Qwen2.5
Fewer than 5 people recommend it so far, so there's no written verdict. Here is what commenters said.
2recommend
1warn against
Jan 14, 2026first mention
Sep 26, 2026latest mention
Why people recommend it
“I use smaller models (qwen2.5:1.5b) with keyboard shortcuts and speech to text to ask for man page entries”
“I chose the 7B model because I wanted something that could run affordably at scale while still being capable enough for nuanced generation.”
Caveats and dissent
“Adding a 7B generative LLM reranker (Qwen2.5-7B-Instruct) downstream degraded top-1 accuracy”
Often compared with
Who recommends it in their own words
Threads where it came up
- Ask HN: When is fine-tuning a small LLM worth it? 1 mention
- Show HN: Fine-tuned 110M encoder beat 7B LLMs and hybrid search for NIST mapping 1 mention
- Show HN: OpenLight – Lightweight Telegram AI Agent for Raspberry Pi 1 mention
- Eight More Months of Agents 1 mention
- Show HN: Fine-tuned Qwen2.5-7B on 100 films for probabilistic story graphs 1 mention
- Show HN: RWS – Local-first task orchestrator using Qwen 2.5 on consumer hardware 1 mention
Also written as Qwen2.5-7B-Instruct, Qwen 2.5. Data: qwen2-5.json. Quotes are verbatim; the verdict and theme labels are written by a model from these comments, and every clause links to its sources.
All comments 7
“I used qwen2.5 for the mobile use”
“Adding a 7B generative LLM reranker (Qwen2.5-7B-Instruct) downstream degraded top-1 accuracy”
“currently using qwen2.5:0.5b”
“I use smaller models (qwen2.5:1.5b) with keyboard shortcuts and speech to text to ask for man page entries”
“I chose the 7B model because I wanted something that could run affordably at scale while still being capable enough for nuanced generation.”
“It uses Qwen 2.5 and handles urgency detection and routing without any API calls.”
“fine-tuned Qwen2.5-7B-Instruct with a LoRA optimized for probabilistic story branching”