--- title: "qlora vs Chinese-LLaMA-Alpaca-2" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/artidoro-qlora-vs-ymcui-chinese-llama-alpaca-2" tools: ["artidoro-qlora", "ymcui-chinese-llama-alpaca-2"] --- # qlora vs Chinese-LLaMA-Alpaca-2 *GraphCanon updated Sep 26, 2026* ## Verdict Pick qlora if qLoRA specializes in accelerating the fine-tuning process of quantized large language models like those in the Guanaco family; 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. [qlora](https://arxiv.org/abs/2305.14314) reports 11k GitHub stars, 876 forks, and 207 open issues, last pushed Jun 10, 2024. [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 [qlora's repository](https://github.com/artidoro/qlora) and [Chinese-LLaMA-Alpaca-2's repository](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2). | | [qlora](/tools/artidoro-qlora.md) | [Chinese-LLaMA-Alpaca-2](/tools/ymcui-chinese-llama-alpaca-2.md) | | --- | --- | --- | | Tagline | QLoRA finetuning of quantized LLMs | Chinese LLaMA-2 & Alpaca-2 LLMs with 64K long context models | | Stars | 11,021 | 7,118 | | Forks | 876 | 559 | | Open issues | 207 | 6 | | Language | Jupyter Notebook | Python | | Adopt for | QLoRA specializes in accelerating the fine-tuning process of quantized large language models like those in the Guanaco family. | 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 License; open-source tool for QLoRA fine-tuning process; LLaMA base models must be obtained legally as per their license terms | 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._ | | [qlora](/tools/artidoro-qlora.md) | [Chinese-LLaMA-Alpaca-2](/tools/ymcui-chinese-llama-alpaca-2.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 837d | 152d | | Open issues (now) | 207 | 6 | | Stars delta | +42 (30d) | -6 (30d) | | Open issues delta | +1 (30d) | 0 (30d) | | Full report | [trust report](/tools/artidoro-qlora/trust.md) | [trust report](/tools/ymcui-chinese-llama-alpaca-2/trust.md) | ## Decision facts: qlora - **Pricing:** freemium - Open source under MIT License; requires access to LLaMA base models - **Requirements:** Installation involves installing PyTorch and specific packages from source; Works with model sizes ranging from 7B to 65B, includes recommendations for tuning different sizes - **Adopt for:** QLoRA specializes in accelerating the fine-tuning process of quantized large language models like those in the Guanaco family. - **License detail:** MIT License; open-source tool for QLoRA fine-tuning process; LLaMA base models must be obtained legally as per their license terms ## 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 qlora if… - qlora is primarily Jupyter Notebook; Chinese-LLaMA-Alpaca-2 is Python. - License: qlora is MIT, Chinese-LLaMA-Alpaca-2 is Apache-2.0. - Pricing: Open source under MIT License; requires access to LLaMA base models. - Requirements: Installation involves installing PyTorch and specific packages from source; Works with model sizes ranging from 7B to 65B, includes recommendations for tuning different sizes. - Tags unique to qlora: fine-tuning, guanaco, llama models, quantization. - Need efficient fine-tuning for quantized LLaMA-based models ### Choose Chinese-LLaMA-Alpaca-2 if… - Chinese-LLaMA-Alpaca-2 is primarily Python; qlora is Jupyter Notebook. - License: Chinese-LLaMA-Alpaca-2 is Apache-2.0, qlora 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 qlora - Require native full-precision model tuning without efficiency constraints - Focusing on non-LLaMA-based language models where specific adaptations may not apply ## 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 qlora and Chinese-LLaMA-Alpaca-2? qlora: QLoRA finetuning of quantized LLMs. 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 qlora over Chinese-LLaMA-Alpaca-2? Choose qlora over Chinese-LLaMA-Alpaca-2 when qlora is primarily Jupyter Notebook; Chinese-LLaMA-Alpaca-2 is Python; License: qlora is MIT, Chinese-LLaMA-Alpaca-2 is Apache-2.0; Pricing: Open source under MIT License; requires access to LLaMA base models; Requirements: Installation involves installing PyTorch and specific packages from source; Works with model sizes ranging from 7B to 65B, includes recommendations for tuning different sizes; Tags unique to qlora: fine-tuning, guanaco, llama models, quantization; Need efficient fine-tuning for quantized LLaMA-based models. ### When should I choose Chinese-LLaMA-Alpaca-2 over qlora? Choose Chinese-LLaMA-Alpaca-2 over qlora when Chinese-LLaMA-Alpaca-2 is primarily Python; qlora is Jupyter Notebook; License: Chinese-LLaMA-Alpaca-2 is Apache-2.0, qlora 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 qlora? Require native full-precision model tuning without efficiency constraints Focusing on non-LLaMA-based language models where specific adaptations may not apply ### 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 qlora or Chinese-LLaMA-Alpaca-2 more popular on GitHub? qlora has more GitHub stars (11,021 vs 7,118). Stars measure visibility, not whether either tool fits your constraints. ### Are qlora and Chinese-LLaMA-Alpaca-2 open source? Yes - both are open-source projects on GitHub (qlora: MIT, Chinese-LLaMA-Alpaca-2: Apache-2.0). ### Where can I find alternatives to qlora or Chinese-LLaMA-Alpaca-2? GraphCanon lists graph-backed alternatives at [qlora alternatives](/tools/artidoro-qlora/alternatives) and [Chinese-LLaMA-Alpaca-2 alternatives](/tools/ymcui-chinese-llama-alpaca-2/alternatives) ([qlora markdown twin](/tools/artidoro-qlora/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/artidoro-qlora-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, qlora or Chinese-LLaMA-Alpaca-2? qlora: 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 qlora and Chinese-LLaMA-Alpaca-2? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [qlora trust report](/tools/artidoro-qlora/trust); [Chinese-LLaMA-Alpaca-2 trust report](/tools/ymcui-chinese-llama-alpaca-2/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=artidoro-qlora`](/api/graphcanon/graph?tool=artidoro-qlora) - 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/_