docs.fireworks.ai

# Fireworks AI Docs

> Official Fireworks AI documentation for inference, deployments, training, Fireworks Nexus, APIs, SDKs, and CLI tools.

> ## Agent Instructions
> For Fireworks Nexus, start at https://docs.fireworks.ai/nexus.
> Use https://docs.fireworks.ai/nexus/quickstart for coding harnesses, custom agents, APIs, SDKs, and LLM gateways.
> Use https://docs.fireworks.ai/nexus/firerouter for how model routers work, the supported model list, composition, closed-model credentials, and pricing.
> Prefer canonical short model IDs such as firerouter/opus. In LiteLLM litellm_params.model, use the full path fireworks_ai/accounts/fireworks/routers/firerouter/opus.
> Family names such as opus track the latest evaluated family version; do not describe them as fixed model versions.

- [Build with Fireworks AI](https://docs.fireworks.ai/getting-started/introduction.md): Fast inference and training for open source models
- [Fireworks Nexus](https://docs.fireworks.ai/getting-started/nexus.md): Connect coding tools, agents, applications, and gateways to open models and model routers.
- [FireConnect](https://docs.fireworks.ai/getting-started/fireconnect.md): Connect coding harnesses to Fireworks models and model routers.
- [FireRouter](https://docs.fireworks.ai/getting-started/firerouter.md): Use one model ID while a router chooses the model for each user turn.
- [Serverless Quickstart](https://docs.fireworks.ai/getting-started/quickstart.md): Make your first Serverless API call in minutes
- [Deployments Quickstart](https://docs.fireworks.ai/getting-started/ondemand-quickstart.md): Deploy models on dedicated GPUs in minutes
- [Which model should I use?](https://docs.fireworks.ai/guides/recommended-models.md): Find the best open models for your use case or migrate from closed source models like Claude, GPT, and Gemini
- [Concepts](https://docs.fireworks.ai/getting-started/concepts.md): This document outlines basic Fireworks AI concepts.
- [Serverless Inference Overview](https://docs.fireworks.ai/serverless/overview.md): How Serverless Inference works on Fireworks: serverless modes, billing, request/response headers, prompt caching, model lifecycle, and when to choose Serverless Inference over On-demand
- [Serverless Modes](https://docs.fireworks.ai/serverless/serverless-modes.md): Standard, Priority, and Fast serverless modes on Fireworks Serverless
- [Serverless Pricing](https://docs.fireworks.ai/serverless/pricing.md): Per-token serverless pricing for text, vision, and embedding models, including Priority and Fast serverless modes
- [Reserved Throughput](https://docs.fireworks.ai/serverless/reserved-throughput.md): Reserve throughput backed by an SLA
- [Serverless Rate Limits](https://docs.fireworks.ai/serverless/rate-limits.md): Adaptive rate limits grow and shrink with your usage
- [US-only Serverless](https://docs.fireworks.ai/serverless/us-only-serverless.md): Run Serverless inference exclusively in the US
- [Deployments](https://docs.fireworks.ai/guides/ondemand-deployments.md): Configure and manage on-demand deployments on dedicated GPUs
- [Autoscaling](https://docs.fireworks.ai/deployments/autoscaling.md): Configure how your deployment scales based on traffic
- [Custom Models](https://docs.fireworks.ai/models/uploading-custom-models.md): Upload, verify, and deploy your own models from Hugging Face or elsewhere
- [Upload via REST API](https://docs.fireworks.ai/models/uploading-custom-models-api.md): Programmatically upload custom models using the Fireworks REST API
- [Quantization](https://docs.fireworks.ai/models/quantization.md): Reduce model precision to improve performance and lower costs
- [Speculative Decoding](https://docs.fireworks.ai/deployments/speculative-decoding.md): Speed up generation with draft models and n-gram speculation
- [Regions](https://docs.fireworks.ai/deployments/regions.md): Fireworks runs a global fleet of hardware on which you can deploy your models.
- [Routers](https://docs.fireworks.ai/deployments/routers.md): Distribute traffic across multiple deployments for A/B testing, traffic migration, and load distribution.
- [Reserved capacity](https://docs.fireworks.ai/deployments/reservations.md)
- [Performance benchmarking](https://docs.fireworks.ai/deployments/benchmarking.md): Measure and optimize your deployment's performance with load testing
- [Client-side performance optimization](https://docs.fireworks.ai/deployments/client-side-performance-optimization.md): Optimize your client code for maximum performance with dedicated deployments
- [Exporting Metrics](https://docs.fireworks.ai/deployments/exporting-metrics.md): Export metrics from your dedicated deployments to your observability stack
- [Deployment Tags](https://docs.fireworks.ai/deployments/deployment-tags.md): Attach customer-defined metadata to dedicated deployments
- [Text Models](https://docs.fireworks.ai/guides/querying-text-models.md): Query, track and manage inference for text models
- [Vision Models](https://docs.fireworks.ai/guides/querying-vision-language-models.md): Query vision-language models to analyze images and visual content
- [Embeddings & Reranking](https://docs.fireworks.ai/guides/querying-embeddings-models.md): Generate embeddings and rerank results for semantic search
- [Tool Calling](https://docs.fireworks.ai/guides/function-calling.md): Connect models to external tools and APIs
- [Reasoning](https://docs.fireworks.ai/guides/reasoning.md): How to use reasoning with Fireworks models
- [Reliability and Error Handling](https://docs.fireworks.ai/guides/reliability.md): Recommended patterns for timeouts, retries, and error handling when building production applications on the Fireworks API.
- [Batch API](https://docs.fireworks.ai/guides/batch-inference.md): Process large-scale async workloads at a discount
- [Model Quality and Precision](https://docs.fireworks.ai/models/model-quality.md): How Fireworks tunes precision per model and validates every launch against the provider's official API before it serves production traffic.
- [Structured Outputs](https://docs.fireworks.ai/structured-responses/structured-response-formatting.md): Enforce output formats using JSON schemas or custom grammars
- [Using predicted outputs](https://docs.fireworks.ai/guides/predicted-outputs.md): Use Predicted Outputs to boost output generation speeds for editing / rewriting use cases
- [Prompt caching](https://docs.fireworks.ai/guides/prompt-caching.md)
- [Inference for RL Rollouts](https://docs.fireworks.ai/guides/rollout-inference.md): Session affinity, KV-cache behavior, weight-swap behavior, and MoE Router Replay for rollout traffic on Fireworks inference deployments.
- [Video & Audio Inputs](https://docs.fireworks.ai/guides/video-audio-inputs.md): Query multimodal models to process video and audio content directly
- [Completions API](https://docs.fireworks.ai/guides/completions-api.md): Use the completions API for raw text generation with custom prompt templates
- [Responses API](https://docs.fireworks.ai/guides/response-api.md): Build stateful, tool-using applications on Fireworks with the OpenAI-compatible Responses API.
- [Inference Error Codes](https://docs.fireworks.ai/guides/inference-error-codes.md): Common error codes, their meanings, and resolutions for inference requests
- [Training Overview](https://docs.fireworks.ai/fine-tuning/finetuning-intro.md)
- [Agent Skills](https://docs.fireworks.ai/fine-tuning/agent/use-with-coding-agents.md): Install Fireworks training skills for your coding agent — research, configure, and debug.
- [Models](https://docs.fireworks.ai/fine-tuning/models.md): Which base models you can train on Fireworks and the surfaces each one is available on.
- [Training cost estimator](https://docs.fireworks.ai/fine-tuning/cost-estimator.md): Estimate Managed Training, compare Fireworks Serverless with Dedicated, or compare Fireworks Dedicated with Tinker
- [Evaluating Trained Models](https://docs.fireworks.ai/fine-tuning/evaluating-fine-tuned-models.md): Evaluate a trained model before you create a production deployment.
