Alternatives

Products that do what Fine-grained stylistic control of LLMs using model arithmetic does

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.

1IB
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
2LL
Learn LLMs LeetCode Style▲181
2025 · github.com
3KG
Knowledge graph of restaurants and chefs, built using LLMs▲201
Hi HN! My latest side project is knowledge graph that maps the French culinary network using data extracted from restaurant reviews from LeFooding.com. The project uses LLMs to extract structured information from unstructured text. Some technical aspects you may be interested in: - Used structured generation to reliably parse unstructured text into a consistent schema - Tested multiple models (Mistral-7B-v0.3, Llama3.2-3B, gpt4o-mini) for information extraction - Created an interactive visualization using gephi-lite and Retina (WebGL) - Built (with Claude) a simple Flask web app to clean and…
2025 · theophilecantelob.re
4HL
How LLMs Work – Interactive visual guide based on Karpathy's lecture▲245
All content is based on Andrej Karpathy's "Intro to Large Language Models" lecture (youtube.com/watch?v=7xTGNNLPyMI). I downloaded the transcript and used Claude Code to generate the entire interactive site from it — single HTML file. I find it useful to revisit this content time to time.
Apr 2026 · ynarwal.github.io
5WF
We fingerprinted 178 AI models' writing styles and similarity clusters▲78
We have a dataset of 3,095 standardized AI responses across 43 prompts. From each response, we extract a 32-dimension stylometric fingerprint (lexical richness, sentence structure, punctuation habits, formatting patterns, discourse markers). Some findings: - 9 clone clusters (>90% cosine similarity on z-normalized feature vectors) - Mistral Large 2 and Large 3 2512 score 84.8% on a composite metric combining 5 independent signals - Gemini 2.5 Flash Lite writes 78% like Claude 3 Opus. Costs 185x less - Meta has the strongest provider "house style" (37.5x distinctiveness ratio) - "Satirical…
Apr 2026 · rival.tips
6
Taylor AI▲118
Fine-tune open source LLMs in minutes
2023
7SO
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
8
2000 Large Language Models (LLM) Prompts▲106
Unlock your knowledge with 2000 LLM prompts
2023
9BR
BrowserAI – Run LLMs directly in browser using WebGPU (open source)▲85
Check out this impressive project that enables running LLMs entirely in the browser using WebGPU. Key features: - Zero token costs, no cloud infrastructure required - Complete data privacy through local processing - Simple 3-line code integration - Built on MLC and Transformer.js The benchmarks show smaller models can effectively handle many common tasks. Currently the project roadmap includes: - No-code AI pipeline builder - Browser-based RAG for document chat - Analytics/logging - Model fine-tuning interface
2025 · github.com
10
Monostate AItraining▲59
Fine-tuning, RL, and inference in one CLI
Dec 2025
11AA
An all-in-one blog for learning Large Language Models (LLMs)▲5
An all-in-one blog for learning LLM ins and outs: tokenize, attention, PE, and more Project I've been diving deep into the internals of Large Language Models (LLMs) and started documenting my findings. My blog covers topics like: Tokenization techniques (e.g., BBPE) Attention mechanism (e.g. MHA, MQA, MLA) Positional encoding and extrapolation (e.g. RoPE, NTK-aware interpolation, YaRN) Architecture details of models like QWen, LLaMA Training methods including SFT and Reinforcement Learning If you're interested in the nuts and bolts of LLMs, feel free to check it out:…
2025 · comfyai.app
12IB
I built a full mulimodal LLM by merging multiple models into one▲40
2025 · github.com
13
FinetuneDB▲52
AI fine-tuning platform to create custom LLMs
2024
14LA
LLM Alignment Template – Aligning Language Models with Human Feedback▲9
Hey Hacker News! I've been working on an open-source project called LLM Alignment Template, a comprehensive toolkit designed to help researchers, developers, and data scientists align large language models (LLMs) with human values using Reinforcement Learning from Human Feedback (RLHF). What the project does: Interactive Web Interface: Easily train models, visualize alignment metrics, and manage alignment with an accessible UI. Training with RLHF: Align models effectively to human preferences using feedback loops. Explainability: Built-in dashboards to help understand model behavior using…
