explosions we usually see.

It is pretty wild to see this outperform techniques like YaRN on benchmarks like Needle In A Haystack while using a fraction of the compute. I think this suggests that Transformers are much better at understanding relative positions from semantic cues than we give them credit for. If this holds up it means we might be wasting a lot of resources trying to engineer complex interpolation methods when the answer was just to take the training wheels off once the model knows how to ride.