- [Deploying Trained Models](https://docs.fireworks.ai/fine-tuning/deploying-loras.md): Deploy one or multiple LoRA models trained on Fireworks using live merge or multi-LoRA
- [Managed Training Overview](https://docs.fireworks.ai/fine-tuning/managed-finetuning-intro.md): Train models with Fireworks-managed infrastructure — no custom code required.
- [Supervised Fine-Tuning - Text](https://docs.fireworks.ai/fine-tuning/fine-tuning-models.md)
- [Thinking history in training](https://docs.fireworks.ai/fine-tuning/thinking-history.md): Choose how historical reasoning is rendered for managed training and match training behavior to inference.
- [Preference Optimization with DPO or ORPO](https://docs.fireworks.ai/fine-tuning/dpo-fine-tuning.md): Train on preferred and non-preferred response pairs using managed DPO or ORPO.
- [Overview](https://docs.fireworks.ai/fine-tuning/training-api/introduction.md): Fireworks Training API — custom training loops with full Python control over objectives, while Fireworks handles distributed GPU infrastructure.
- [Serverless Training](https://docs.fireworks.ai/fine-tuning/training-api/serverless.md): Run LoRA training, preference optimization, and RL on a shared pooled trainer, with no provisioning and per-token pricing.
- [Dedicated Training](https://docs.fireworks.ai/fine-tuning/training-api/dedicated.md): Run Training API workloads with provisioned trainer and deployment resources, explicit checkpoints, and lifecycle control.
- [Training Shapes](https://docs.fireworks.ai/fine-tuning/training-api/training-shapes.md): Pre-configured GPU and model training profiles that simplify distributed training setup.
- [The Cookbook](https://docs.fireworks.ai/fine-tuning/training-api/cookbook/overview.md): Ready-to-run training recipes for GRPO, DPO, SFT, and distillation built on top of the Training API.
- [Cookbook: Supervised Fine-Tuning](https://docs.fireworks.ai/fine-tuning/training-api/cookbook/sft.md): Supervised fine-tuning via the cookbook's sft_loop recipe.
- [Cookbook: Preference Optimization](https://docs.fireworks.ai/fine-tuning/training-api/cookbook/dpo.md): Direct Preference Optimization with pairwise data using the cookbook recipe.
- [Cookbook: Reinforcement Learning](https://docs.fireworks.ai/fine-tuning/training-api/cookbook/rl.md): Run experimental async GRPO with a custom rollout function while the recipe owns scheduling, training, and weight publication.
- [Cookbook: Agentic Reinforcement Learning](https://docs.fireworks.ai/fine-tuning/training-api/cookbook/agentic-rl.md): Preserve exact token evidence when tool-using agents cross the token/message boundary.
- [Cookbook: Fireworks x HUD RL Training](https://docs.fireworks.ai/fine-tuning/training-api/cookbook/hud-rl-training.md): Train a LoRA adapter with reinforcement learning using HUD environments and Fireworks Serverless Training.
- [Cookbook: Distillation](https://docs.fireworks.ai/fine-tuning/training-api/cookbook/distillation.md): On-policy distillation recipes with one or more frozen teachers.
- [Cookbook: Reference](https://docs.fireworks.ai/fine-tuning/training-api/cookbook/reference.md): Configuration classes, checkpoint utilities, weight sync, and advanced recipe knobs.
- [RL Rollouts with Your Own Trainer](https://docs.fireworks.ai/fine-tuning/rl-rollout-integration.md): Integrate an external RL trainer with Fireworks inference: hot-load new checkpoints from your bucket and run rollouts via the OpenAI-compatible API.
- [RL rollout cost comparison vs Tinker](https://docs.fireworks.ai/fine-tuning/multi-turn-cost-comparison.md): Compare rollout inference costs for multi-turn agentic RL on Fireworks and Tinker
- [Fire Pass](https://docs.fireworks.ai/firepass.md): Use selected open-weight model routers for personal, non-production agentic coding
- [Managing users](https://docs.fireworks.ai/accounts/users.md): Add, delete, and manage roles for users in your Fireworks account
- [Custom SSO](https://docs.fireworks.ai/accounts/sso.md): Set up custom Single Sign-On (SSO) authentication and SCIM user and group provisioning for Fireworks AI
- [Service Accounts](https://docs.fireworks.ai/accounts/service-accounts.md): How to manage and use service accounts in Fireworks
- [Account quotas](https://docs.fireworks.ai/guides/quotas_usage/account-quotas.md): Account-wide request limits, spending tiers, spend limits, and on-demand GPU quotas
- [Exporting Billing Metrics](https://docs.fireworks.ai/accounts/exporting-billing-metrics.md): Export billing and usage metrics for all Fireworks services
- [Exporting Usage Costs](https://docs.fireworks.ai/accounts/exporting-usage-costs.md): Export rated serverless usage costs to CSV, grouped by model, model tier, user, or API key
- [Usage & Cost Breakdown](https://docs.fireworks.ai/accounts/exporting-usage-and-costs.md): Break down usage by deployment, model, API key, or custom tags, and read account-level rated costs — via firectl or the billingUsage API
- [Enterprise features](https://docs.fireworks.ai/accounts/enterprise-features.md): Administrative capabilities available to Enterprise accounts
- [Model access policy](https://docs.fireworks.ai/accounts/model-access-policy.md): Restrict which models users on your Enterprise account can access for inference, deployments, and training
- [Data residency](https://docs.fireworks.ai/accounts/data-residency.md): Restrict inference for your Enterprise account to a selected region
- [Zero Data Retention policy](https://docs.fireworks.ai/accounts/zero-data-retention.md): Ensure no user actions on your account can persist or log customer content
- [Data Security](https://docs.fireworks.ai/guides/security_compliance/data_security.md): How we secure and handle your data for inference and training
- [Secure Training](https://docs.fireworks.ai/guides/security_compliance/secure_training.md): Choose the right training surface for your data-privacy needs, and understand what Fireworks retains and how to delete it
- [Bring Your Own Bucket (BYOB)](https://docs.fireworks.ai/guides/security_compliance/secure_training/byob.md): Keep training datasets in your own cloud storage; Fireworks reads them in place and never persists a copy
- [Customer-Managed Encryption Keys (CMEK)](https://docs.fireworks.ai/guides/security_compliance/secure_training/cmek.md): Use your own cloud KMS key to control encryption of your managed training data
- [Secure Reinforcement Fine-Tuning](https://docs.fireworks.ai/guides/security_compliance/secure_training/secure_rft.md): Run RFT end to end with your dataset, reward pipeline, and rollout infrastructure under your own control
- [Zero Data Retention](https://docs.fireworks.ai/guides/security_compliance/data_handling.md): Data retention policies at Fireworks
- [Audit & Access Logs](https://docs.fireworks.ai/guides/security_compliance/audit_logs.md): Monitor and track account activities with audit logging for Enterprise accounts
- [Microsoft Foundry](https://docs.fireworks.ai/ecosystem/integrations/azure-foundry.md): Deploy frontier open models inside your Azure subscription, billed through Azure.
- [Bring Your Own Cluster](https://docs.fireworks.ai/ecosystem/integrations/byoc/overview.md): Run Fireworks inference in your own Kubernetes cluster, cloud account or data center, and network boundary.
- [How Setup Works](https://docs.fireworks.ai/ecosystem/integrations/byoc/how-setup-works.md): Understand the high-level onboarding flow for Bring Your Own Cluster.
- [Operational Model](https://docs.fireworks.ai/ecosystem/integrations/byoc/operational-model.md): Learn how Fireworks operates Bring Your Own Cluster environments day to day.
