# Pioneer > Docs for building, fine-tuning, evaluating, and deploying models with Pioneer. ## Docs - [CLI installation](https://docs.pioneer.ai/CLI-Installation.md): Install the Pioneer CLI, authenticate with an API key, and verify your terminal setup. - [Pioneer API authentication: generate and use API keys](https://docs.pioneer.ai/api-reference/authentication.md): How to generate a Pioneer API key, pass it in the X-API-Key header, and manage keys programmatically including creation, listing, and revocation. - [Coding agent integration](https://docs.pioneer.ai/api-reference/coding-agent-integration.md): Integrating Pioneer with coding agents - [Dataset management API — list, inspect, and delete](https://docs.pioneer.ai/api-reference/datasets.md): List all datasets in your Pioneer account, inspect version history and example counts, and delete datasets you no longer need. Storage is free on all plans. - [Pioneer API error codes: 401, 402, 404, 422, 429, 500](https://docs.pioneer.ai/api-reference/errors.md): HTTP status codes returned by the Pioneer API — 401, 402, 404, 422, 429, and 500 — with plain-language explanations and steps to resolve each one. - [Evaluation API — measure model F1 before deploying](https://docs.pioneer.ai/api-reference/evaluations.md): Run Pioneer evaluations against labeled datasets to measure F1, precision, and recall with per-entity breakdowns before promoting a model to production. - [Anthropic-compatible POST /v1/messages on Pioneer API](https://docs.pioneer.ai/api-reference/inference/anthropic-compatible.md): Use Pioneer as a drop-in Anthropic SDK replacement. Point base_url to https://api.pioneer.ai/v1 and use your Pioneer API key to access fine-tuned models. - [Inference history and feedback endpoints on Pioneer](https://docs.pioneer.ai/api-reference/inference/history.md): List Pioneer inference history, filter by model or project, retrieve individual results, and submit corrections to improve your model via Adaptive Inference. - [OpenAI-compatible chat and completions on Pioneer API](https://docs.pioneer.ai/api-reference/inference/openai-compatible.md): Drop-in OpenAI replacement on Pioneer. Set base_url to https://api.pioneer.ai/v1, use your Pioneer key, and all SDK methods including streaming work unchanged. - [POST /inference — Pioneer native inference endpoint](https://docs.pioneer.ai/api-reference/inference/pioneer.md): POST /inference runs schema-based predictions on encoder or decoder models. Accepts model_id, text, schema with entities or classifications, and a threshold. - [Pioneer REST API: base URL, auth, and quick reference](https://docs.pioneer.ai/api-reference/overview.md): The Pioneer REST API overview: base URL, authentication, the recommended 5-step workflow from dataset to inference, and links to all endpoint group references. - [Rate Limits](https://docs.pioneer.ai/api-reference/rate-limits.md) - [Synthetic data API — POST /generate and label-existing](https://docs.pioneer.ai/api-reference/synthetic-data.md): Start Pioneer data generation jobs for NER, classification, or decoder tasks, poll job status, and auto-label existing text without manual annotation. - [Training jobs API — start, poll, stop, and download](https://docs.pioneer.ai/api-reference/training-jobs.md): Submit Pioneer fine-tuning jobs, poll status, stream logs, list checkpoints, download weights, and stop or delete jobs. Supports LoRA and full fine-tuning. - [How to authenticate your requests with Pioneer API](https://docs.pioneer.ai/authentication.md): Generate an API key from your Pioneer account, then include it in the X-API-Key header on every request. No OAuth or token refresh required. - [Pioneer datasets: create, version, inspect, and delete](https://docs.pioneer.ai/concepts/datasets.md): Pioneer stores and versions your training datasets automatically. Learn how to create them via generation or auto-labeling, then list, inspect, and delete them. - [Model evaluations in Pioneer: F1, precision, recall](https://docs.pioneer.ai/concepts/evaluations.md): Run Pioneer evaluations to measure F1, precision, and recall on a labeled dataset before deploying your fine-tuned model to production traffic. - [Inference](https://docs.pioneer.ai/concepts/inference.md) - [Pioneer model catalog: encoders, decoders, and inference](https://docs.pioneer.ai/concepts/models.md): Browse Pioneer's encoder (GLiNER) and decoder (LLM) models for fine-tuning and inference. Covers on-demand vs. serverless and how to query the live catalog. - [Pioneer training jobs: lifecycle, metrics, and weights](https://docs.pioneer.ai/concepts/training.md): Understand how Pioneer training jobs work — from submitting a job and polling status to reading metrics, stopping jobs, and downloading trained model weights. - [Pioneer FAQ: plans, data privacy, storage, and teams](https://docs.pioneer.ai/faq.md): Answers to common questions about Pioneer plans, storage costs, data training practices, team collaboration, and special pricing for nonprofits and students. - [Adaptive Inference: automatic continuous retraining](https://docs.pioneer.ai/guides/adaptive-inference.md): Pioneer's Adaptive Inference monitors live traffic, collects corrections, retrains a new checkpoint, and promotes it automatically when performance improves. - [Use Pioneer with AI coding agents via Agent Skills](https://docs.pioneer.ai/guides/agent-skills.md): Add a SKILL.md file to your AI coding agent so Cursor, Claude Code, or similar agents can manage Pioneer datasets, training, and inference autonomously. - [Fine-tune an open-source LLM using LoRA on Pioneer](https://docs.pioneer.ai/guides/fine-tune-llm.md): Train a custom large language model using LoRA fine-tuning on Qwen, Llama, DeepSeek, or Gemma base models. Pioneer handles compute, routing, and serving. - [Fine-tune a GLiNER NER model from data to inference](https://docs.pioneer.ai/guides/fine-tune-ner.md): Train a custom Named Entity Recognition model on your data using Pioneer's GLiNER encoder models, from dataset prep through evaluation and inference. - [Generate synthetic training data for NER and LLM tasks](https://docs.pioneer.ai/guides/synthetic-data.md): Use Pioneer's data generation API to create labeled NER, classification, and decoder training examples without manual annotation, or auto-label existing text. - [Drop us in. We'll ship the models.](https://docs.pioneer.ai/introduction.md): Pioneer spots where your model fails, then quietly retrains it on your own data. No MLOps team required. - [Pioneer plans: Free, Pro, and Enterprise](https://docs.pioneer.ai/pricing.md): Compare Pioneer's Free, Pro, and Enterprise plans. Start free with $200 of usage, scale to uncapped inference on Pro, or contact us for Enterprise. - [Pioneer quickstart: from signup to your first inference](https://docs.pioneer.ai/quickstart.md): Go from zero to a working Pioneer inference call in minutes. Generate an API key, browse available models, and run your first NER prediction.