--- title: "MiniMax-01 vs Chinese-LLaMA-Alpaca-2" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/minimax-ai-minimax-01-vs-ymcui-chinese-llama-alpaca-2" tools: ["minimax-ai-minimax-01", "ymcui-chinese-llama-alpaca-2"] --- # MiniMax-01 vs Chinese-LLaMA-Alpaca-2 *GraphCanon updated Sep 20, 2026* ## Verdict Pick MiniMax-01 if miniMax-01 optimizes Linear Attention for large-language and vision-language models; pick Chinese-LLaMA-Alpaca-2 if chinese-LLaMA-Alpaca-2 provides Chinese versions of LLaMA-2 and Alpaca-2 large language models, including long context models up to 64K. It includes pretraining and instruction fine-tuning scripts, and supports various部署. [MiniMax-01](https://www.minimax.io/) reports 3.5k GitHub stars, 332 forks, and 8 open issues, last pushed Jul 7, 2025. [Chinese-LLaMA-Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2) has 7.1k stars, 559 forks, and 6 open issues, last pushed Apr 19, 2026. Figures are from public GitHub metadata via [MiniMax-01's repository](https://github.com/MiniMax-AI/MiniMax-01) and [Chinese-LLaMA-Alpaca-2's repository](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2). | | [MiniMax-01](/tools/minimax-ai-minimax-01.md) | [Chinese-LLaMA-Alpaca-2](/tools/ymcui-chinese-llama-alpaca-2.md) | | --- | --- | --- | | Tagline | Repository for MiniMax-Text-01 and MiniMax-VL-01 models based on Linear Attention | Chinese LLaMA-2 & Alpaca-2 LLMs with 64K long context models | | Stars | 3,463 | 7,118 | | Forks | 332 | 559 | | Open issues | 8 | 6 | | Language | Python | Python | | Adopt for | MiniMax-01 optimizes Linear Attention for large-language and vision-language models. | Chinese-LLaMA-Alpaca-2 provides Chinese versions of LLaMA-2 and Alpaca-2 large language models, including long context models up to 64K. It includes pretraining and instruction fine-tuning scripts, and supports various部署 | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | LLM Frameworks, Model Training | Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [MiniMax-01](/tools/minimax-ai-minimax-01.md) | [Chinese-LLaMA-Alpaca-2](/tools/ymcui-chinese-llama-alpaca-2.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 406d | 152d | | Open issues (now) | 8 | 6 | | Stars delta | +17 (30d) | -6 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/minimax-ai-minimax-01/trust.md) | [trust report](/tools/ymcui-chinese-llama-alpaca-2/trust.md) | ## Decision facts: MiniMax-01 - **Adopt for:** MiniMax-01 optimizes Linear Attention for large-language and vision-language models. ## Decision facts: Chinese-LLaMA-Alpaca-2 - **Adopt for:** Chinese-LLaMA-Alpaca-2 provides Chinese versions of LLaMA-2 and Alpaca-2 large language models, including long context models up to 64K. It includes pretraining and instruction fine-tuning scripts, and supports various部署 ## Choose when ### Choose MiniMax-01 if… - License: MiniMax-01 is MIT, Chinese-LLaMA-Alpaca-2 is Apache-2.0. - Tags unique to MiniMax-01: large-language-models, llm, vision-language-model, vlm. - When high throughput performance is required for model serving ### Choose Chinese-LLaMA-Alpaca-2 if… - License: Chinese-LLaMA-Alpaca-2 is Apache-2.0, MiniMax-01 is MIT. - Tags unique to Chinese-LLaMA-Alpaca-2: 64k, alpaca, alpaca-2, flash-attention. - Also covers Inference & Serving. - When you need Chinese-specific large language models with long context capabilities up to 64K. ## When NOT to use MiniMax-01 - If deep customization of attention mechanisms aside from Linear Attention is needed - In favor of frameworks lacking vLLM's optimization features, when efficiency or memory use are secondary to flexibility ## When NOT to use Chinese-LLaMA-Alpaca-2 - If you need models that support