- [Development Setup with Fireworks Docs MCP](https://docs.fireworks.ai/ecosystem/integrations/development-setup.md): Configure the Fireworks AI Docs MCP server for Claude Code and Cursor
- [Agent Frameworks](https://docs.fireworks.ai/ecosystem/integrations/agent-frameworks.md): Build production-ready AI agents with Fireworks and leading open-source frameworks
- [MLOps & Observability](https://docs.fireworks.ai/ecosystem/integrations/mlops-observability.md): Track and monitor your Fireworks AI deployments with leading MLOps and observability platforms
- [Glossary](https://docs.fireworks.ai/getting-started/glossary.md): Definitions for key terms used across Fireworks AI documentation.
- [Overview](https://docs.fireworks.ai/nexus.md): Fireworks Nexus brings frontier open models and model routers to coding harnesses, agents, and gateways, with Fireworks usage visibility and per-user spend caps for supported serverless models.
- [Quickstart](https://docs.fireworks.ai/nexus/quickstart.md): Connect a coding harness, application, or LLM gateway to Fireworks and make your first Nexus request.
- [FireConnect](https://docs.fireworks.ai/nexus/fireconnect.md): Point Claude Code, Claude Desktop, Codex, ChatGPT, OpenCode, Cursor IDE, VS Code, and Copilot at Fireworks with one FireConnect command
- [Coding Harnesses](https://docs.fireworks.ai/nexus/harnesses.md): Connect Claude Code, the Claude Agent SDK, Claude Desktop, OpenCode, Codex, Pi, Cursor IDE, VS Code, Copilot, or DeepSeek Harness to Fireworks, with FireConnect or by editing the harness settings yourself.
- [APIs and SDKs](https://docs.fireworks.ai/nexus/apis-and-sdks.md): Call Fireworks from custom agents, background runners, services, HTTP APIs, and SDKs
- [LLM Gateways](https://docs.fireworks.ai/nexus/llm-gateways.md): Connect Fireworks to LiteLLM, Portkey, Vercel AI Gateway, Helicone, Keywords AI, AISIX, Requesty, OpenRouter, or Cloudflare AI Gateway
- [Foundry for Coding Harnesses](https://docs.fireworks.ai/nexus/microsoft-foundry.md): Use Fireworks models deployed in Microsoft Foundry from Claude Code, OpenCode, Cursor IDE, VS Code, and other coding harnesses, through FireConnect, manual setup, or an LLM gateway.
- [FireRouter](https://docs.fireworks.ai/nexus/firerouter.md): Learn what FireRouter is, how it picks a model for each user turn, which models you can route, how to pass Anthropic or OpenAI credentials, and how to watch routing live.
- [FireRouter Setup](https://docs.fireworks.ai/nexus/firerouter/setup.md): Set up FireRouter in a coding harness with FireConnect, in an LLM gateway such as LiteLLM or Portkey, or from the OpenAI, Anthropic, or Fireworks SDK.
- [Open Models](https://docs.fireworks.ai/nexus/open-models.md): Use auto, version-tracking aliases, fast tiers, or pinned Fireworks open models in coding workflows.
- [Provider Keys](https://docs.fireworks.ai/nexus/provider-keys.md): Connect an account-level Anthropic, OpenAI, or Amazon Bedrock Provider Key once, where available, so model routers can call closed families without developers sending the key.
- [Amazon Bedrock](https://docs.fireworks.ai/nexus/provider-keys/bedrock.md): Connect a Bedrock API key so FireRouter can call supported models with credentials from your AWS account.
- [Usage and Cost](https://docs.fireworks.ai/nexus/metrics.md): Track estimated Claude Code session cost and model mix locally. Then view Fireworks model usage by model, user, or API key and Fireworks account cost by model.
- [Session Cost](https://docs.fireworks.ai/nexus/session-usage.md): Use FireConnect commands to inspect Claude Code session cost, model mix, cache share, token buckets, and request-level estimates.
- [Spend Limits](https://docs.fireworks.ai/nexus/usage-limits.md): Set account, group, and user-specific spend limits to cap per-user spend on supported Fireworks serverless models.
- [Harness Compatibility](https://docs.fireworks.ai/nexus/harness-compatibility.md): Compare FireRouter support, closed-model credentials, and MCP behavior across coding harnesses connected through FireConnect.
- [Routing Preferences](https://docs.fireworks.ai/nexus/routing-preferences.md): Adjust how strongly a firerouter request favors its primary model or lower-cost models.
- [Estimated Savings](https://docs.fireworks.ai/nexus/estimated-savings.md): Compare eligible serverless usage at public list prices with what the same tokens would have cost on Claude Opus 5.5.
- [Web Search](https://docs.fireworks.ai/nexus/web-search.md): Use native web search and fetch in Claude Code, Codex, and ChatGPT while connected through FireConnect.
- [CLI Reference](https://docs.fireworks.ai/nexus/cli-reference.md): FireConnect global commands, providers, authentication, and model selection
- [Introduction](https://docs.fireworks.ai/api-reference/introduction.md)
- [Python SDK](https://docs.fireworks.ai/tools-sdks/python-sdk.md)
- [Create Chat Completion](https://docs.fireworks.ai/api-reference/post-chatcompletions.md): Create a completion for the provided prompt and parameters.
- [Create Completion](https://docs.fireworks.ai/api-reference/post-completions.md): Create a completion for the provided prompt and parameters.
- [Create a Message](https://docs.fireworks.ai/api-reference/anthropic-messages.md): **Anthropic-compatible endpoint.**
- [Create Response](https://docs.fireworks.ai/api-reference/post-responses.md): Creates a model response, optionally interacting with custom tools via the Model Context Protocol (MCP). This endpoint supports conversational continuation and streaming.
- [List Responses](https://docs.fireworks.ai/api-reference/list-responses.md): Get a list of all responses for the authenticated account.
- [Get Response](https://docs.fireworks.ai/api-reference/get-response.md)
- [Delete Response](https://docs.fireworks.ai/api-reference/delete-response.md): Deletes a model response by its ID. Once deleted, the response data will be gone immediately and permanently.
- [Create Batch Inference Job](https://docs.fireworks.ai/api-reference/create-batch-inference-job.md)
- [List Batch Inference Jobs](https://docs.fireworks.ai/api-reference/list-batch-inference-jobs.md)
- [Get Batch Inference Job](https://docs.fireworks.ai/api-reference/get-batch-inference-job.md)
- [Delete Batch Inference Job](https://docs.fireworks.ai/api-reference/delete-batch-inference-job.md)
- [Rerank documents](https://docs.fireworks.ai/api-reference/rerank-documents.md): Rerank documents for a query using relevance scoring
- [Create embeddings](https://docs.fireworks.ai/api-reference/creates-an-embedding-vector-representing-the-input-text.md)
- [Create Deployment](https://docs.fireworks.ai/api-reference/create-deployment.md)
- [List Deployments](https://docs.fireworks.ai/api-reference/list-deployments.md)
- [Get Deployment](https://docs.fireworks.ai/api-reference/get-deployment.md)
- [Update Deployment](https://docs.fireworks.ai/api-reference/update-deployment.md)
- [Delete Deployment](https://docs.fireworks.ai/api-reference/delete-deployment.md)
- [Undelete Deployment](https://docs.fireworks.ai/api-reference/undelete-deployment.md)
- [Scale Deployment to a specific number of replicas or to zero](https://docs.fireworks.ai/api-reference/scale-deployment.md)
- [Create Model](https://docs.fireworks.ai/api-reference/create-model.md)
- [List Models](https://docs.fireworks.ai/api-reference/list-models.md)
- [Get Model](https://docs.fireworks.ai/api-reference/get-model.md)
- [Get Model Upload Endpoint](https://docs.fireworks.ai/api-reference/get-model-upload-endpoint.md)
- [Get Model Download Endpoint](https://docs.fireworks.ai/api-reference/get-model-download-endpoint.md)
- [Update Model](https://docs.fireworks.ai/api-reference/update-model.md)
- [Delete Model](https://docs.fireworks.ai/api-reference/delete-model.md)
- [Prepare Model for different precisions](https://docs.fireworks.ai/api-reference/prepare-model.md)
- [Validate Model Upload](https://docs.fireworks.ai/api-reference/validate-model-upload.md)
- [Load LoRA](https://docs.fireworks.ai/api-reference/create-deployed-model.md)
- [List LoRAs](https://docs.fireworks.ai/api-reference/list-deployed-models.md)
- [Get LoRA](https://docs.fireworks.ai/api-reference/get-deployed-model.md)
- [Update LoRA](https://docs.fireworks.ai/api-reference/update-deployed-model.md)
- [Unload LoRA](https://docs.fireworks.ai/api-reference/delete-deployed-model.md)
- [Create Router](https://docs.fireworks.ai/api-reference/create-router.md)
- [List Routers](https://docs.fireworks.ai/api-reference/list-routers.md)
- [CRUD APIs for routers.
