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---
name: training-finetuning
description: "Route supervised and contrastive fine-tuning for CoSENT,
Sentence-BERT, BERT-match, and BGE, including dataset schemas, validation
helpers, command construction, and GPU/multi-card boundaries."
disable-model-invocation: true
metadata:
disco-role: operating
license: Apache 2.0
---
# Training and Fine-tuning
Use this sub-skill for text-matching and retrieval fine-tuning workflows:
- CoSENT
- Sentence-BERT
- BERT-match
- BGE
Use the bundled validators before training custom data.
## Route away
- embeddings-only inference → embeddings
- benchmark reporting or model choice → evaluation-benchmarks
- serving or deployment → serving-deployment
## Read in order
1. `references/data-formats.md`
2. `references/api-reference.md`
3. `references/training-workflows.md`
4. `references/troubleshooting.md`
## Bundled scripts
- `scripts/validate_text_matching_data.py`
- `scripts/validate_bge_jsonl.py`
## Operating notes
- Full training may need network access, GPU hardware, bf16 support, or Hugging Face dataset cache.
- Keep `output_dir` dedicated to one experiment; it is the reload path for prediction.
- Do not assume field names are uniform: the loaders accept either `sentence1`/`sentence2` or `text1`/`text2`.
