AI · alternatives · 2026
24 alternatives to WaveletLM – wavelet-based, attention-free model with O(n log n) scaling
WaveletLM is a wavelet-based, attention-free architecture that replaces self-attention with learned lifting wavelet decomposition, a Fast Walsh-Hadamard Transform, per-scale gated spectral mixing with SwiGLU activation, an inverse FWHT, and wavelet reconstruction. Combined with expanded MLPs and sparse product-key memory, this yields a model with O(n log n) scaling in sequence length. With 23.8 PPL on WikiText-103, WaveletLM beats both GPT-2 Medium, which was trained on 80× more data, and Transformer-XL Standard, which uses recurrence to extend its effective context. It is undertrained and…
Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.
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Built a ~9M param LLM from scratch to understand how they actually work. Vanilla transformer, 60K synthetic conversations, ~130 lines of PyTorch. Trains in 5 min on a free Colab T4. The fish thinks the meaning of life is food. Fork it and swap the personality for your own character.
Apr 2026 · github.com · its alternatives →
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Ferret▲193Refer and ground anything anywhere at any granularity
2024 · arxiv.org · its alternatives →
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TurboQuant▲295New LLM compression algorithm by Google
Mar 2026 · research.google · its alternatives →
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Dream 7B▲191Powerful Open Diffusion LLM, Beyond Autoregressive
2025 · hkunlp.github.io · its alternatives →
- 6
Hi all! This morning, we released a new Apache 2.0 licensed model on HuggingFace for detecting hallucinations in retrieval augmented generation (RAG) systems. What we've found is that even when given a "simple" instruction like "summarize the following news article," every LLM that's available hallucinates to some extent, making up details that never existed in the source article -- and some of them quite a bit. As a RAG provider and proponents of ethical AI, we want to see LLMs get better at this. We've published an open source model, a blog more thoroughly describing our methodology (and…
2023 · vectara.com · its alternatives →
- 7
I previously posted this a month and a half ago: https://news.ycombinator.com/item?id=45876742 I have rewritten it to be much more simple, from 951 lines of code to just 364, with no loss in core functionality or generation quality. I also added a tiny GPT implementation as a comparison (inspired by Andrej Karpathy's code). The two model implementations are ~80% identical, with the core differences being the generate and get_batch functions. The model architecture, training loop, etc, differ in only 19 lines of code. Trained weights are included, so you can just clone and run…
Nov 2025 · github.com · its alternatives →
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It's our new text-to-image model: a 9.3B single-stream diffusion transformer trained entirely from scratch. We focused heavily on controllability through structured JSON prompts, with strong text rendering, spatial awareness through bounding box guidance, and color palette control. It has the best text rendering of any open-weight model we've tested so far, and the NF4 quantized checkpoint runs on a single 24GB GPU. For more technical details and examples see our blog post: https://ideogram.ai/blog/ideogram-4.0/ We will be happy to answer any questions :)
Jun 2026 · github.com · its alternatives →
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Fine-tuning, RL, and inference in one CLI
Dec 2025 · github.com · its alternatives →
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We’ve recently open-sourced Model2vec, a method to distill sentence transformers into static embeddings that outperform all previous approaches by a large margin on MTEB. Our new models set a new state-of-the-art for static embeddings. Main features: - Our best model (potion-base-8M) has only 8M parameters, which is ~30mb on disk - Inference is ~500x faster than the distilled base model (bge-base), on a CPU - New models can be distilled in 30 seconds on a CPU without requiring a dataset - just a vocabulary - Numpy-only inference: The packaged can be install the package with minimal…
2024 · github.com · its alternatives →
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FastVLM▲14Lightweight open-source vision models for Apple devices
2025 · github.com · its alternatives →
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A simple re-implementation of Google's sparsehash as a learning excercise. - qpfiffer/Simple-Sparsehash
2015 · github.com · its alternatives →
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Lately a friend of mine has been working on a personal project and I wanted to share it. The goal of Diffulab is to provide a flexible and modular framework for training diffusion models from scratch. The project is still in its early stages, and he is actively working on adding new features and improvements.
2025 · github.com · its alternatives →
- 15
The traditional pipeline for unstructured data extraction typically follows these steps: 1. Image → OCR Model (e.g., Google Vision) → Layout Model (e.g. Surya) → LLM → Final Answer However, this can be streamlined using a Vision-Language Model (VLM): 2. Image → VLM → Final Answer Recently VLMs have improved a lot for OCR and document understanding tasks, specifically the Qwen-2.5-VL series. We can run the Qwen-2.5-VL-7B-AWQ model locally with just 16GB VRAM, and perform end-to-end information extraction (fields and table extraction) without any external models. Hallucination with VLMs One…
2025 · github.com · its alternatives →
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Build LLMs that costs 85% less on usage.
2024 · hyperllm.org · its alternatives →
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Minimal, readable LLM post-training experiments on one 8GB GPU. Measures forgetting, seed variance, and RL emergence. - pochenai/nano-llm-posttraining
Aug 2026 · github.com · its alternatives →
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High-Performance Hyperbolic Sparse Autoencoders for Mechanistic Interpretability - vishal-dehurdle/hypersae
Aug 2026 · github.com · its alternatives →
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A lightweight implementation of Walklets from "Don't Walk Skip! Online Learning of Multi-scale Network Embeddings" (ASONAM 2017). - benedekrozemberczki/walklets
2019 · github.com · its alternatives →
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A PyTorch implementation of "Graph Wavelet Neural Network" (ICLR 2019) - benedekrozemberczki/GraphWaveletNeuralNetwork
2019 · github.com · its alternatives →
- 22
VibeSolve is an open-source tool that turns a plain-English description of an optimisation problem into Timefold code. Mathematical optimisation is a branch of mathematics and computer science that searches for the minimum/maximum of objective functions, and has applications in transport, logistics, scheduling etc. We are exploring where LLMs can add value in optimisation algorithm development, and where they get in the way. Right now, it works well for rapid prototyping. It does not create production-ready code and requires technical skills to use. It is noticeably better at creating…
Jun 2026 · vibesolve.ai · its alternatives →
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An implementation of "MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing" (ICML 2019). - benedekrozemberczki/MixHop-and-N-GCN
2019 · github.com · its alternatives →
- 24LT
I’m excited to share a project I’ve been working on for over a year, which I believe will fundamentally change our approach to language models. We’ve designed a new architecture, which replaces the hidden state of an RNN with a machine learning model. This model compresses context through actual gradient descent on input tokens. We call our method “Test-Time-Training layers.” TTT layers directly replace attention, and unlock linear complexity architectures with expressive memory, allowing us to train LLMs with millions (someday billions) of tokens in context. Our instantiations, TTT-Linear…
2024 · its alternatives →
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Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →