Life & fun · alternatives · 2026

24 alternatives to ESM C, setting a new state of the art for protein language models

Hey HN! We're releasing ESM C, a series of protein language models that set a new state of the art. By training on metagenomics and fine-tuning on UniRef, we're able to match ESM2 650M with ESM C 300M, and ESM C 6B has incredible unsupervised contact precision performance. These models are meant to be used as base models and representation learning models for protein sequences, and we'll release a preprint describing exact details soon.

ESM C is a series of protein language models trained on metagenomics data and fine-tuned on UniRef sequences. Designed for researchers working with protein sequences, these models serve as base models for… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. ESM C, setting a new state of the art for protein language models launched in 2024; newer entries below may have overtaken it.

1
I built a tiny LLM to demystify how language models work▲915
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 →
2
An open-source implementation of AlphaFold3▲314
Hi HN - we’re the founders of Ligo Biosciences and are excited to share an open-source implementation of AlphaFold3, the frontier model for protein structure prediction. Google DeepMind and their new startup Isomorphic Labs, are expanding into drug discovery. They developed AlphaFold3 as their model to accelerate drug discovery and create demand from big pharma. They already signed Novartis and Eli Lilly for $3 billion - Google’s becoming a pharma company! (https://www.isomorphiclabs.com/articles/isomorphic-labs-kick...) AlphaFold3 is a biomolecular structure prediction…
2024 · github.com · its alternatives →
3
Timber – Ollama for classical ML models, 336x faster than Python▲207
Ollama for classical ML models. AOT compiler that turns XGBoost, LightGBM, scikit-learn, CatBoost & ONNX models into native C99 inference code. One command to load, one command to serve. 336x faster than Python inference. - kossisoroyce/timber
Mar 2026 · github.com · its alternatives →
4
Model2vec – Lightning-fast Static Embeddings for RAG/Semantic Search▲28
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 →
5
Port of OpenAI's Whisper model in C/C++▲399
Hi HN, OpenAI recently released a model for automatic speech recognition called Whisper [0]. I decided to reimplement the inference of the model from scratch using C/C++. To achieve this I implemented a minimalistic tensor library in C and ported the high-level architecture of the model in C++. The entire code is less than 8000 lines of code and is contained in just 2 source files without any third-party dependencies. The Github project is here: https://github.com/ggerganov/whisper.cpp With this implementation I can very easily build and run the model - “make…
2022 · github.com · its alternatives →
6
Model2vec-Rs – Fast Static Text Embeddings in Rust▲60
Hey HN! We’ve just open-sourced model2vec-rs, a Rust crate for loading and running Model2Vec static embedding models with zero Python dependency. This allows you to embed text at (very) high throughput; for example, in a Rust-based microservice or CLI tool. This can be used for semantic search, retrieval, RAG, or any other text embedding usecase. Main Features: - Rust-native inference: Load any Model2Vec model from Hugging Face or your local path with StaticModel::from_pretrained(...). - Tiny footprint: The crate itself is only ~1.7 mb, with embedding models between 7 and 30 mb. Performance:…
2025 · github.com · its alternatives →
7
Wordllama – Things you can do with the token embeddings of an LLM▲370
After working with LLMs for long enough, I found myself wanting a lightweight utility for doing various small tasks to prepare inputs, locate information and create evaluators. This library is two things: a very simple model and utilities that inference it (eg. fuzzy deduplication). The target platform is CPU, and it’s intended to be light, fast and pip installable — a library that lowers the barrier to working with strings semantically. You don’t need to install pytorch to use it, or any deep learning runtimes. How can this be accomplished? The model is simply token embeddings that are…
2024 · github.com · its alternatives →
8
Explore large language models with 512MB of RAM▲138
Explore large language models in 512MB of RAM. Contribute to jncraton/languagemodels development by creating an account on GitHub.
2023 · github.com · its alternatives →
9
AlphaFold 3▲161
Predicting the structure & interactions of life’s molecules
2024 · blog.google · its alternatives →
10
Tiny-vLLM – high performance LLM inference engine in C++ and CUDA▲205
Build your own high performance LLM inference engine in C++ and CUDA - a smaller version of vLLM - jmaczan/tiny-vllm
May 2026 · github.com · its alternatives →
11IB
In-Browser Graph RAG with Kuzu-WASM and WebLLM▲158
We show the potential of modern, embedded graph databases in the browser by demonstrating a fully in-browser chatbot that can perform Graph RAG using Kuzu (the graph database we're building) and WebLLM, a popular in-browser inference engine for LLMs. The post retrieves from the graph via a Text-to-Cypher pipeline that translates a user question into a Cypher query, and the LLM uses the retrieved results to synthesize a response. As LLMs get better, and WebGPU and Wasm64 become more widely adopted, we expect to be able to do more and more in the browser in combination with LLMs, so a lot of…
