FastChat vs LLMs-from-scratch

Common questions

What is the difference between FastChat and LLMs-from-scratch?

FastChat: An open platform for training, serving, and evaluating large language models. LLMs-from-scratch: Implement a ChatGPT-like LLM in PyTorch from scratch, step by step. See the comparison table for live GitHub stats and shared categories.

When should I choose FastChat over LLMs-from-scratch?

Choose FastChat over LLMs-from-scratch when FastChat is primarily Python; LLMs-from-scratch is Jupyter Notebook; License: FastChat is Apache-2.0, LLMs-from-scratch is Other; Tags unique to FastChat: evaluation system, large-language-models, chatbots, distributed serving; Also covers Inference & Serving, Evaluation & Observability; - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench.

When should I choose LLMs-from-scratch over FastChat?

Choose LLMs-from-scratch over FastChat when LLMs-from-scratch is primarily Jupyter Notebook; FastChat is Python; License: LLMs-from-scratch is Other, FastChat is Apache-2.0; Tags unique to LLMs-from-scratch: deep-learning, ai, artificial-intelligence, attention-mechanism; - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework.

When should I avoid FastChat?

- You require a proprietary or closed-source framework; FastChat is open-source under Apache-2.0 license and its use might be unsuitable for environments requiring proprietary solutions. - Your chatbot evaluation needs do not align with the types of data used in FastChat's datasets (e.g., human votes, MT-Bench evaluations). - You prefer a more user-friendly setup without the need to clone a repository and manually install dependencies; FastChat requires installation from source with additional steps for Rust and CMake on + Mac.

When should I avoid LLMs-from-scratch?

- If you are looking for a rapid deployment of an LLM without understanding its intricate structure - this tool requires extensive manual and conceptual work. - You prefer frameworks with automatic model generation or other high-level abstractions that simplify the process. This repository emphasizes manual creation, which is more time-consuming but offers丰 a deeper learning experience.

Is FastChat or LLMs-from-scratch more popular on GitHub?

LLMs-from-scratch has more GitHub stars (98,899 vs 39,490). Stars measure visibility, not whether either tool fits your constraints.

Are FastChat and LLMs-from-scratch open source?

Yes - both are open-source projects on GitHub (FastChat: Apache-2.0, LLMs-from-scratch: Other).

Where can I find alternatives to FastChat or LLMs-from-scratch?

GraphCanon lists graph-backed alternatives at FastChat alternatives and LLMs-from-scratch alternatives (FastChat markdown twin, LLMs-from-scratch markdown twin), ranked by typed relationship edges rather than popularity votes.

Is there a machine-readable version of this comparison?

Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

Which is better maintained, FastChat or LLMs-from-scratch?

FastChat: Steady. LLMs-from-scratch: Steady. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

Where are the full trust reports for FastChat and LLMs-from-scratch?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: FastChat trust report; LLMs-from-scratch trust report.