Awesome-Code-LLM vs CodeGeeX

Common questions

What is the difference between Awesome-Code-LLM and CodeGeeX?

Awesome-Code-LLM: 👨💻 An awesome and curated list of best code-LLM for research.. CodeGeeX: CodeGeeX: An Open Multilingual Code Generation Model. See the comparison table for live GitHub stats and shared categories.

When should I choose Awesome-Code-LLM over CodeGeeX?

Choose Awesome-Code-LLM over CodeGeeX when License: Awesome-Code-LLM is MIT, CodeGeeX is Apache-2.0; Requirements: No specific requirements to use the repository for reference or evaluation, but contributions may involve technical knowledge and familiarity with code-LLMs.; Tags unique to Awesome-Code-LLM: awesome, large-language-models; Also covers Evaluation & Observability; When you need a comprehensive list of state-of-the-art code generation LLMs with performance metrics such as HumanEval.

When should I choose CodeGeeX over Awesome-Code-LLM?

Choose CodeGeeX over Awesome-Code-LLM when License: CodeGeeX is Apache-2.0, Awesome-Code-LLM is MIT; Requirements: Requires Docker; Requires Python 3.7+, CUDA 11+, PyTorch 1.10+, and DeepSpeed 0.6+.; Supports installation via pip or through a Docker container for quick setup.; Tags unique to CodeGeeX: code-generation-model, gpu-support, mindspore, multilingual; Also covers Model Training; When you need a multilingual code generation model that supports both Mindspore and PyTorch frameworks, allowing flexibility between Ascend 910 AI Processors and GPU platforms.

When should I avoid Awesome-Code-LLM?

When looking for a tool that provides pre-trained models with built-in APIs or services, as Awesome-Code-LLM is primarily a directory/collection of information without direct service provision. If you require real-time interactive use-cases and need immediate API access to LLMs; this repository does not offer such functionality. In scenarios where you need a single end-to-end solution for training your own code generation models, as the platform is focused on aggregating third-party resources and research rather than offering

When should I avoid CodeGeeX?

If your project is strictly limited to TensorFlow or other frameworks not supported by CodeGeeX, as it primarily supports Mindspore and PyTorch. When your development environment does not support Python 3.7+, CUDA 11+, PyTorch 1.10+, or DeepSpeed 0.6+, as these are the minimum requirements for running CodeGeeX. If you are looking for a model that does not require specific hardware like Ascend 910 AI Processors or GPUs, as CodeGeeX is optimized for these platforms.

Is Awesome-Code-LLM or CodeGeeX more popular on GitHub?

CodeGeeX has more GitHub stars (8,806 vs 1,295). Stars measure visibility, not whether either tool fits your constraints.

Are Awesome-Code-LLM and CodeGeeX open source?

Yes - both are open-source projects on GitHub (Awesome-Code-LLM: MIT, CodeGeeX: Apache-2.0).

Where can I find alternatives to Awesome-Code-LLM or CodeGeeX?

GraphCanon lists graph-backed alternatives at Awesome-Code-LLM alternatives and CodeGeeX alternatives (Awesome-Code-LLM markdown twin, CodeGeeX markdown twin), ranked by typed relationship edges rather than popularity votes.

Is there a machine-readable version of this comparison?

Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

Which is better maintained, Awesome-Code-LLM or CodeGeeX?

Awesome-Code-LLM: Dormant. CodeGeeX: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

Where are the full trust reports for Awesome-Code-LLM and CodeGeeX?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-Code-LLM trust report; CodeGeeX trust report.