2025 · blog.kuzudb.com · its alternatives →
12
Omnilingual ASR▲132
Advancing automatic speech recognition for 1,600+ languages
Nov 2025 · ai.meta.com · its alternatives →
13
CeLLama – Single cell annotation with local LLMs▲132
A simple R package which helps with annotation of single cell experiments such as single cell RNA-seq. With up and down regulated genes per cell cluster, the local LLM guesses the cell type annotation and creates an overall extensive report.
2024 · github.com · its alternatives →
14
LLM Explorer▲152
Find your best LLM for a local inference
2023 · its alternatives →
15WA
Web app for DNA sequence manipulation and PCR Primer design▲40
Online DNA and Protein Sequence Tools, Alignments, Reverse complement, Translation, Sequence annotation, design Primers
2017 · genewarrior.com · its alternatives →
16
Fine-grained stylistic control of LLMs using model arithmetic▲85
We developed a new framework that enables flexible control of generated text in language models. By combining several models and/or system prompts in one mathematical formula, it lets you tweak your style and combine model outputs with ease. A handy tool for those working with LLMs, looking for more fine-grained control of stylistic output. More details in our paper: https://arxiv.org/abs/2311.14479. Feedback and potential applications are welcome.
2023 · github.com · its alternatives →
17
LLM, a Rust Crate/CLI for CPU Inference of LLMs (LLaMA, GPT-NeoX, etc.)▲45
G'day, HN! I'm one of the maintainers of `llm`. I've been working alongside a trusty group of contributors to bring this project to life, and we're now at a point where we're ready to share it with the world. Large language models (LLMs) are taking the computing world by storm due to their emergent abilities that allow them to perform a wide variety of tasks, including translation, summarization, code generation, and even some degree of reasoning. However, the ecosystem around LLMs is still in its infancy, and it can be difficult to get started with these models. `llm` is a one-stop shop for…
2023 · github.com · its alternatives →
18
Kalosm an embeddable framework for pre-trained models in Rust▲66
Hi everyone, I'm happy to announce the release of Kalosm! [Kalosm](https://floneum.com/kalosm/) is a framework for embedded AI in rust. ## What is Kalosm? Kalosm provides a simple interface for pre-trained language, audio, and image models models. To make it easy to use with these models in your application, Kalosm includes a set of integrations other systems like your database or documents. ```rust use kalosm::language::*; #[tokio::main] async fn main() { let mut model = Llama::new_chat(); let mut chat = Chat::builder(&mut model) .with_system_prompt("The assistant will…
2024 · floneum.com · its alternatives →
19WD
Web demo of 13B Alpaca-LLaMA trained on improved Stanford dataset▲42
2023 · lama.nbnl.uk · its alternatives →
20
A general-purpose encoder-decoder framework for Tensorflow▲54
A general-purpose encoder-decoder framework for Tensorflow - google/seq2seq
2017 · github.com · its alternatives →
21
NanoEuler – GPT-2 scale model in pure C/CUDA from scratch▲55
Hi everyone, I started working on nanoeuler after the ban of anthropic's fable because my ambition and dream is to work in the AI field in anthropic. The two interesting reasons that led me to create nanoeuler were (1) interfacing with llm does not mean understanding how they are composed and (2), working on llm with a very low-level layer to understand the correlation between parameters and data and growth of the model and how the GPU works and how some layers can be optimized. So I started working on it with a research aspect by making nanoeuler grow more and more but doing one step after…
Jun 2026 · github.com · its alternatives →
22
Open-source model and scorecard for measuring hallucinations in LLMs▲65
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 →
23
Smolmodels – open-source tool to build ML models using natural language▲37
Hi HN - Marcello and Vaibhav here. We built smolmodels to experiment with using LLMs for ML development. It's a fully open-source library that generates complete model training and inference code from natural language descriptions. It combines graph search with LLM code generation to find a model that gives as good predictions as possible. The core idea is that LLMs are overkill for a lot of predictive tasks. Smolmodels automates the trial-and-error process of finding the right model architecture and training approach, letting you build small, specialised models. You can either provide your…
2025 · github.com · its alternatives →
24
Improve Text-to-SQL Accuracy with LLM▲17
A repository that contains models, datasets, and fine-tuning techniques for DB-GPT, with the purpose of enhancing model performance in Text-to-SQL - eosphoros-ai/DB-GPT-Hub
2023 · github.com · its alternatives →

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