Ask HN: Local model experiences with 'high-reasoning distill' finetunes
The article discusses experiences with various finetunes on small models, particularly focusing on 'Opus-Reasoning' finetunes. The author notes that while these models may perform better on benchmarks, they often produce messy and buggy code in practical applications. The piece invites others to share their experiences and preferences regarding different finetunes.
- ▪The author has primarily worked with 'Opus-Reasoning' finetunes on qwen models.
- ▪Despite better benchmark performance, the models tend to produce overconfident and buggy code.
- ▪The author encourages others to share their experiences with different finetunes.
2 outlets in our directory ran this story, first to last over 26 hours. All of the coverage we found sits in one bucket: centre. That one-sidedness is itself worth noticing.
- ▪ Model is currently experiencing high demand — Maybe-Ray
Hacker News (Newest) files mainly under programming. We currently carry 5,306 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Ycombinator |
| Canonical URL | https://news.ycombinator.com/item?id=48266001 |
| Publication time | Mon, 25 May 2026 12:20:31 +0000 |
| Retrieval time | 2026-05-25T12:37:36.725Z |
| Last seen | 2026-05-25T12:37:36.725Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | IH55JXUsqxD7 · 2 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
What are your experiences with all the different variations of finetunes on small models (<40B) with those popular datasets? My personal experience is mostly with the 'Opus-Reasoning' ones on qwen models, and aside from the output being subjectively better looking (ascii charts and all), in actual coding performance every one I've tried tends to become a lot more overconfident, writing more messy and buggy code and tries to gaslight me that the task I give it is impossible when it cannot achieve it.I have seen them perform better on public benchmarks in some cases, which shouldn't be ignored completely, but that doesn't seem to translate to better output on real work in my limited testing.What are your observations? Any specific ones that you lean towards, or have had good experiences…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Ycombinator.