Decision Comparison
GitHub - rasbt/LLMs-from-scratch: Implement a ChatGPT-like LLM in PyTorch from scratch, step by step vs OpenAI Codex
Compare LLMs-from-scratch, a PyTorch educational resource for building LLMs from zero, with OpenAI Codex, a retired AI coding assistant. Understand the shift from learning internals to utilizing modern alternatives.
GitHub - rasbt/LLMs-from-scratch: Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
An open-source PyTorch guide by Sebastian Raschka to building a ChatGPT-like LLM from scratch, covering tokenization, attention, pretraining, and fine-tuning.
- Pricing
- FREE
- Free tier
- Yes
OpenAI Codex
OpenAI Codex was an AI coding assistant trained on billions of lines of public code, enabling natural language-to-code translation. It has been retired and replaced by ChatGPT Code Interpreter and other models.
- Pricing
- Not listed
- Free tier
- Not listed
Pros
- Specialized training on public code for high coding accuracy.
- Supported multiple popular programming languages.
- Enabled rapid prototyping and boilerplate generation.
- Integrated easily via API for custom developer tools.
- Helped beginners learn programming concepts through examples.
Cons
- Service has been retired and is no longer accessible.
- Trained on public data, raising potential privacy concerns.
- Generated code often required human review for security and bugs.
- API pricing could become expensive for high-volume usage.
- Limited context window compared to newer multimodal models.
Side-by-side signals
Core comparison table
| Signal | GitHub - rasbt/LLMs-from-scratch: Implement a ChatGPT-like LLM in PyTorch from scratch, step by step | OpenAI Codex |
|---|---|---|
| Summary | An open-source PyTorch guide by Sebastian Raschka to building a ChatGPT-like LLM from scratch, covering tokenization, attention, pretraining, and fine-tuning. | OpenAI Codex was an AI coding assistant trained on billions of lines of public code, enabling natural language-to-code translation. It has been retired and replaced by ChatGPT Code Interpreter and other models. |
| Pricing | FREE | Not listed |
| Free tier | Yes | Not listed |
| Pros count | 0 | 5 |
| Cons count | 0 | 5 |
Comparison analysis
This comparison contrasts two distinct approaches to large language models and code generation: one focused on deep educational implementation and the other on a now-retired production tool.
**LLMs-from-scratch** is an open-source GitHub repository by Sebastian Raschka. It provides a step-by-step guide to implementing a ChatGPT-like model using PyTorch. It is designed for developers and researchers who want to understand the inner workings of LLMs, covering tokenization, attention mechanisms, pretraining, and instruction tuning. It requires PyTorch knowledge and is best suited for educational purposes or small-scale research prototypes.
**OpenAI Codex**, announced in 2021, was an AI model trained on billions of lines of public code to translate natural language into code. It supported multiple languages like Python, JavaScript, and SQL. However, OpenAI officially retired the standalone Codex API in March 2023. Its capabilities have been integrated into ChatGPT (Advanced Data Analysis) and newer models like GPT-4. While Codex was excellent for rapid prototyping and lowering the barrier to entry for coding, it is no longer accessible as a standalone service.
**Key Differences:**
- **Status:** LLMs-from-scratch is active and open-source; Codex is retired.
- **Purpose:** LLMs-from-scratch teaches LLM architecture; Codex assisted in writing code.
- **Current Use:** Use LLMs-from-scratch to learn how LLMs work. For coding assistance, use modern alternatives like ChatGPT with Code Interpreter or GitHub Copilot.
Verdict
Which should you choose?
Choose LLMs-from-scratch for learning LLM internals. For coding tasks, use modern AI assistants as Codex is retired.
FAQ
Which is better for individuals: GitHub - rasbt/LLMs-from-scratch: Implement a ChatGPT-like LLM in PyTorch from scratch, step by step or OpenAI Codex?
GitHub - rasbt/LLMs-from-scratch: Implement a ChatGPT-like LLM in PyTorch from scratch, step by step lists a free tier, making it easier for low-cost trials.
Where does this comparison data come from?
The data comes from ToolSeekAI tool profiles, including summaries, pros, cons, keywords, and public official-site information.
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