Alternatives

Products that do what A 150M model that extracts verbatim evidence spans for RAG, no LLM call does

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
2AB
AutoThink – Boosts local LLM performance with adaptive reasoning▲397
I built AutoThink, a technique that makes local LLMs reason more efficiently by adaptively allocating computational resources based on query complexity. The core idea: instead of giving every query the same "thinking time," classify queries as HIGH or LOW complexity and allocate thinking tokens accordingly. Complex reasoning gets 70-90% of tokens, simple queries get 20-40%. I also implemented steering vectors derived from Pivotal Token Search (originally from Microsoft's Phi-4 paper) that guide the model's reasoning patterns during generation. These vectors encourage behaviors like numerical…
2025
3OS
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
4EL
Explore large language models with 512MB of RAM▲138
2023 · github.com
5
Dolly▲113
Democratizing the magic of ChatGPT with open models
2023
6PP
Plait.py – a fake data modeler▲85
2018 · github.com
7FG
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
8LD
LLM Deceptiveness and Gullibility Benchmark▲7
2024 · github.com
9C0
Cherry2.0 – text classification without ML knowledge needed▲71
2019 · github.com
10TH
The Hungarian maximum likelihood trick▲10
2018 · louisabraham.github.io
11LP
Landmark Papers in Machine Learning▲32
2020 · github.com
12TB
The Bare Message Encoding▲36
2020 · drewdevault.com
13VF
Viveka: filter LLM output against a Lean-verified Advaita Vedanta model▲7
Jun 2026 · github.com
14MN
ML News – Like HN, but for Machine Learning▲38
2020 · mln.dev
15SV
Structural Verification for LLMs: Why Best-of-N Isn't Enough▲5
A lightweight, no-retraining verification layer that rejects smooth hallucinations by measuring structural tension instead of probability.
Dec 2025 · github.com
161M
17MB model beats human experts at pronunciation scoring▲13
Feb 2026 · huggingface.co
17ML
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
18VA
Verbatim – a simple pastebin▲14
2016 · verbatim.link
19AE
An educational Local Qwen3 LLM Inference project written in Rust▲15
2025 · github.com
20LL
LettuceDetect – Lightweight hallucination detector for RAG pipelines▲10
Hallucinations are still a major blocker for deploying reliable retrieval-augmented generation (RAG) systems, especially in complex domains like medical or legal. Most existing hallucination detectors rely on full LLM inference (expensive, slow), or struggle with long-context inputs. I built LettuceDetect — an open-source, encoder-only framework that detects hallucinated spans in LLM-generated answers based on the retrieved context. No LLMs needed, and it much more efficiently. Highlights: - Token-level hallucination detection (unsupported spans flagged based on retrieved evidence) - Built…
2025 · github.com
21AN
A New 34B Open Source LLM, Astonishing 78 Score in MMLU (GPT-4 MMLU:83)▲21
2023 · github.com
22DP
Detect patterns and quick insights without an expert or sheet▲5
2022 · startupschool.org
23IS
Improving Synthetic Data Distributions with Conditional Sampling▲17
2022 · tonic.ai
24IT
I trained a 65B LLM on my texts to talk to myself (details inside)▲13
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

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