Practical overview
When it fits
Use Magicoder when you need a code-generation model and want repository-level access to its checkpoints, dataset references, example inference code or demo options.
Who it is for
- Developers exploring instruction-based code generation or embedding a model component in a coding application.
- Researchers studying code-generation models, instruction-data methods or benchmark experiments.
Documented uses
- Generate code in response to a written instruction using a Transformers pipeline.
- Try a Magicoder model through the linked online Gradio playground.
- Launch the documented local Gradio demo.
- Explore the datasets used to train and further fine-tune Magicoder models.
Requirements & limits
The repository identifies its license as MIT. Its model table lists distinct Llama 2 or DeepSeek licenses for the individual checkpoints, so model terms vary by checkpoint.
- The quick start uses Hugging Face Transformers and PyTorch with the Magicoder-S-DS-6.7B checkpoint.
- The documented local demo command specifies CUDA, cuda:0 and port 8080.
- The repository links to an online Gradio playground as a separate way to try a model.
- The repository lists multiple model sizes and reports different HumanEval and MBPP results for its checkpoints.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Generate code with the Magicoder Transformers example
Use the documented Python quick start to send a coding instruction to the Magicoder-S-DS-6.7B model.
Before you start
- Python environment with Transformers and PyTorch available
- Access to the ise-uiuc/Magicoder-S-DS-6.7B model checkpoint
Steps
Import the Transformers pipeline and PyTorch.
Format an instruction between the example's @@ Instruction and @@ Response prompt markers.
Create a text-generation pipeline for ise-uiuc/Magicoder-S-DS-6.7B using bfloat16 and device_map="auto".
Generate and print the response with the example's max_length, sequence-count and temperature settings.
Expected outcome
The pipeline returns generated text in response to the coding instruction.
Guide limitations
- The example uses bfloat16 and automatic device mapping; the repository does not specify hardware requirements beyond these settings.
- The repository presents this as a quick-start example, not a hosted API.
Launch the documented local Gradio demo
Start the repository's local Gradio demo using the Magicoder-S-DS-6.7B checkpoint.
Before you start
- The repository's demo/magicoder_demo.py script
- A CUDA device, as specified by the launch command
Steps
Change to the repository's demo directory.
Run magicoder_demo.py with the base model set to ise-uiuc/Magicoder-S-DS-6.7B, device set to cuda:0 and port set to 8080.
Expected outcome
The command is documented to launch a local Gradio demo server.
Guide limitations
- The documented command assumes CUDA and does not describe a CPU launch path.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 13 October 2026
- Original website title
- ise-uiuc/magicoder · Official homepage title · retained in its source language