GraphCanon updated Oct 7, 2026 · GitHub synced Oct 7, 2026
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Decision brief
A specialized LLaMA model for generating code from instructions, leveraging 20K fine-tuning data inspired by the Self-Instruct paper.
Good fit when
- When you need instruction-following capabilities tailored specifically for code generation tasks.
- If your requirements align with a code-focused model that was trained using prompts emphasizing coding activities, such as code editing and optimization.
Avoid when
- Avoid if you require models fine-tuned on datasets that cover a broader spectrum of non-code-related instructions beyond code editing and generation.
- Do not use this tool when you must adhere to strict compliance or safety standards for model output, as the Code Alpaca model is noted to be unsafe and not fine-tuned for harmlessness.
- Requirements:
- Model weights are not available in this repository due to licensing restrictions.
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (1243d since push)
- As of Oct 7, 2026
- Provenance
- Not a fork · Personal account
- As of Oct 7, 2026
- Security (OSV)
- 46 low (46 low)
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install codealpacaSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
This repository focuses on creating and sharing an instruction-following LLaMA model tailored for generating code, using 20K fine-tuning data inspired by the Self-Instruct paper. It includes dataset generation processes, training protocols, but excludes model weights due to licensing restrictions.
Capability facts
- Languages
- python
Source: github.language · Oct 7, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Oct 7, 2026)
1. Set environment variables `OPENAI_API_KEY` to your OpenAI API key.Source link
Source: README excerpt (regex_v1, Oct 7, 2026)
3. Run `python -m generate_instruction generate_instruction_following_data` to generate the daSource link
Tags
README
Code Alpaca: An Instruction following LLaMA Model trained on code generation instructions This is the repo for the Code Alpaca project, which aims to build and share an instruction following LLaMA model for code generation. This repo is fully based on Stanford Alpaca ,and only ch...
For agents
This page has a .md twin and JSON over the API.