Get Router](https://docs.fireworks.ai/api-reference/get-router.md)
- [Update Router](https://docs.fireworks.ai/api-reference/update-router.md)
- [Delete Router](https://docs.fireworks.ai/api-reference/delete-router.md)
- [List Deployment Shapes Versions](https://docs.fireworks.ai/api-reference/list-deployment-shape-versions.md)
- [Match Deployment Shape Versions](https://docs.fireworks.ai/api-reference/match-deployment-shape-versions.md): Returns the deployment shape versions compatible with the provided deployment create request. Use this to discover a validated shape before creating a deployment with `deployment_shape` set - shapeless deployments (raw accelerator type/count) skip validated-configuration checks and are far more like…
- [Get Deployment Shape](https://docs.fireworks.ai/api-reference/get-deployment-shape.md)
- [Get Deployment Shape Version](https://docs.fireworks.ai/api-reference/get-deployment-shape-version.md)
- [Create Dataset](https://docs.fireworks.ai/api-reference/create-dataset.md)
- [List Datasets](https://docs.fireworks.ai/api-reference/list-datasets.md)
- [Get Dataset](https://docs.fireworks.ai/api-reference/get-dataset.md)
- [Get Dataset Upload Endpoint](https://docs.fireworks.ai/api-reference/get-dataset-upload-endpoint.md)
- [Get Dataset Download Endpoint](https://docs.fireworks.ai/api-reference/get-dataset-download-endpoint.md)
- [Update Dataset](https://docs.fireworks.ai/api-reference/update-dataset.md)
- [Delete Dataset](https://docs.fireworks.ai/api-reference/delete-dataset.md)
- [Upload Dataset Files](https://docs.fireworks.ai/api-reference/upload-dataset-files.md): Provides a streamlined way to upload a dataset file in a single API request. This path can handle file sizes up to 150Mb. For larger file sizes use [Get Dataset Upload Endpoint](get-dataset-upload-endpoint).
- [Validate Dataset Upload](https://docs.fireworks.ai/api-reference/validate-dataset-upload.md)
- [Create Supervised Fine-tuning Job](https://docs.fireworks.ai/api-reference/create-supervised-fine-tuning-job.md)
- [List Supervised Fine-tuning Jobs](https://docs.fireworks.ai/api-reference/list-supervised-fine-tuning-jobs.md)
- [Get Supervised Fine-tuning Job](https://docs.fireworks.ai/api-reference/get-supervised-fine-tuning-job.md)
- [Delete Supervised Fine-tuning Job](https://docs.fireworks.ai/api-reference/delete-supervised-fine-tuning-job.md)
- [Resume Supervised Fine-tuning Job](https://docs.fireworks.ai/api-reference/resume-supervised-fine-tuning-job.md)
- [Create Reinforcement Fine-tuning Job](https://docs.fireworks.ai/api-reference/create-reinforcement-fine-tuning-job.md)
- [List Reinforcement Fine-tuning Jobs](https://docs.fireworks.ai/api-reference/list-reinforcement-fine-tuning-jobs.md)
- [Get Reinforcement Fine-tuning Job](https://docs.fireworks.ai/api-reference/get-reinforcement-fine-tuning-job.md)
- [Delete Reinforcement Fine-tuning Job](https://docs.fireworks.ai/api-reference/delete-reinforcement-fine-tuning-job.md)
- [Cancel Reinforcement Fine-tuning Job](https://docs.fireworks.ai/api-reference/cancel-reinforcement-fine-tuning-job.md)
- [Resume Reinforcement Fine-tuning Job](https://docs.fireworks.ai/api-reference/resume-reinforcement-fine-tuning-job.md)
- [Create Reinforcement Fine-tuning Step](https://docs.fireworks.ai/api-reference/create-reinforcement-fine-tuning-step.md)
- [List Reinforcement Fine-tuning Steps](https://docs.fireworks.ai/api-reference/list-reinforcement-fine-tuning-steps.md)
- [Get Reinforcement Fine-tuning Step](https://docs.fireworks.ai/api-reference/get-reinforcement-fine-tuning-step.md)
- [Delete Reinforcement Fine-tuning Step](https://docs.fireworks.ai/api-reference/delete-reinforcement-fine-tuning-step.md)
- [Resume Rlor Trainer Job](https://docs.fireworks.ai/api-reference/resume-reinforcement-fine-tuning-step.md)
- [Execute reinforcement fine tuning step](https://docs.fireworks.ai/api-reference/execute-reinforcement-fine-tuning-step.md)
- [Create dpo job](https://docs.fireworks.ai/api-reference/create-dpo-job.md)
- [List dpo jobs](https://docs.fireworks.ai/api-reference/list-dpo-jobs.md)
- [Get dpo job](https://docs.fireworks.ai/api-reference/get-dpo-job.md)
- [Get dpo job metrics file endpoint](https://docs.fireworks.ai/api-reference/get-dpo-job-metrics-file-endpoint.md)
- [Delete dpo job](https://docs.fireworks.ai/api-reference/delete-dpo-job.md)
- [Resume Dpo Job](https://docs.fireworks.ai/api-reference/resume-dpo-job.md)
- [FireworksClient](https://docs.fireworks.ai/fine-tuning/training-api/reference/fireworks-client.md): Account-level operations that don't require a running trainer job.
- [TrainerJobManager (Compatibility)](https://docs.fireworks.ai/fine-tuning/training-api/reference/trainer-job-manager.md): Legacy SDK reference for service-mode trainer job lifecycle management.
- [FiretitanServiceClient & TrainingClient](https://docs.fireworks.ai/fine-tuning/training-api/reference/service-client.md): Connect to a trainer endpoint and use the training client for forward/backward passes, optimizer steps, and checkpointing.
- [DeploymentManager (Compatibility)](https://docs.fireworks.ai/fine-tuning/training-api/reference/deployment-manager.md): Legacy SDK reference for direct deployment lifecycle and weight-sync management.
- [DeploymentSampler](https://docs.fireworks.ai/fine-tuning/training-api/reference/deployment-sampler.md): Client-side tokenized sampling for Training API rollouts and evaluation.
- [WeightSyncer (Legacy)](https://docs.fireworks.ai/fine-tuning/training-api/reference/weight-syncer.md): Backward-compatibility reference for the old standalone checkpoint-then-sync helper.
