--- title: "codealpaca" type: "tool" slug: "sahil280114-codealpaca" canonical_url: "https://www.graphcanon.com/tools/sahil280114-codealpaca" github_url: "https://github.com/sahil280114/codealpaca" homepage_url: null stars: 1514 forks: 113 primary_language: "Python" license: "Apache-2.0" archived: false categories: ["llm-frameworks", "vector-databases", "model-training"] tags: ["python"] updated_at: "2026-07-11T23:44:31.146882+00:00" --- # codealpaca > codealpaca ## Facts - Repository: https://github.com/sahil280114/codealpaca - Stars: 1,514 · Forks: 113 · Open issues: 17 · Watchers: 19 - Primary language: Python - License: Apache-2.0 - Last pushed: 2023-05-12T17:41:28+00:00 ## Trust & health _Signals computed from public GitHub metadata. Not a security guarantee._ - Maintenance: Dormant (computed 2026-07-11T23:44:27.994Z) - Security scan: Findings present (0 critical, 0 high, 0 medium, 46 low) · last scan 2026-07-11T23:44:28.530Z - Full report: [trust report](/tools/sahil280114-codealpaca/trust.md) · [JSON](https://www.graphcanon.com/api/graphcanon/tools/sahil280114-codealpaca/trust) ## Categories - [LLM Frameworks](/categories/llm-frameworks.md) - [Vector Databases](/categories/vector-databases.md) - [Model Training](/categories/model-training.md) ## Tags python ## Category neighbours (exploratory) _Same-category tools for discovery only - not curated alternatives. 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The repo contains: - The [20K data](#data-release) used for fine-tuning the model - The code for [generating the data](#data-generation-process) - The code for [fine-tuning the model](#fine-tuning) Demo for the model can be found [https://code-alpaca-demo.vercel.app/](https://code-alpaca-demo.vercel.app/) ## Overview The Code Alpaca models are fine-tuned from a 7B and 13B LLaMA model on 20K instruction-following data generated by the techniques in the Self-Instruct [1] paper, with some modifications that we discuss in the next section. Evals are still a todo. The model is not finetuned to be safe and harmless, so be cautious. Current release contains the data generation procedure, dataset, and training code. Model weights aren't part of the release for now, to respect OpenAI TOS and LLaMA license. [1]: Self-Instruct: Aligning Language Model with Self Generated Instructions. Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, Hannaneh Hajishirzi. https://arxiv.org/abs/2212.10560 ## Data Release [`data/code_alpaca_20k.json`](./data/code_alpaca_20k.json) contains 20K instruction-following data used for fine-tuning the Code Alpaca model. This JSON file is a list of dictionaries, each dictionary contains the following fields: - `instruction`: `str`, describes the task the model should perform. Each of the 20K instructions is unique. - `input`: `str`, optional context or input for the task. For example, when the instruction is "Amend the following SQL query to select distinct elements", the input is the SQL query. Around 40% of the examples have an input. - `output`: `str`, the answer to the instruction as generated by `text-davinci-003`. We used the following prompts for fine-tuning the model: - for examples with a non-empty input field: ``` Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: {instruction} ### Input: {input} ### Response: ``` - for examples with an empty input field: ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {instruction} ### Response: ``` During inference (eg for the web demo), we use the user instruction with an empty input field (second option). ## Data Generation Process
Running the code 1. Set environment variables `OPENAI_API_KEY` to your OpenAI API key. 2. Install the dependencies with `pip install -r requirements.txt`. 3. Run `python -m generate_instruction generate_instruction_following_data` to generate the data.
Data generation pipeline had minor changes from [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) - Modified prompt to focus on code generation/editing/optimization tasks instead of general tasks. - Modified seed tasks to only be related to code generation. This produced an instruction-following dataset with 20K examples obtained at a much lower cost (less than $200). Also including a smaller 2k samples dataset which was used to derisk the approach and quality of the model. ## Fine-tuning Finetuned the models using standard Hugging Face training code and deepspeed with the following hyperparameters: | Hyperparameter | Value | |----------------|-------| | Learning rate | 2e-5 | | Epochs | 3 | | Max length | 512 | | Weight decay | 0 | Given Hugging Face hasn't officially supported the LLaMA models, we fine-tuned LLaMA wi ```` --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/tools/sahil280114-codealpaca`](/api/graphcanon/tools/sahil280114-codealpaca) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_