> ## Documentation Index > Fetch the complete documentation index at: https://docs.fireworks.ai/llms.txt > Use this file to discover all available pages before exploring further. # The Cookbook > Ready-to-run training recipes for GRPO, DPO, SFT, and distillation built on top of the Training API. ## What is the Cookbook? The [Fireworks Cookbook](https://github.com/fw-ai/cookbook/tree/main/training) is a collection of training recipes and utilities built on top of the [Training API](/fine-tuning/training-api/introduction). It provides config-driven training loops that handle trainer provisioning, data loading, tokenization, gradient accumulation, checkpointing, and cleanup automatically. The cookbook is **optional** — everything it does can be done with the API directly. Use the cookbook when you want a working training loop quickly; use the API when you need full control. ## Installation ```bash theme={null} git clone https://github.com/fw-ai/cookbook.git cd cookbook/training && pip install -e . ``` Set your credentials: ```bash theme={null} export FIREWORKS_API_KEY="your-api-key" ``` ## Available recipes | Recipe | Module | Use case | | ---------------------------------------- | ------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **RL** *(primary, experimental)* | `training.recipes.async_rl_loop` | Reinforcement learning — you write a rollout function, and the recipe owns completion-driven rollout production, bounded off-policy admission, serialized GRPO training, and weight publication. See [Cookbook RL](/fine-tuning/training-api/cookbook/rl). **No backward-compatibility guarantee.** | | **Serverless async RL** *(experimental)* | `training.recipes.experiment.async_rl_loop_serverless` | The same rollout and scheduling contract on the shared serverless training and sampling pool. See [Serverless Training](/fine-tuning/training-api/serverless). **No backward-compatibility guarantee.** | | **Agentic RL integration** | `async_rl_loop` plus your rollout adapter | Multi-turn agents, tools, environments, token ancestry, and session architecture. See [Cookbook Agentic RL](/fine-tuning/training-api/cookbook/agentic-rl). | | **RL** *(simpler, synchronous)* | `training.recipes.rl_loop` | Synchronous on-policy GRPO scaffold — reach for it when you don't need rollout/train overlap | | **IGPO** | `training.recipes.igpo_loop` | Information Gain-based Policy Optimization — turn-level IG rewards for multi-turn agent trajectories (extends GRPO) | | **DPO** | `training.recipes.dpo_loop` | Direct preference optimization from chosen/rejected pairs | | **SFT** | `training.recipes.sft_loop` | Supervised fine-tuning with cross-entropy loss | | **Distillation** | `training.recipes.distillation_loop` | On-policy distillation with sampled-token OPD, routed multi-teacher MOPD, and top-K SDFT | | **ORPO** | `training.recipes.orpo_loop` | Odds ratio preference optimization | Each recipe follows the same pattern: import `Config` and `main`, set your config, and call `main(cfg)`. Dedicated recipes attach or create trainer and deployment resources from `TrainerConfig` / `DeployConfig`. Serverless recipes attach to the shared pool and publish session-scoped sampler snapshots instead of provisioning a deployment. All launch examples below use `trainer=TrainerConfig(training_shape_id=...)` for explicit shape selection. Cookbook recipes can also auto-select validated shapes when `training_shape_id` is unset. The main run-level trainer knob you may set alongside a shape is `replica_count` for replicated HSDP launches; reference shapes can usually be left unset because the cookbook auto-selects or uses a shared-session reference when appropriate. If you want field-level details about what a training shape controls and what stays configurable, see [Training shapes](/fine-tuning/training-api/training-shapes) and the [Cookbook Reference](/fine-tuning/training-api/cookbook/reference). `InfraConfig` and the standalone `setup_infra` / `ResourceCleanup` helpers are **deprecated and removed from the recipe surface**. Recipes now take `trainer=TrainerConfig(...)` (and `deployment=DeployConfig(...)` for RL). See [Migrating from the deprecated managed infra](/fine-tuning/training-api/cookbook/reference#deprecated-managed-infra-infraconfig). ## Quick example: SFT ```python theme={null} from training.recipes.sft_loop import Config, main from training.utils import TrainerConfig cfg = Config( log_path="./sft_quickstart", base_model="accounts/fireworks/models/qwen3-8b", dataset="/path/to/training_data.jsonl", tokenizer_model="Qwen/Qwen3-8B", max_seq_len=4096, epochs=1, batch_size=4, trainer=TrainerConfig( training_shape_id="accounts/fireworks/trainingShapes/qwen3-8b-128k", ), ) main(cfg) ``` ## Quick example: GRPO ```python theme={null} from training.recipes.rl_loop import Config, main from training.utils import DeployConfig, TrainerConfig cfg = Config( log_path="./grpo_quickstart", base_model="accounts/fireworks/models/qwen3-8b", dataset="/path/to/prompts.jsonl", max_rows=100, trainer=TrainerConfig( training_shape_id="accounts/fireworks/trainingShapes/qwen3-8b-128k", ), deployment=DeployConfig( deployment_id="grpo-serving", tokenizer_model="Qwen/Qwen3-8B", ), weight_sync_interval=1, ) main(cfg) ``` ## W\&B logging All cookbook recipes accept a `WandBConfig` to stream metrics to [Weights & Biases](https://wandb.ai): ```python theme={null} from training.utils import WandBConfig cfg = Config( # ... same config as above ... wandb=WandBConfig( entity="my-team", project="grpo-experiment", run_name="qwen3-8b-sft-v1", # optional, auto-generated if omitted ), ) main(cfg) ``` ## Vision-language model support All cookbook recipes support VLM fine-tuning. Use a VLM training shape and tokenizer, and provide multimodal datasets with `image_url` content. See [Vision Inputs](/fine-tuning/models) for dataset format and examples. ## Embedding loop The [`embedding_loop`](https://github.com/fw-ai/cookbook/blob/main/training/recipes/embedding_loop.py) recipe fine-tunes embedding models with contrastive or supervised embedding objectives. Use it when your base model is an embedding endpoint rather than a chat completion model. ## Next steps * [Cookbook SFT](/fine-tuning/training-api/cookbook/sft) — supervised fine-tuning * [Cookbook DPO](/fine-tuning/training-api/cookbook/dpo) — preference optimization with pairwise data * [Cookbook RL (GRPO)](/fine-tuning/training-api/cookbook/rl) — full GRPO walkthrough with reward functions * [Cookbook Agentic RL](/fine-tuning/training-api/cookbook/agentic-rl) — multi-turn trajectory, token ancestry, and session design * [Cookbook Distillation](/fine-tuning/training-api/cookbook/distillation) — OPD, routed MOPD, and top-K SDFT * [Vision Inputs](/fine-tuning/models) — fine-tune VLMs with image and text data * [Cookbook Reference](/fine-tuning/training-api/cookbook/reference) — all config classes and parameters