- [Cleanup and Teardown](https://docs.fireworks.ai/fine-tuning/training-api/reference/cleanup.md): Delete trainer jobs and deployments after experiments to avoid leaked resources.
- [RL parameters reference](https://docs.fireworks.ai/fine-tuning/rft-parameters-reference.md): Checkpoint, resume, and GRPO metrics fields for reinforcement learning recipes.
- [Create Evaluation Job](https://docs.fireworks.ai/api-reference/create-evaluation-job.md)
- [List Evaluation Jobs](https://docs.fireworks.ai/api-reference/list-evaluation-jobs.md)
- [Get Evaluation Job](https://docs.fireworks.ai/api-reference/get-evaluation-job.md)
- [Get Evaluation Job execution logs (stream log endpoint + tracing IDs).](https://docs.fireworks.ai/api-reference/get-evaluation-job-log-endpoint.md)
- [Delete Evaluation Job](https://docs.fireworks.ai/api-reference/delete-evaluation-job.md)
- [Create Evaluator](https://docs.fireworks.ai/api-reference/create-evaluator.md): Creates a custom evaluator for scoring model outputs. Evaluators use the [Eval Protocol](https://evalprotocol.io) to define test cases, run model inference, and score responses. They are used with evaluation jobs and Reinforcement Fine-Tuning (RFT).
- [List Evaluators](https://docs.fireworks.ai/api-reference/list-evaluators.md): Lists all evaluators for an account with pagination support.
- [Get Evaluator](https://docs.fireworks.ai/api-reference/get-evaluator.md): Retrieves an evaluator by name. Use this to monitor build progress after creation (**step 6** in the [Create Evaluator](/api-reference/create-evaluator) workflow).
- [Get Evaluator Upload Endpoint](https://docs.fireworks.ai/api-reference/get-evaluator-upload-endpoint.md): Returns signed URLs for uploading evaluator source code (**step 3** in the [Create Evaluator](/api-reference/create-evaluator) workflow). After receiving the signed URL, upload your `.tar.gz` archive using HTTP `PUT` with `Content-Type: application/octet-stream` header.
- [Get Evaluator Build Log Endpoint](https://docs.fireworks.ai/api-reference/get-evaluator-build-log-endpoint.md): Returns a signed URL to download the evaluator's build logs. Useful for debugging `BUILD_FAILED` state.
- [Get Evaluator Source Code Endpoint](https://docs.fireworks.ai/api-reference/get-evaluator-source-code-endpoint.md): Returns a signed URL to download the evaluator's source code archive. Useful for debugging or reviewing the uploaded code.
- [Update Evaluator](https://docs.fireworks.ai/api-reference/update-evaluator.md): Updates evaluator metadata (display_name, description, default_dataset). Changing `requirements` or `entry_point` triggers a rebuild. To upload new source code, set `prepare_code_upload: true` then follow the upload flow.
- [Delete Evaluator](https://docs.fireworks.ai/api-reference/delete-evaluator.md): Deletes an evaluator and its associated versions and build artifacts.
- [Validate Evaluator Upload](https://docs.fireworks.ai/api-reference/validate-evaluator-upload.md): Triggers server-side validation of the uploaded source code (**step 5** in the [Create Evaluator](/api-reference/create-evaluator) workflow). The server extracts and processes the archive, then builds the evaluator environment. Poll [Get Evaluator](/api-reference/get-evaluator) to monitor progress.
- [List Accounts](https://docs.fireworks.ai/api-reference/list-accounts.md)
- [Get Account](https://docs.fireworks.ai/api-reference/get-account.md)
- [List Quotas](https://docs.fireworks.ai/api-reference/list-quotas.md): Lists all quotas for an account.
- [Get Quota](https://docs.fireworks.ai/api-reference/get-quota.md): Gets a single quota by resource name.
- [Update Quota](https://docs.fireworks.ai/api-reference/update-quota.md): Updates a quota.
- [List User Audit Logs](https://docs.fireworks.ai/api-reference/list-audit-logs.md)
- [Get Account Usage](https://docs.fireworks.ai/api-reference/get-billing-usage.md)
- [Get billing summary information for an account](https://docs.fireworks.ai/api-reference/get-billing-summary.md)
- [Query grouped usage cost subtotals for an account.](https://docs.fireworks.ai/api-reference/query-usage-costs.md): Returns rated dollar subtotals for usage over a time range, grouped by up to two of: HOUR, DAY, MODEL, USER, or API_KEY. Unlike `GET /billingUsage` (metered quantities) and `GET /billing/summary` (line items by billing category), this endpoint returns *rated costs* broken down by caller-supplied dim…
- [Get Policy Settings](https://docs.fireworks.ai/api-reference/get-policy-settings.md)
- [Update Policy Settings](https://docs.fireworks.ai/api-reference/update-policy-settings.md)
- [Create User](https://docs.fireworks.ai/api-reference/create-user.md)
- [List Users](https://docs.fireworks.ai/api-reference/list-users.md)
- [Get User](https://docs.fireworks.ai/api-reference/get-user.md)
- [Update User](https://docs.fireworks.ai/api-reference/update-user.md)
- [Create API Key](https://docs.fireworks.ai/api-reference/create-api-key.md)
- [List API Keys](https://docs.fireworks.ai/api-reference/list-api-keys.md)
- [Delete API Key](https://docs.fireworks.ai/api-reference/delete-api-key.md)
- [Create secret](https://docs.fireworks.ai/api-reference/create-secret.md)
- [List Secrets](https://docs.fireworks.ai/api-reference/list-secrets.md): Lists all secrets for an account. Note that the `value` field is not returned in the response for security reasons. Only the `name` and `key_name` fields are included for each secret.
- [Get Secret](https://docs.fireworks.ai/api-reference/get-secret.md): Retrieves a secret by name. Note that the `value` field is not returned in the response for security reasons. Only the `name` and `key_name` fields are included.
- [Update secret](https://docs.fireworks.ai/api-reference/update-secret.md)
- [Delete secret](https://docs.fireworks.ai/api-reference/delete-secret.md)
- [Getting started](https://docs.fireworks.ai/tools-sdks/firectl/firectl.md): Learn to create, deploy, and manage resources using Firectl
- [Authentication](https://docs.fireworks.ai/tools-sdks/firectl/commands/authentication.md): Authentication for access to your account
- [firectl account get](https://docs.fireworks.ai/tools-sdks/firectl/commands/account-get.md): Prints information about an account.
- [firectl account list](https://docs.fireworks.ai/tools-sdks/firectl/commands/account-list.md): Prints all accounts the current signed-in user has access to.
- [firectl api-key create](https://docs.fireworks.ai/tools-sdks/firectl/commands/api-key-create.md): Creates an API key for the signed in user or a specified service account user.
- [firectl api-key delete](https://docs.fireworks.ai/tools-sdks/firectl/commands/api-key-delete.md): Deletes an API key.
- [firectl api-key get](https://docs.fireworks.ai/tools-sdks/firectl/commands/api-key-get.md): Prints information about an API key.
- [firectl api-key list](https://docs.fireworks.ai/tools-sdks/firectl/commands/api-key-list.md): Prints all API keys for the signed in user.
- [firectl audit-logs list](https://docs.fireworks.ai/tools-sdks/firectl/commands/audit-logs-list.md): Lists audit logs for the signed in user.
- [firectl batch-inference-job create](https://docs.fireworks.ai/tools-sdks/firectl/commands/batch-inference-job-create.md): Creates a batch inference job.
- [firectl batch-inference-job delete](https://docs.fireworks.ai/tools-sdks/firectl/commands/batch-inference-job-delete.md): Deletes a batch inference job.
