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Multilingual Fine-Tuning Dataset

Multi-language conversations and translations

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$19 for the dataset. Training is priced separately by model, starting at $47.

What is in this dataset

898 instruction-response pairs covering multi-language conversations and translations. Each pair is a realistic question with a full written answer, in the JSONL format used for supervised fine-tuning. No synthetic filler, no duplicated rows.

Example pairs

Real rows from the dataset, shown in full.

Translate the following English sentence to Spanish: 'The quick brown fox jumps over the lazy dog.'

El zorro marrón rápido salta sobre el perro perezoso.

Explain the difference between 'savoir' and 'connaître' in French, with examples.

In French, 'savoir' means 'to know' in the sense of knowing a fact or how to do something (e.g., 'Je sais nager' – I know how to swim), while 'connaître' means 'to know' in the sense of being familiar with a person, place, or thing (e.g., 'Je connais Paris' – I know Paris). The key difference: savoir is for knowledge or skills, connaître for acquaintance or familiarity.

Provide a cultural context in German for why punctuality is important in business meetings, and give a sample phrase for arriving on time.

In German business culture, punctuality (Pünktlichkeit) is a sign of respect and professionalism. Arriving even 5 minutes late can be seen as rude. A common phrase is 'Ich bin pünktlich zur Besprechung gekommen' (I arrived on time for the meeting). Germans often plan their schedules meticulously, so being on time demonstrates reliability.

Who this is for

Language-learning and translation tools covering grammar and pronunciation.

Which models this works with

The dataset is plain JSONL, so it works with any instruction-tuned open model. On Rebiha you can train it directly on:

Training uses LoRA, or QLoRA on a 4-bit base for larger models. Your base model's weights are never modified — training produces a separate adapter.

How it works

  1. Pick this dataset and a base model.
  2. Rebiha runs the job on a managed GPU. You set up nothing.
  3. Download your model: a ready-to-run GGUF, plus developer assets (adapter, tokenizer, configs).

Fine-tuning or RAG?

Use RAG when the model needs facts that change — your prices, your documents, today's inventory. Use fine-tuning when you want consistent style and structure without prompting for it every time. This dataset teaches the shape of a good answer in this domain, not facts to look up.

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