Show HN: Fine-tune an 8B model on a 4 GB laptop GPU
Soup Fine-tune and post-train LLMs in one command. Website · Quick Start · Config · Docs · Commands · Models · Discord Soup turns the pain of LLM fine-tuning into a simple workflow. One config, one command, done. pip install "soup-cli[train]" # add [train] to fine-tune; bare `soup-cli` is the light CLI soup init --template chat soup train Why Soup?
- ▪Soup Fine-tune and post-train LLMs in one command.
- ▪Website · Quick Start · Config · Docs · Commands · Models · Discord Soup turns the pain of LLM fine-tuning into a simple workflow.
- ▪One config, one command, done. pip install "soup-cli[train]" # add [train] to fine-tune; bare `soup-cli` is the light CLI soup init --template chat soup train Why Soup?
Hacker News (Front Page) files mainly under programming. We currently carry 1,057 of its stories. Top-voted stories on Hacker News.
Story provenance
Source · retrieval · rights · ranking — open for full record
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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 | GitHub |
| Canonical URL | https://github.com/MakazhanAlpamys/Soup |
| Publication time | Tue, 04 Aug 2026 11:17:57 +0000 |
| Retrieval time | 2026-08-04T12:15:47.150Z |
| Last seen | 2026-08-04T12:15:47.150Z |
| 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 | nz1C7hLO0C1K · 1 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
Soup Fine-tune and post-train LLMs in one command. No SSH, no config hell. Website · Quick Start · Config · Docs · Commands · Models · Discord Soup turns the pain of LLM fine-tuning into a simple workflow. One config, one command, done. pip install "soup-cli[train]" # add [train] to fine-tune; bare `soup-cli` is the light CLI soup init --template chat soup train Why Soup? Training LLMs is still painful. Even experienced teams spend 30-50% of their time fighting infrastructure instead of improving models. Soup fixes that. Zero SSH. Never SSH into a broken GPU box again. One config. A simple YAML file is all you need. Auto everything. Batch size, GPU detection, quantization — handled. Works locally. Train on your own GPU with QLoRA. No cloud required.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.