{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/5"}],"enrichment":{"capability":"Tinker Cookbook provides recipes and abstractions for fine-tuning language models via the Tinker API, supporting supervised learning, reinforcement learning, and specialized tasks like math reasoning, code generation, and multimodal training.","skillfed_tags":["llm-training","fine-tuning","post-training"],"use_cases":["Fine-tune an open-source LLM like Llama on your own conversational dataset using supervised learning without managing distributed training infrastructure.","Train a model to solve math problems with verifiable rewards using the math RL recipe with automated grading.","Build a code-generation model using competitive programming examples and sandboxed execution (DeepCoder-style) via the code RL recipe.","Implement a three-stage RLHF pipeline (SFT, reward model, RL) for preference-aligned models using the preference learning recipe.","Evaluate a fine-tuned model against standardized benchmarks (GSM8K, MMLU-Pro, IFEval, etc.) using the built-in evaluation framework.","Train a vision-language model for image classification or an audio model for speech recognition using the multimodal recipes."],"what_it_does":"Tinker Cookbook is a library of recipes and utilities built on top of the Tinker API\u2014a managed fine-tuning service for language models. It abstracts away distributed training complexity by letting you send API requests to Thinking Machines Lab's infrastructure rather than managing training yourself. The package includes realistic, runnable examples for common post-training workflows: supervised fine-tuning on conversational data, reinforcement learning for math and code, preference learning (DPO and RLHF), knowledge distillation, tool-use training, multi-agent RL, audio and vision-language model training, and more.\n\nYou install it alongside its 19 runtime dependencies (torch, transformers, datasets, huggingface-hub, and others) and then import recipes or utilities to configure and launch training jobs. The package also ships an experimental benchmark framework to evaluate trained models against 12 standard benchmarks, plus utilities for hyperparameter scaling, token-to-chat-message rendering, and weight management. All of this assumes you have a Tinker API account and an API key.","worth_installing":"Yes, if you have a Tinker API account and want to fine-tune LLMs without managing distributed training infrastructure. The package is actively maintained, well-documented, and provides production-ready recipes for common post-training tasks. Install friction is low and the Apache-2.0 license is permissive. No security vulnerabilities are known. The main blocker is the hard dependency on a Tinker API account and the substantial runtime footprint (torch, transformers, datasets, etc.)\u2014not a concern if you're already working in the LLM space."},"id":"tinker-cookbook","links":{"html":"https://skillfed.io/packages/tinker-cookbook","md":"https://skillfed.io/packages/tinker-cookbook.md","pypi":"https://pypi.org/project/tinker-cookbook/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-11","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"tinker_cookbook","python_support":"supports_current","summary":"Implementations of post-training algorithms using the Tinker API"},"popularity":{"monthly_downloads":599605,"position":5827,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.5.4"}