Model Inference
Make LLMs Hallucinate Less
Model Inference launched on February 9, 2024 with 7 votes, #1,918 of 3,960 launches that month. The site was still online in September 2026, 2 years on.
What it does
Interact programmatically with your fine tuned model via API, to make accurate predictions on your data. More Accurate, Domain Specific and Reliable Answers to Questions / Categories Predicted in the Financial Domain
Anote helps AI researchers and developers create labeled datasets and evaluate AI results so they can improve LLMs and build reliable AI agents for real-world use.
Does a similar job
all alternatives →- ABAutoThink – Boosts local LLM performance with adaptive reasoning2025 · ▲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…
Open-source model and scorecard for measuring hallucinations in LLMs2023 · vectara.com · ▲65Hi 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…
Sup AIApr 2026 · sup.ai · ▲103Hi HN. I'm Ken, a 20-year-old Stanford CS student. I built Sup AI. I started working on this because no single AI model is right all the time, but their errors don’t strongly correlate. In other words, models often make unique mistakes relative to other models. So I run multiple models in parallel and synthesize the outputs by weighting segments based on confidence. Low entropy in the output token probability distributions correlates with accuracy. High entropy is often where hallucinations begin. My dad Scott (AI Research Scientist at TRI) is my research partner on this. He sends me papers…
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