- [firectl batch-inference-job get](https://docs.fireworks.ai/tools-sdks/firectl/commands/batch-inference-job-get.md): Retrieves information about a batch inference job.
- [firectl batch-inference-job list](https://docs.fireworks.ai/tools-sdks/firectl/commands/batch-inference-job-list.md): Lists all batch inference jobs in an account.
- [firectl billing export-metrics](https://docs.fireworks.ai/tools-sdks/firectl/commands/billing-export-metrics.md): Exports billing metrics
- [firectl billing get-usage](https://docs.fireworks.ai/tools-sdks/firectl/commands/billing-get-usage.md): Prints account usage and rated costs for a time range.
- [firectl billing list-invoices](https://docs.fireworks.ai/tools-sdks/firectl/commands/billing-list-invoices.md): Prints information about invoices.
- [firectl billing notification-settings](https://docs.fireworks.ai/tools-sdks/firectl/commands/billing-notification-settings.md): Manage notification settings.
- [firectl billing notification-settings get](https://docs.fireworks.ai/tools-sdks/firectl/commands/billing-notification-settings-get.md): Get notification settings for an account.
- [firectl billing notification-settings update](https://docs.fireworks.ai/tools-sdks/firectl/commands/billing-notification-settings-update.md): Update notification settings for an account.
- [firectl credit-redemption list](https://docs.fireworks.ai/tools-sdks/firectl/commands/credit-redemption-list.md): Lists credit code redemptions for the current account.
- [firectl credit-redemption redeem](https://docs.fireworks.ai/tools-sdks/firectl/commands/credit-redemption-redeem.md): Redeems a credit code.
- [firectl dataset create](https://docs.fireworks.ai/tools-sdks/firectl/commands/dataset-create.md): Creates and uploads a dataset.
- [firectl dataset delete](https://docs.fireworks.ai/tools-sdks/firectl/commands/dataset-delete.md): Deletes a dataset.
- [firectl dataset download](https://docs.fireworks.ai/tools-sdks/firectl/commands/dataset-download.md): Downloads a dataset to a local directory.
- [firectl dataset get](https://docs.fireworks.ai/tools-sdks/firectl/commands/dataset-get.md): Prints information about a dataset.
- [firectl dataset list](https://docs.fireworks.ai/tools-sdks/firectl/commands/dataset-list.md): Prints all datasets in an account.
- [firectl dataset update](https://docs.fireworks.ai/tools-sdks/firectl/commands/dataset-update.md): Updates a dataset.
- [firectl deployed-model get](https://docs.fireworks.ai/tools-sdks/firectl/commands/deployed-model-get.md): Prints information about a deployed model.
- [firectl deployed-model list](https://docs.fireworks.ai/tools-sdks/firectl/commands/deployed-model-list.md): Prints all deployed models in the account.
- [firectl deployed-model update](https://docs.fireworks.ai/tools-sdks/firectl/commands/deployed-model-update.md): Update a deployed model.
- [firectl deployment create](https://docs.fireworks.ai/tools-sdks/firectl/commands/deployment-create.md): Creates a new deployment.
- [firectl deployment delete](https://docs.fireworks.ai/tools-sdks/firectl/commands/deployment-delete.md): Deletes a deployment.
- [firectl deployment get](https://docs.fireworks.ai/tools-sdks/firectl/commands/deployment-get.md): Prints information about a deployment.
- [firectl deployment list](https://docs.fireworks.ai/tools-sdks/firectl/commands/deployment-list.md): Prints all deployments in the account.
- [firectl deployment scale](https://docs.fireworks.ai/tools-sdks/firectl/commands/deployment-scale.md): Scales a deployment to a specified number of replicas.
- [firectl deployment undelete](https://docs.fireworks.ai/tools-sdks/firectl/commands/deployment-undelete.md): Undeletes a deployment.
- [firectl deployment update](https://docs.fireworks.ai/tools-sdks/firectl/commands/deployment-update.md): Update a deployment.
- [firectl deployment-shape-version get](https://docs.fireworks.ai/tools-sdks/firectl/commands/deployment-shape-version-get.md): Prints information about a deployment shape version.
- [firectl deployment-shape-version list](https://docs.fireworks.ai/tools-sdks/firectl/commands/deployment-shape-version-list.md): Prints all deployment shape versions of this deployment shape.
- [firectl deployment-shape-version match](https://docs.fireworks.ai/tools-sdks/firectl/commands/deployment-shape-version-match.md): Prints the deployment shape versions this account can deploy a model on.
- [firectl dpo-job cancel](https://docs.fireworks.ai/tools-sdks/firectl/commands/dpo-job-cancel.md): Cancels a running dpo job.
- [firectl dpo-job create](https://docs.fireworks.ai/tools-sdks/firectl/commands/dpo-job-create.md): Creates a dpo job.
- [firectl dpo-job delete](https://docs.fireworks.ai/tools-sdks/firectl/commands/dpo-job-delete.md): Deletes a dpo job.
- [firectl dpo-job export-metrics](https://docs.fireworks.ai/tools-sdks/firectl/commands/dpo-job-export-metrics.md): Exports metrics for a dpo job.
- [firectl dpo-job get](https://docs.fireworks.ai/tools-sdks/firectl/commands/dpo-job-get.md): Retrieves information about a dpo job.
- [firectl dpo-job list](https://docs.fireworks.ai/tools-sdks/firectl/commands/dpo-job-list.md): Lists all dpo jobs in an account.
- [firectl dpo-job resume](https://docs.fireworks.ai/tools-sdks/firectl/commands/dpo-job-resume.md): Resumes a dpo job.
- [firectl evaluator-revision alias](https://docs.fireworks.ai/tools-sdks/firectl/commands/evaluator-revision-alias.md): Alias an evaluator revision
- [firectl evaluator-revision delete](https://docs.fireworks.ai/tools-sdks/firectl/commands/evaluator-revision-delete.md): Delete an evaluator revision
- [firectl evaluator-revision get](https://docs.fireworks.ai/tools-sdks/firectl/commands/evaluator-revision-get.md): Get an evaluator revision
- [firectl evaluator-revision list](https://docs.fireworks.ai/tools-sdks/firectl/commands/evaluator-revision-list.md): List evaluator revisions
- [firectl identity-provider create](https://docs.fireworks.ai/tools-sdks/firectl/commands/identity-provider-create.md): Creates a new identity provider.
- [firectl identity-provider get](https://docs.fireworks.ai/tools-sdks/firectl/commands/identity-provider-get.md): Prints information about an identity provider.
- [firectl identity-provider list](https://docs.fireworks.ai/tools-sdks/firectl/commands/identity-provider-list.md): List identity providers for an account
- [firectl model create](https://docs.fireworks.ai/tools-sdks/firectl/commands/model-create.md): Creates and uploads a model.
- [firectl model delete](https://docs.fireworks.ai/tools-sdks/firectl/commands/model-delete.md): Deletes a model.
- [firectl model download](https://docs.fireworks.ai/tools-sdks/firectl/commands/model-download.md): Download a model.
- [firectl model get](https://docs.fireworks.ai/tools-sdks/firectl/commands/model-get.md): Prints information about a model.
- [firectl model list](https://docs.fireworks.ai/tools-sdks/firectl/commands/model-list.md): Prints all models in an account.
- [firectl model load-lora](https://docs.fireworks.ai/tools-sdks/firectl/commands/model-load-lora.md): Loads a LoRA model.
- [firectl model prepare](https://docs.fireworks.ai/tools-sdks/firectl/commands/model-prepare.md): Prepare models for different precisions
- [firectl model unload-lora](https://docs.fireworks.ai/tools-sdks/firectl/commands/model-unload-lora.md): Unloads a LoRA model.
