Nicheloom

Market intelligence for builders — see what's gaining traction before it's crowded.

Selora

local model for Home Assistant

Details

External ID
48576208
Source
HN
Company
—
Product
Selora
Website domain
github.com
Launched
June 17, 2026
Cohort
—
Upvotes
7
Upvotes percentile
0.42008196721311475
Tags
—
Fetched at
Sept. 7, 2026, 9:26 p.m.
Updated at
Sept. 7, 2026, 9:26 p.m.

Description

Selora AI Local is an open-source, Qwen-based model for Home Assistant.Specs: Qwen3 1.7B base model (Q6 quantized~1.6GB) Four Home Assistant-specific LoRA adapters: - Answers - Clarifications - Automations - Commands ~3.5 GB total download size Runs locally via llama.cppWe chose a Qwen-based architecture because of a paper on Arxiv (link below) which applied a Qwen based model for local LLM configuration, and showed promising results. We took it a step further in application by training LoRA adapters specialized in Home Assistant configuration.This alpha release ships our base model with four specialist LoRA adapters preloaded for faster response: answers, clarifications, automations, and commands. The Q6 quantized base model is 1.6GB and the adapters are less than 100MB running either on self-hosted llama.cpp, or on Selora Hub devices, where everything is preconfigured and plug-and-play for you.We started working on this because the existing options for local LLMs in Home Assistant lack knowledge to run anything useful, so users opt for very large LLMs, typically a cloud model that’s too expensive and not optimized for the kind of high-frequency, always-on smart home use we care about. We think there's a need for open-source models that are small and specialized enough to run on the kind of hardware people actually have at home.Would love any feedback, questions, or ideas. Thanks for checking it out!Hugging Face: https://huggingface.co/selorahomes/Selora-AI Arxiv: https://arxiv.org/abs/2502.12923 More details: https://selorahomes.com/selora-ai/

Enrichment

Theme
lightweight and on-device AI runtimes
Vertical
Horizontal
Function
Model & infra
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
local model for home assistant
Manually corrected
False

Could you build this?

No Developing custom domain-specific LoRA adapters across multiple agent tasks and packaging them with quantized base models requires deep ML fine-tuning expertise and domain datasets.

What it would actually take: The project requires curating extensive Home Assistant schema datasets, command corpora, and device interaction topologies, followed by fine-tuning multiple distinct LoRA adapters on a Qwen base model using PyTorch/Unsloth. The deployment stack relies on llama.cpp integration inside Home Assistant OS or Docker with strict latency constraints. Producing accurate function calling without hallucinations requires experienced ML fine-tuning engineering and deep knowledge of Home Assistant's internals.

Discussion

4 comments analyzed.

Competitors mentioned: Qwen8B

Concerns raised: No benchmark data or performance comparisons provided, Unclear pricing model for local version despite claiming it's free, Confusion between cloud and local model pricing/credits

Feature requests: Performance benchmarks against other models, Better documentation clarifying cloud vs local pricing

Competitors

Other products that read as similar to this one — 36 launches clear the similarity bar, closest 8 shown.

Attention rank: #25 of 37 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 209 days after the earliest competitor.

Other launches for this product

Same idea, different domain

Nobody's really built a model & infra tool for Fintech yet.