CodeParrot
Hugging Face's open-source code generation model — fully documented training for learning from scratch.
Overview
CodeParrot is an open-source GPT-2 based code generation model trained by Hugging Face, notable as one of the most thoroughly documented AI training projects available. Rather than just releasing model weights, Hugging Face published a detailed training guide, training script, and dataset creation methodology for CodeParrot, making it an educational resource for anyone wanting to understand how code language models are trained.
The model was trained on a filtered subset of Python code from GitHub and produces basic Python code generation. While not state-of-the-art, it serves as a practical starting point for researchers and students learning about code generation model training, as every step of the process is documented and reproducible.
CodeParrot's documentation covers dataset creation, tokeniser training, model configuration, training loop implementation, and evaluation — a complete end-to-end tutorial. This transparency made it one of the most valuable educational resources in the code model training community and continues to be referenced in courses and tutorials about LLM training.
Pricing
Pricing shown for reference only. These figures reflect RECATOOLS research as of 11 Jul 2026 and may be out of date or incomplete. This is not financial or purchasing advice — always confirm the current price on the provider’s official website before making any decision.
Use cases
ASEAN Perspective
CodeParrot in Southeast Asia
ASEAN-region availability and pricing notes coming soon. Drop the editorial team a note via /contact/ if you can supply local context (Singapore/Malaysia/Indonesia/Thailand/Vietnam).
CodeParrot is the model you train to learn how models are trained. Hugging Face's GPT-2-based Python generator (1.5B and 110M variants) came with a full public recipe — data cleaning, tokenizer, training loop, evaluation — and that documentation is why it still appears in the Hugging Face LLM course and the O'Reilly Transformers book. Genuinely useful as an educational reference and a lightweight open baseline; not remotely competitive as a coding assistant. It's Python-only, 2021-era, and ships as bare weights loaded through the Transformers library — no product, UI or hosted API. Students and the LLM-curious: yes. Developers wanting real completions should use a modern assistant.
What people say
CodeParrot is a tutorial wearing a model's name tag, and that's meant as praise. Hugging Face's Leandro von Werra trained this GPT-2-based Python generator (1.5B, plus a 110M "small" variant) largely to show every step of the process in public: dataset scraping and deduplication, tokenizer training, an Accelerate training loop in under 200 lines, and evaluation. The December 2021 blog post walking through it remains one of the most-read train-an-LLM-from-scratch guides anywhere.
That afterlife is the real story. The Hugging Face LLM course still uses CodeParrot as the worked example in its train-a-causal-language-model-from-scratch chapter, and the "Natural Language Processing with Transformers" book gives it a chapter of its own. As pedagogy it has aged well. As a model it hasn't — a 2021 GPT-2 trained on Python is hopeless next to even small modern code models, and there's no product, UI or hosted endpoint; you load the checkpoint from the codeparrot org through the Transformers library.
The audience, then, is students, researchers and engineers who want to see how a code LLM gets built end to end — everything is documented and reproducible, and the small variant is light enough to fine-tune on modest hardware. Anyone who actually wants code suggestions should be elsewhere; that stopped being a fair ask of CodeParrot around 2022.
Summary of public user & expert reviews, compiled by RECATOOLS.
Notable facts
- CodeParrot's training was fully documented including every training hyperparameter — making it a university-level tutorial that anyone can follow to train their own code model from scratch.
- The model was trained live in public, with Hugging Face posting training loss curves and intermediate checkpoints as training progressed.
- CodeParrot was one of the first projects to demonstrate that a team without Big Tech resources could train a usable code generation model using open tools and datasets.
Frequently asked questions
About this listing
This entry was compiled from publicly available data including CodeParrot's official website, press releases, documentation, and reputable third-party publications. RECATOOLS is not affiliated with CodeParrot unless explicitly stated.
Third-party AI tools update their pricing, features, availability, and policies frequently. Information here may be outdated by the time you read this — we make reasonable efforts to keep listings current, but cannot guarantee absolute accuracy.
For the latest details, please refer to CodeParrot directly →
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