- [firectl model update](https://docs.fireworks.ai/tools-sdks/firectl/commands/model-update.md): Updates a model.
- [firectl model upload](https://docs.fireworks.ai/tools-sdks/firectl/commands/model-upload.md): Resumes or completes a model upload.
- [firectl quota get](https://docs.fireworks.ai/tools-sdks/firectl/commands/quota-get.md): Prints information about a quota.
- [firectl quota list](https://docs.fireworks.ai/tools-sdks/firectl/commands/quota-list.md): Prints all quotas.
- [firectl quota update](https://docs.fireworks.ai/tools-sdks/firectl/commands/quota-update.md): Updates a quota.
- [firectl reinforcement-fine-tuning-job cancel](https://docs.fireworks.ai/tools-sdks/firectl/commands/reinforcement-fine-tuning-job-cancel.md): Cancels a running reinforcement fine-tuning job.
- [firectl reinforcement-fine-tuning-job create](https://docs.fireworks.ai/tools-sdks/firectl/commands/reinforcement-fine-tuning-job-create.md): Creates a reinforcement fine-tuning job.
- [firectl reinforcement-fine-tuning-job delete](https://docs.fireworks.ai/tools-sdks/firectl/commands/reinforcement-fine-tuning-job-delete.md): Deletes a reinforcement fine-tuning job.
- [firectl reinforcement-fine-tuning-job get](https://docs.fireworks.ai/tools-sdks/firectl/commands/reinforcement-fine-tuning-job-get.md): Retrieves information about a reinforcement fine-tuning job.
- [firectl reinforcement-fine-tuning-job list](https://docs.fireworks.ai/tools-sdks/firectl/commands/reinforcement-fine-tuning-job-list.md): Lists all reinforcement fine-tuning jobs in an account.
- [firectl reinforcement-fine-tuning-job resume](https://docs.fireworks.ai/tools-sdks/firectl/commands/reinforcement-fine-tuning-job-resume.md): Resumes a failed reinforcement fine-tuning job.
- [firectl reinforcement-fine-tuning-job update](https://docs.fireworks.ai/tools-sdks/firectl/commands/reinforcement-fine-tuning-job-update.md): Update fields on a reinforcement fine-tuning job.
- [firectl reservation get](https://docs.fireworks.ai/tools-sdks/firectl/commands/reservation-get.md): Prints information about a reservation.
- [firectl reservation list](https://docs.fireworks.ai/tools-sdks/firectl/commands/reservation-list.md): Prints active reservations.
- [firectl rlor-trainer-job cancel](https://docs.fireworks.ai/tools-sdks/firectl/commands/rlor-trainer-job-cancel.md): Cancels a running rlor trainer job.
- [firectl rlor-trainer-job create](https://docs.fireworks.ai/tools-sdks/firectl/commands/rlor-trainer-job-create.md): Creates a rlor trainer job.
- [firectl rlor-trainer-job delete](https://docs.fireworks.ai/tools-sdks/firectl/commands/rlor-trainer-job-delete.md): Deletes a rlor trainer job.
- [firectl rlor-trainer-job get](https://docs.fireworks.ai/tools-sdks/firectl/commands/rlor-trainer-job-get.md): Retrieves information about a rlor trainer job.
- [firectl rlor-trainer-job list](https://docs.fireworks.ai/tools-sdks/firectl/commands/rlor-trainer-job-list.md): Lists all rlor trainer jobs in an account.
- [firectl router create](https://docs.fireworks.ai/tools-sdks/firectl/commands/router-create.md): Creates a router.
- [firectl router delete](https://docs.fireworks.ai/tools-sdks/firectl/commands/router-delete.md): Deletes a router.
- [firectl router get](https://docs.fireworks.ai/tools-sdks/firectl/commands/router-get.md): Prints information about a router.
- [firectl router list](https://docs.fireworks.ai/tools-sdks/firectl/commands/router-list.md): Prints all routers in the account.
- [firectl router update](https://docs.fireworks.ai/tools-sdks/firectl/commands/router-update.md): Update a router.
- [firectl secret create](https://docs.fireworks.ai/tools-sdks/firectl/commands/secret-create.md): Creates a secret for the signed in user.
- [firectl secret delete](https://docs.fireworks.ai/tools-sdks/firectl/commands/secret-delete.md): Deletes a secret.
- [firectl secret get](https://docs.fireworks.ai/tools-sdks/firectl/commands/secret-get.md): Retrieves a secret by name.
- [firectl secret list](https://docs.fireworks.ai/tools-sdks/firectl/commands/secret-list.md): Lists all secrets for the signed in user.
- [firectl secret update](https://docs.fireworks.ai/tools-sdks/firectl/commands/secret-update.md): Updates an existing secret.
- [firectl set-api-key](https://docs.fireworks.ai/tools-sdks/firectl/commands/set-api-key.md): Sets the default API key in ~/.fireworks/auth.ini.
- [firectl supervised-fine-tuning-job cancel](https://docs.fireworks.ai/tools-sdks/firectl/commands/supervised-fine-tuning-job-cancel.md): Cancels a running supervised fine-tuning job.
- [firectl supervised-fine-tuning-job create](https://docs.fireworks.ai/tools-sdks/firectl/commands/supervised-fine-tuning-job-create.md): Creates a supervised fine-tuning job.
- [firectl supervised-fine-tuning-job delete](https://docs.fireworks.ai/tools-sdks/firectl/commands/supervised-fine-tuning-job-delete.md): Deletes a supervised fine-tuning job.
- [firectl supervised-fine-tuning-job get](https://docs.fireworks.ai/tools-sdks/firectl/commands/supervised-fine-tuning-job-get.md): Retrieves information about a supervised fine-tuning job.
- [firectl supervised-fine-tuning-job list](https://docs.fireworks.ai/tools-sdks/firectl/commands/supervised-fine-tuning-job-list.md): Lists all supervised fine-tuning jobs in an account.
- [firectl training-shape clone](https://docs.fireworks.ai/tools-sdks/firectl/commands/training-shape-clone.md): Clones an existing training shape to a new shape.
- [firectl training-shape create](https://docs.fireworks.ai/tools-sdks/firectl/commands/training-shape-create.md): Creates a new training shape.
- [firectl training-shape delete](https://docs.fireworks.ai/tools-sdks/firectl/commands/training-shape-delete.md): Deletes a training shape and all its versions.
- [firectl training-shape get](https://docs.fireworks.ai/tools-sdks/firectl/commands/training-shape-get.md): Prints information about a training shape.
- [firectl training-shape list](https://docs.fireworks.ai/tools-sdks/firectl/commands/training-shape-list.md): Lists training shapes in the account.
- [firectl training-shape update](https://docs.fireworks.ai/tools-sdks/firectl/commands/training-shape-update.md): Updates a training shape (mutable fields only). Creates a new version automatically.
- [firectl training-shape-version get](https://docs.fireworks.ai/tools-sdks/firectl/commands/training-shape-version-get.md): Prints information about a training shape version.
- [firectl training-shape-version list](https://docs.fireworks.ai/tools-sdks/firectl/commands/training-shape-version-list.md): Lists training shape versions.
- [firectl training-shape-version update](https://docs.fireworks.ai/tools-sdks/firectl/commands/training-shape-version-update.md): Update a training shape version.
- [firectl upgrade](https://docs.fireworks.ai/tools-sdks/firectl/commands/upgrade.md): Upgrades the firectl binary to the latest version.
- [firectl user create](https://docs.fireworks.ai/tools-sdks/firectl/commands/user-create.md): Creates a new user.
- [firectl user delete](https://docs.fireworks.ai/tools-sdks/firectl/commands/user-delete.md): Deletes a user.
