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Hyperlearn Alternatives
Similar projects and alternatives to hyperlearn
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supabase
The Postgres development platform. Supabase gives you a dedicated Postgres database to build your web, mobile, and AI applications.
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ollama
Get up and running with Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models.
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marimo
A reactive notebook for Python — run reproducible experiments, query with SQL, execute as a script, deploy as an app, and version with git. Stored as pure Python. All in a modern, AI-native editor.
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llama.cpp
Discontinued LLM inference in C/C++ [Moved to: https://github.com/ggml-org/llama.cpp] (by ggerganov)
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unsloth
Unsloth Studio is a web UI for training and running open models like Gemma 4, Qwen3.6, DeepSeek, gpt-oss locally.
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hexclave
The user infrastructure platform. You choose the frontend, backend, and database. Hexclave handles everything else.
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qmd
mini cli search engine for your docs, knowledge bases, meeting notes, whatever. Tracking current sota approaches while being all local
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Econometrics-With-Python
Tutorials of econometrics featuring Python programming. This is a crash course for reviewing the most important concepts and techniques of basic econometrics, the theories are presented lightly without hustles of derivation and Python codes are straightforward.
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vllm-mlx
OpenAI and Anthropic compatible server for Apple Silicon. Run LLMs and vision-language models (Llama, Qwen-VL, LLaVA) with continuous batching, MCP tool calling, and multimodal support. Native MLX backend, 400+ tok/s. Works with Claude Code.
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notebooks
Implement, demonstrate, reproduce and extend the results of the Risk articles 'Differential Machine Learning' (2020) and 'PCA with a Difference' (2021) by Huge and Savine, and cover implementation details left out from the papers. (by differential-machine-learning)
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Basic-Statistics-With-Python
Introduction to statistics featuring Python. This series of lecture notes aim to walk you through all basic concepts of statistics, such as descriptive statistics, parameter estimations, hypothesis testing, ANOVA and etc. All codes are straightforward to understand.
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NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better hyperlearn alternative or higher similarity.
hyperlearn discussion
hyperlearn reviews and mentions
Posts with mentions or reviews of hyperlearn.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2026-04-10.
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Apple Silicon LLM Inference Optimization: The Complete Guide to Maximum Performance
Unsloth is primarily a fine-tuning tool — it makes QLoRA training 2-5x faster with 50-70% less VRAM. It does NOT run inference. For inference, use Ollama/llama.cpp/MLX.
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LLM Fine-Tuning: The Complete Guide to Customizing Language Models (2026)
LoRA is the breakthrough that democratized fine-tuning: by training only 1% of model weights, it reduces GPU/VRAM needs by 10-100x. QLoRA takes it further — quantizing to 4 bits enables fine-tuning 65B+ parameter models on a single consumer GPU with just 3GB VRAM (Unsloth).
- Ggml.ai joins Hugging Face to ensure the long-term progress of Local AI
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I optimised my vibe coding tech stack cost to $0
- LLM/AI Model - Gemini by AI Studio (aistudio.google.com) or Self-trained Unsloth Model (https://unsloth.ai) or Openrouter for testing (openrouter.ai)
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10 Open Source AI Tools Every Developer Should Know
Unsloth AI is designed to optimize large language model fine-tuning on modest hardware. It leverages efficient training algorithms to allow even GPUs with 24GB VRAM, like consumer-grade cards, to fine-tune models such as Llama 3 without massive resource demands or overheating risks.
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80% faster, 50% less memory, 0% accuracy loss Llama finetuning
I agree fully - what do you suggest then? OSS the entire code base and using AGPL3? I tried that with https://github.com/danielhanchen/hyperlearn to no avail - we couldn't even monetize it at all, so I just OSSed everything.
I listed all the research articles and methods in Hyperlearn which in the end were gobbled up by other packages.
We still have to cover life expenses and stuff sadly as a startup.
Do you have any suggestions how we could go about this? We thought maybe an actual training / inference platform, and not even OSSing any code, but we decided against this, so we OSSed some code.
Ay suggestions are welcome!
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80% faster, 50% less memory, 0% loss of accuracy Llama finetuning
Good point - the main issue is we encountered this exact issue with our old package Hyperlearn (https://github.com/danielhanchen/hyperlearn).
I OSSed all the code to the community - I'm actually an extremely open person and I love contributing to the OSS community.
The issue was the package got gobbled up by other startups and big tech companies with no credit - I didn't want any cash from it, but it stung and hurt really bad hearing other startups and companies claim it was them who made it faster, whilst it was actually my work. It hurt really bad - as an OSS person, I don't want money, but just some recognition for the work.
I also used to accept and help everyone with their writing their startup's software, but I never got paid or even any thanks - sadly I didn't expect the world to be such a hostile place.
So after a sad awakening, I decided with my brother instead of OSSing everything, we would first OSS something which is still very good - 5X faster training is already very reasonable.
I'm all open to other suggestions on how we should approach this though! There are no evil intentions - in fact I insisted we OSS EVERYTHING even the 30x faster algos, but after a level headed discussion with my brother - we still have to pay life expenses no?
If you have other ways we can go about this - I'm all ears!! We're literally making stuff up as we go along!
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[Project] BFLOAT16 on ALL hardware (>= 2009), up to 2000x faster ML algos, 50% less RAM usage for all old/new hardware - Hyperlearn Reborn.
Hello everyone!! It's been a while!! Years back I released Hyperlearn https://github.com/danielhanchen/hyperlearn. It has 1.2K Github stars, where I made tonnes of algos faster:
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Stats
Basic hyperlearn repo stats
9
2,454
3.4
over 1 year ago
unslothai/hyperlearn is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of hyperlearn is Jupyter Notebook.
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