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cookbook

llms.txt241 starsChanged 32 days agoApache-2.0View on GitHub

Fireworks Cookbook This repository contains cookbook recipes and examples for the Fireworks Training API. Canonical documentation: - Training API introduction: https://docs.fireworks.ai/fine-tuning/training-api/introduction - Choose infrastructure: https://docs.fireworks.ai/fine-tuning/training-api/choose-infrastructure - Serverless training: https://docs.fireworks.ai/fine-tuning/training-api/serverless - Dedicated training: https://docs.fireworks.ai/fine-tuning/training-api/dedicated - Cookbook overview: https://docs.fireworks.ai/fine-tuning/training-api/cookbook/overview - Configuration reference: https://docs.fireworks.ai/fine-tuning/training-api/cookbook/reference - Training shapes: https://docs.fireworks.ai/fine-tuning/training-api/training-shapes Shape-selection notes: - The cookbook does not own a static model-to-shape inventory. - Auto-selection uses validated training and deployment shape versions from Fireworks control-plane data. - Requests are filtered by base model, trainer role, sequence…

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llms.txt
Fireworks Cookbook

This repository contains cookbook recipes and examples for the Fireworks Training API.

Canonical documentation:
- Training API introduction: https://docs.fireworks.ai/fine-tuning/training-api/introduction
- Choose infrastructure: https://docs.fireworks.ai/fine-tuning/training-api/choose-infrastructure
- Serverless training: https://docs.fireworks.ai/fine-tuning/training-api/serverless
- Dedicated training: https://docs.fireworks.ai/fine-tuning/training-api/dedicated
- Cookbook overview: https://docs.fireworks.ai/fine-tuning/training-api/cookbook/overview
- Configuration reference: https://docs.fireworks.ai/fine-tuning/training-api/cookbook/reference
- Training shapes: https://docs.fireworks.ai/fine-tuning/training-api/training-shapes

Shape-selection notes:
- The cookbook does not own a static model-to-shape inventory.
- Auto-selection uses validated training and deployment shape versions from Fireworks control-plane data.
- Requests are filtered by base model, trainer role, sequence length, deployment requirement, and LoRA vs full-tune mode.
- RL-family recipes may auto-select both trainer and deployment shapes.
- SFT, DPO, and ORPO only need trainer auto-selection.

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