languages other than Chinese, as this repository focuses on Chinese models. - If you require models with shorter context lengths than 4K, as the standard models support 4K and the long context models support 16K and 64K. - If you are looking for models that do not include human preference alignment (RLHF), as this repository includes models that have been fine-tuned for this purpose. ## Common questions ### What is the difference between MiniMax-01 and Chinese-LLaMA-Alpaca-2? MiniMax-01: Repository for MiniMax-Text-01 and MiniMax-VL-01 models based on Linear Attention. Chinese-LLaMA-Alpaca-2: Chinese LLaMA-2 & Alpaca-2 LLMs with 64K long context models. See the comparison table for live GitHub stats and shared categories. ### When should I choose MiniMax-01 over Chinese-LLaMA-Alpaca-2? Choose MiniMax-01 over Chinese-LLaMA-Alpaca-2 when License: MiniMax-01 is MIT, Chinese-LLaMA-Alpaca-2 is Apache-2.0; Tags unique to MiniMax-01: large-language-models, llm, vision-language-model, vlm; When high throughput performance is required for model serving. ### When should I choose Chinese-LLaMA-Alpaca-2 over MiniMax-01? Choose Chinese-LLaMA-Alpaca-2 over MiniMax-01 when License: Chinese-LLaMA-Alpaca-2 is Apache-2.0, MiniMax-01 is MIT; Tags unique to Chinese-LLaMA-Alpaca-2: 64k, alpaca, alpaca-2, flash-attention; Also covers Inference & Serving; When you need Chinese-specific large language models with long context capabilities up to 64K. ### When should I avoid MiniMax-01? If deep customization of attention mechanisms aside from Linear Attention is needed In favor of frameworks lacking vLLM's optimization features, when efficiency or memory use are secondary to flexibility ### When should I avoid Chinese-LLaMA-Alpaca-2? If you need models that support languages other than Chinese, as this repository focuses on Chinese models. If you require models with shorter context lengths than 4K, as the standard models support 4K and the long context models support 16K and 64K. If you are looking for models that do not include human preference alignment (RLHF), as this repository includes models that have been fine-tuned for this purpose. ### Is MiniMax-01 or Chinese-LLaMA-Alpaca-2 more popular on GitHub? Chinese-LLaMA-Alpaca-2 has more GitHub stars (7,118 vs 3,463). Stars measure visibility, not whether either tool fits your constraints. ### Are MiniMax-01 and Chinese-LLaMA-Alpaca-2 open source? Yes - both are open-source projects on GitHub (MiniMax-01: MIT, Chinese-LLaMA-Alpaca-2: Apache-2.0). ### Where can I find alternatives to MiniMax-01 or Chinese-LLaMA-Alpaca-2? GraphCanon lists graph-backed alternatives at [MiniMax-01 alternatives](/tools/minimax-ai-minimax-01/alternatives) and [Chinese-LLaMA-Alpaca-2 alternatives](/tools/ymcui-chinese-llama-alpaca-2/alternatives) ([MiniMax-01 markdown twin](/tools/minimax-ai-minimax-01/alternatives.md), [Chinese-LLaMA-Alpaca-2 markdown twin](/tools/ymcui-chinese-llama-alpaca-2/alternatives.md)), 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](/compare/minimax-ai-minimax-01-vs-ymcui-chinese-llama-alpaca-2.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, MiniMax-01 or Chinese-LLaMA-Alpaca-2? MiniMax-01: Dormant. Chinese-LLaMA-Alpaca-2: Slowing. 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 MiniMax-01 and Chinese-LLaMA-Alpaca-2? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [MiniMax-01 trust report](/tools/minimax-ai-minimax-01/trust); [Chinese-LLaMA-Alpaca-2 trust report](/tools/ymcui-chinese-llama-alpaca-2/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=minimax-ai-minimax-01`](/api/graphcanon/graph?tool=minimax-ai-minimax-01) - 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/_