OpenHermes
Teknium's Mistral 7B fine-tune — 2023's go-to local chat model
Overview
OpenHermes 2.5 is Teknium's instruction-tuned Mistral 7B fine-tune, trained on roughly a million mostly GPT-4-generated examples plus ~100k code instructions. Released in November 2023, it became one of the most-downloaded local chat models of its generation.
Pricing
Pricing shown for reference only. These figures reflect RECATOOLS research as of 27 Sep 2026 and may be out of date or incomplete. This is not financial or purchasing advice — always confirm the current price on the provider’s official website before making any decision.
Use cases
What you can produce with OpenHermes
- Run a 7B instruction-tuned chat model locally on modest hardware
- Use one of the most-downloaded local chat models of its era as a baseline
- Study what roughly 100,000 code instructions did: HumanEval from 43% to 50.7% pass@1, and non-code benchmarks rose too
- Fine-tune from it rather than from a base Mistral checkpoint
- Reuse the dataset recipe, which is the durable part
- Treat it as a November 2023 artefact and choose a current model for new work
OpenHermes 2.5 (Mistral-7B) by Teknium is a widely respected open fine-tune that became a go-to general-purpose 7B model, noted for solid instruction-following, conversation and a notable coding bump from its training mix. For a small self-hostable model it was a community favourite and a strong base for further tuning. It suits hobbyists, fine-tuners and developers running local assistants on modest hardware. Caveats: it is a 2023-era model and the open ecosystem has moved on — newer Nous, Qwen and Llama fine-tunes generally outperform it now — so it is more of a proven baseline than a frontier choice. It is a model on Hugging Face, not a service, so there is no first-party API. Free, open, globally usable for self-hosting.
What we found
Adding code instructions — between 7% and 14% of the dataset, on the card's own estimate — to the Hermes 2 data pushed HumanEval from 43% to 50.7% pass@1 in Glaive's benchmarking, and unexpectedly lifted the non-code benchmarks too. That happy accident is how OpenHermes 2.5 came about: Teknium was building OpenHermes-2-Coder, noticed the code data improved almost everything, and shipped it at the end of October 2023 as a general model instead.
For about six months it was the reflex recommendation on r/LocalLLaMA for anyone with 8GB of VRAM. Trained on around a million mostly GPT-4-generated examples, it followed instructions well, held multi-turn conversations via ChatML, and quantised down to run on modest hardware. TheBloke's GGUF and AWQ conversions plus an official Ollama library entry meant you could be chatting with it minutes after hearing about it, and plenty of 2023–24 RAG tutorials and agent demos still reference it.
The ecosystem moved on, though. Nous Research's own Nous-Hermes 2 Mistral 7B DPO superseded it in February 2024, Llama 3 reset the small-model bar that April, and the Hermes line has since run through Hermes 4 on Llama 3.1 405B in August 2025 to Hermes 4.3, a 36B model on ByteDance's Seed base that Nous says nearly matches Hermes 4 70B, and on to an autonomous Hermes Agent in February 2026. Nous itself graduated from scrappy fine-tuning collective to funded lab, though neither the Series A figure nor the download total that circulate appears on any Nous page we could read.
The honest 2026 read: OpenHermes 2.5 is a well-documented, freely usable baseline that still handles undemanding local chat, but Qwen, Llama 3.x and newer Hermes releases beat it at the same or smaller sizes. Run it for reproducibility or nostalgia, not performance.
The model itself is still easy to reach: it is Apache 2.0 and ungated, draws about 5,500 downloads a month, and its Ollama library entry has passed 1.2 million pulls — though neither has been touched since early 2024.
Compiled by RECATOOLS from public sources: vendor documentation and pricing, code repositories and published reporting.
Notable facts
- OpenHermes 2.5 was one of the top 3 most downloaded language models on Hugging Face in December 2023, driven by its practical utility across a wide range of tasks.
- The model's function calling capability was specifically trained on data that teaches the model to invoke external tools correctly — critical for building AI agents.
- OpenHermes was produced entirely by volunteer researchers in the Nous Research community, with no institutional funding.
Frequently asked questions
About this listing
This entry was compiled from publicly available data including OpenHermes's official website, press releases, documentation, and reputable third-party publications. RECATOOLS is not affiliated with OpenHermes unless explicitly stated.
Third-party AI tools update their pricing, features, availability, and policies frequently. Information here may be outdated by the time you read this — we make reasonable efforts to keep listings current, but cannot guarantee absolute accuracy.
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