Practical overview
When it fits
Use it when you need one repository interface to select instruction data, a supported model and a tuning method for research or development, or when working through its documented inference and prediction workflows.
Who it is for
- Researchers comparing instruction-tuning datasets, models and methods.
- Developers configuring fine-tuning and inference workflows for supported language models.
Documented uses
- Configure instruction data and a supported language model for an instruction-tuning experiment.
- Apply a parameter-efficient method such as LoRA, AdaLoRA, prefix tuning, P-tuning or prompt tuning.
- Run documented inference, merge LoRA parameters, conduct local ChatGLM chatting or perform batch prediction.
Requirements & limits
The repository is public and open source under the Apache-2.0 license.
- The README identifies PyTorch 1.13 or later and provides a requirements-file installation step.
- Python 3.9 or later is specified for ChatGLM fine-tuning.
- Methods beyond LoRA require the project's edited PEFT package, according to the setup instructions.
- The project documents multiple model and data choices, but users must select model weights, dataset and training settings.
- For ChatGLM, 8-bit loading is noted as unsuitable, and the batch size must be smaller than for other models.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Set up the Alpaca-CoT environment
Install the repository dependencies and, when using tuning methods beyond LoRA, its edited PEFT package.
Before you start
- A local Python environment; Python 3.9 or later is specified for fine-tuning ChatGLM.
- The Alpaca-CoT repository and its requirements file.
Steps
Install the dependencies with the command below.
pip install -r requirements.txtIf using a method besides LoRA, install the project's edited PEFT package with the command below.
pip install -e ./peftFor ChatGLM fine-tuning, use Python 3.9 or later.
Expected outcome
The documented dependency setup is applied for the chosen tuning workflow.
Guide limitations
- The setup instructions do not establish that any particular environment or configuration will work.
- The ChatGLM instructions state that 8-bit loading is not yet suitable and its batch size must be smaller than for other models.
Configure a single-GPU LLaMA fine-tuning run
Use the documented single-GPU example to specify a LLaMA model, dataset, LoRA targets and training settings.
Before you start
- The Alpaca-CoT dependencies are installed.
- LLaMA model weights are available at the configured model path.
- A selected dataset, such as the documented Alpaca-Belle-CoT example.
Steps
Choose
--model_type llamaand set--model_name_or_pathto the desired LLaMA weights.Set the data input with
--dataand configure LoRA target modules; the example usesq_proj v_proj.Specify training options such as per-GPU batch size, learning rate and epochs, following the repository's single-GPU example.
Expected outcome
A configured command for the repository's documented single-GPU LLaMA instruction-tuning workflow.
Guide limitations
- The documented example uses particular model weights, data and training parameters; it does not guarantee successful execution or a particular result.
- The repository also permits multiple dataset paths through
--data, but the available hardware and resource requirements are not specified here.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 14 October 2026
- Original website title
- PhoebusSi/Alpaca-CoT · Official homepage title · retained in its source language