- [firectl user get](https://docs.fireworks.ai/tools-sdks/firectl/commands/user-get.md): Prints information about a user.
- [firectl user list](https://docs.fireworks.ai/tools-sdks/firectl/commands/user-list.md): Prints all users in the account.
- [firectl user update](https://docs.fireworks.ai/tools-sdks/firectl/commands/user-update.md): Updates a user.
- [firectl version](https://docs.fireworks.ai/tools-sdks/firectl/commands/version.md): Prints the version of firectl
- [firectl whoami](https://docs.fireworks.ai/tools-sdks/firectl/commands/whoami.md): Shows the currently authenticated user
- [Changelog](https://docs.fireworks.ai/updates/changelog.md)
- [OpenAI compatibility](https://docs.fireworks.ai/tools-sdks/openai-compatibility.md)
- [Anthropic compatibility](https://docs.fireworks.ai/tools-sdks/anthropic-compatibility.md): Use Anthropic SDKs with Fireworks, and understand the supported surface for the Anthropic-compatible Messages API.
- [NIM compatibility](https://docs.fireworks.ai/tools-sdks/nim-compatibility.md): Point NVIDIA NIM and vLLM OpenAI clients at Fireworks. We translate common extras like chat_template_kwargs so requests don't fail with Extra inputs are not permitted.
- [Courses](https://docs.fireworks.ai/examples/introduction.md): Standalone end-to-end examples showing how to use Fireworks to solve real-world use cases
- [Cookbooks](https://docs.fireworks.ai/examples/cookbooks.md): Interactive Jupyter notebooks demonstrating advanced use cases and best practices with Fireworks AI
- [What should I do if I can't access my company account after being invited when I already have a personal account?](https://docs.fireworks.ai/faq-new/account-access/what-should-i-do-if-i-cant-access-my-company-account-after-being-invited-when-i.md)
- [How do I close my Fireworks.ai account?](https://docs.fireworks.ai/faq-new/account-access/how-do-i-close-my-fireworksai-account.md)
- [I have multiple Fireworks accounts. When I try to login with Google on Fireworks' web UI, I'm getting signed into the wrong account. How do I fix this?](https://docs.fireworks.ai/faq-new/account-access/i-have-multiple-fireworks-accounts-when-i-try-to-login-with-google-on-fireworks.md)
- [What email does GitHub authentication use?](https://docs.fireworks.ai/faq-new/account-access/what-email-does-github-authentication-use.md)
- [What email does LinkedIn authentication use?](https://docs.fireworks.ai/faq-new/account-access/what-email-does-linkedin-authentication-use.md)
- [How much does Fireworks cost?](https://docs.fireworks.ai/faq-new/billing-pricing/how-much-does-fireworks-cost.md)
- [Are there extra fees for serving trained models?](https://docs.fireworks.ai/faq-new/billing-pricing/are-there-extra-fees-for-serving-fine-tuned-models.md)
- [Are there discounts for bulk usage?](https://docs.fireworks.ai/faq-new/billing-pricing/are-there-discounts-for-bulk-usage.md)
- [How does billing and credit usage work?](https://docs.fireworks.ai/faq-new/billing-pricing/how-does-billing-and-credit-usage-work.md)
- [Why might my account be suspended even with remaining credits?](https://docs.fireworks.ai/faq-new/billing-pricing/why-might-my-account-be-suspended-even-with-remaining-credits.md)
- [How do credits work?](https://docs.fireworks.ai/faq-new/billing-pricing/what-happens-when-i-finish-my-1-dollar-credit.md)
- [Is prompt caching billed differently for serverless models?](https://docs.fireworks.ai/faq-new/billing-pricing/is-prompt-caching-billed-differently.md)
- [How many tokens per image?](https://docs.fireworks.ai/faq-new/billing-pricing/how-many-tokens-per-image.md): Learn how to calculate token usage for images in vision models and understand pricing implications
- [What is a deployment shape, and why did my deployment fail to create?](https://docs.fireworks.ai/faq-new/deployment-infrastructure/what-is-a-deployment-shape.md)
- [Are there SLAs for serverless?](https://docs.fireworks.ai/faq-new/deployment-infrastructure/is-latency-guaranteed-for-serverless-models.md)
- [Are there any quotas for serverless?](https://docs.fireworks.ai/faq-new/deployment-infrastructure/are-there-any-quotas-for-serverless.md)
- [Do you provide notice before removing model availability?](https://docs.fireworks.ai/faq-new/deployment-infrastructure/do-you-provide-notice-before-removing-model-availability.md)
- [Why am I experiencing request timeout errors and slow response times with serverless LLM models?](https://docs.fireworks.ai/faq-new/deployment-infrastructure/why-am-i-experiencing-request-timeout-errors-and-slow-response-times-with-server.md)
- [How does the system scale?](https://docs.fireworks.ai/faq-new/deployment-infrastructure/how-does-the-system-scale.md)
- [Do you support Auto Scaling?](https://docs.fireworks.ai/faq-new/deployment-infrastructure/do-you-support-auto-scaling.md)
- [What’s the supported throughput?](https://docs.fireworks.ai/faq-new/deployment-infrastructure/whats-the-supported-throughput.md)
- [What factors affect the number of simultaneous requests that can be handled?](https://docs.fireworks.ai/faq-new/deployment-infrastructure/what-factors-affect-the-number-of-simultaneous-requests-that-can-be-handled.md)
- [How does autoscaling affect my costs?](https://docs.fireworks.ai/faq-new/deployment-infrastructure/how-does-autoscaling-affect-my-costs.md)
- [What are the rate limits for on-demand deployments?](https://docs.fireworks.ai/faq-new/deployment-infrastructure/what-are-the-rate-limits-for-on-demand-deployments.md)
- [How does billing work for on-demand deployments?](https://docs.fireworks.ai/faq-new/deployment-infrastructure/how-does-billing-work-for-on-demand-deployments.md)
- [How does billing and scaling work for on-demand GPU deployments?](https://docs.fireworks.ai/faq-new/deployment-infrastructure/how-does-billing-and-scaling-work-for-on-demand-gpu-deployments.md)
- [Does Fireworks support custom base models?](https://docs.fireworks.ai/faq-new/models-inference/does-fireworks-support-custom-base-models.md)
- [How to check if a model is available on serverless?](https://docs.fireworks.ai/faq-new/models-inference/how-to-check-if-a-model-is-available-on-serverless.md)
- [There’s a model I would like to use that isn’t available on Fireworks. Can I request it?](https://docs.fireworks.ai/faq-new/models-inference/theres-a-model-i-would-like-to-use-that-isnt-available-on-fireworks-can-i-reques.md)
- [Does the API support batching and load balancing?](https://docs.fireworks.ai/faq-new/models-inference/does-the-api-support-batching-and-load-balancing.md)
- [What factors affect the number of simultaneous requests that can be handled?](https://docs.fireworks.ai/faq-new/models-inference/what-factors-affect-the-number-of-simultaneous-requests-that-can-be-handled.md)
- [How do I control output image sizes when using SDXL ControlNet?](https://docs.fireworks.ai/faq-new/models-inference/how-do-i-control-output-image-sizes-when-using-sdxl-controlnet.md)

## OpenAPI Specs

- [anthropic-messages.openapi](/anthropic-messages.openapi.json)
- [gateway-extra.openapi](/gateway-extra.openapi.yaml)
- [gateway.openapi](/gateway.openapi.yaml)
- [merged.openapi](/merged.openapi.yaml)
- [openapi](/openapi.yml)
- [responses.openapi](/responses.openapi.yaml)
- [text-completion.openapi](/text-completion.openapi.yaml)