Alternatives
Products that do what I built a full mulimodal LLM by merging multiple models into one does
- 1IB
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
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- 6FG
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
- 7TV
May 2026 · github.com
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- 10AT
I’ve been working on AnyModal, a framework for integrating different data types (like images and audio) with LLMs. Existing tools felt too limited or task-specific, so I wanted something more flexible. AnyModal makes it easy to combine modalities with minimal setup—whether it’s LaTeX OCR, image captioning, or chest X-ray interpretation. You can plug in models like ViT for image inputs, project them into a token space for your LLM, and handle tasks like visual question answering or audio captioning. It’s still a work in progress, so feedback or contributions would be great. GitHub:…
2024 · github.com
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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
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I trained the 65b model on my texts so I can talk to myself. It's pretty useless as an assistant, and will only do stuff you convince it to, but I guess it's technically uncensored? I'll leave it up for a bit if you want to chat with it. I posted this to Reddit and had several hundred people talking to it. Salient points from that discussion: LLAMA 1 65b Rank 128 5 epochs Batch size 1, 256 cutoff Trained in the Oobabooga suite using bitsandbytes 4-bit quantization for the lora Loss around 1.5 seems to give the most coherent results Trained on raw text dumps that is then parsed by a crappy…
2023 · airic.serveo.net
- 14LI
2018 · languagemodels.io
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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
- 16UP
Jan 2026 · github.com
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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
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Aug 2026 · github.com
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Hi HN, Today I'd like to present the results of my weekend project of the last year or so. Given there are many posts on HN about LLMs and Prolog, I thought that this would be of interest. DeepClause is my own (possibly misguided :-) attempt at combining LLMs with Logic Programming, ultimately hoping to establish a foundation for building more reliable agents, that produce reproducible and fully traceable result. At the heart of DeepClause is a DSL called "DeepClause Meta Language" (DML) which can be used to encode agent behaviors as executable logic programs. DML is executed by a…
Nov 2025 · github.com
- 20LO
Oct 2025 · e-mm1.github.io
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I feel like LLMs can help me understand anything. However, after I get a summary, I can't dive in to parts that I find interesting; can't refer to original source easily and can't control context with chatbots. This is an attempt to solve for a complete knowledge consumption experience with AI . Please give me feedback!
Oct 2025 · kerns.ai
- 22LM
2024 · gpavanb1.github.io
- 23LC
2023 · github.com
- 24LA
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
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