2024 · github.com
15CL
Create-LLM – Train your own LLM in 60 seconds▲54
https://medium.com/@theaniketgiri/three-months-ago-i-wanted-...
Oct 2025 · github.com
16PR
PrePrompt – rewrites vague prompts before they reach the LLM▲23
Apr 2026 · preprompt.org
17TA
Throw a Whole Book into an LLM to Extract Characters and Relationships▲9
Let's try a small experiment with LLMs that have a large context length: feed an entire book into the context window and ask it to generate a list of characters, their relationships, and physical descriptions—data that can later be used for image generation. In this repository, you can find two tools: a script that extracts data from book text using an LLM (Gemini or OpenRouter API) and an HTML/JS (D3) visualization of the character graph. An external text-to-image model can be used to generate character illustrations (a Google Colab example is provided). Explore the visualizations,…
2025 · github.com
18CL
Create LLM graders and run evals in JavaScript with one file▲28
Hi HN! Run it: OPENROUTER_API_KEY="sk" npx bff-eval --demo We built a tool to help people take LLM outputs and easily grade them / eval them to know how good an assistant response is. We've built a number of LLM apps, and while we could ship decent tech demos, we were disappointed with how they'd perform over time. We worked with a few companies who had the same problem, and found out scientifically building prompts and evals is far from a solved problem... writing these things feels more like directing a play than coding. Inspired by Anthropic's constitutional ai concepts, and amazing…
2025 · github.com
19LI
LanguageModels.io▲9
2018 · languagemodels.io
20AG
Aiaiai.guide: Plain-English mental model for LLM apps, tools and agents▲7
I’ve been building LLM tooling for a small VC fund and found myself explaining the same mental model over and over to non-technical people around me: how a stateless LLM becomes a chatbot, how tool use works, what an agent is mechanically, and why context windows shape all of it. I never found a guide that covered that full chain at the level I wanted, so I wrote one. It’s nine short chapters, each building on the last. Deliberately simplified: the goal is a useful mental model, not a textbook. Feedback, corrections, and contributions welcome: github.com/ymyke/aiaiai
Apr 2026 · aiaiai.guide
21SD
SteadyText: Deterministic LLMs: Same input → same output, every time▲6
Hey HN! After spending way too many nights debugging flaky AI tests, I built SteadyText. It's a simple python library for deterministic llm generations and embeddings. We use it in production for: - Testing our AI features (zero flakes in 3 months) - CLI tools that need consistent outputs - Reproducible documentation examples It's not for creative tasks - this is specifically for when you need AI to be boring and predictable. Think of it as the opposite of ChatGPT. The coolest part? It includes a Postgres extension. You can now do: SELECT steadytext_generate('explain this query: ...'); And…
2025 · steadytext.julep.ai
22ZA
ZigFormer – An LLM implemented in pure Zig▲22
Hi everyone, I've made an early version of ZigFormer, a small LLM implemented in Zig with no dependencies on external ML frameworks like PyTorch or JAX. ZigFormer is modelled after a textbook LLM (like GPT-2 from OpenAI) and can be used as a Zig library as well as a standalone application to train a model and chat with it. This was mainly an educational project. I'm sharing it here in case others find it interesting or useful. Link to the project: https://github.com/CogitatorTech/zigformer
Nov 2025
23AE
An educational Local Qwen3 LLM Inference project written in Rust▲15
2025 · github.com
24HP
Horizon – Programmatic Prompt Generation and LLM Configurations▲7
Hi HN. I heard you like dev tools and AI, so we wanted to share our project that we’ve been working on. We’re working on Horizon [1] - a higher level abstraction for LLMs so that developers can spend less time trying to grapple with LLMs to make them work and more time with users. This is the starting feature set which takes an auto-ML approach to identify the optimal LLM model, hyperparameters, and prompt - instead of just giving you the tooling to figure it out yourself. You can read more about it in our documentations. Our view is that as LLMs become increasingly commoditized and prompts…
2023 · gethorizon.ai

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