# sahil280114/codealpaca

Repository: https://github.com/sahil280114/codealpaca
Canonical: https://ross.abutalabs.com/products/codealpaca
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2023-05-12T17:41:28+00:00

## Health v2 (maintenance only)
Score: 30/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 90
- inputs: {"age_days": 1260, "days_push": 1209, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1514, forks 113 (observed 2026-08-28T04:04:56.410966+00:00)

## What it is
Code Alpaca is a 20K instruction-following dataset and training code for fine-tuning LLaMA models on code generation tasks, based on Stanford Alpaca. It includes the dataset, the Self-Instruct-based data generation pipeline, and fine-tuning scripts.

## Use cases
- fine-tune a llama model to generate code from instructions
- get a dataset of code generation instruction-output pairs
- generate synthetic instruction data for code tasks
- reproduce an instruction-tuned code model
- train a small open model for programming help

## When to choose
- you want to instruction-tune LLaMA-style models for code generation
- you need a compact 20K code instruction dataset
- you want to study the Self-Instruct data generation approach

## When to avoid
- you need model weights out of the box (not released)
- you need a safety-tuned production chat model
- you need up-to-date training tooling or strong benchmark results (evals are a todo)

## Facets
- artifact type: dataset
- maturity: maintenance
- function: machine-learning, llm-training, data-generation, prompt-engineering
- domain: large-language-models, machine-learning, developer-tools
- platform: python
- tags: code-generation, instruction-tuning, llama, fine-tuning, self-instruct, dataset

## Member repositories
- sahil280114/codealpaca (main) score 30

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:56.410966+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:32:13.589056+00:00, confidence not recorded.
  - readme: https://github.com/sahil280114/codealpaca (fetched 2026-08-28T04:04:56.410966+00:00, sha 0f089bcb7ba6)
- Data as of 2026-08-30T08:39:29.467469+00:00.