---
title: "alpaca-lora vs Chinese-LLaMA-Alpaca"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/tloen-alpaca-lora-vs-ymcui-chinese-llama-alpaca"
tools: ["tloen-alpaca-lora", "ymcui-chinese-llama-alpaca"]
---

# alpaca-lora vs Chinese-LLaMA-Alpaca

*GraphCanon updated Aug 17, 2026*

## Verdict

Pick alpaca-lora if alpaca-lora is an instruct tuning repository for the LLaMA model designed to work with consumer-grade hardware through Docker integration; pick Chinese-LLaMA-Alpaca if `Chinese-LLaMA-Alpaca` is a repository that includes Chinese versions of the LLaMA and Alpaca large language models, specifically designed with extended Chinese vocabulary and fine-tuned on Chinese instruction data for a.

[alpaca-lora](https://github.com/tloen/alpaca-lora) reports 19k GitHub stars, 2.2k forks, and 365 open issues, last pushed Jul 29, 2024. [Chinese-LLaMA-Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca/wiki) has 19k stars, 1.8k forks, and 6 open issues, last pushed Apr 19, 2026. Figures are from public GitHub metadata via [alpaca-lora's repository](https://github.com/tloen/alpaca-lora) and [Chinese-LLaMA-Alpaca's repository](https://github.com/ymcui/Chinese-LLaMA-Alpaca).

| | [alpaca-lora](/tools/tloen-alpaca-lora.md) | [Chinese-LLaMA-Alpaca](/tools/ymcui-chinese-llama-alpaca.md) |
| --- | --- | --- |
| Tagline | Instruct-tune LLaMA on consumer hardware | Chinese LLaMA & Alpaca Large Language Models for Local CPU/GPU Training and Deployment |
| Stars | 18,912 | 18,933 |
| Forks | 2,180 | 1,839 |
| Open issues | 365 | 6 |
| Language | Jupyter Notebook | Python |
| Adopt for | alpaca-lora is an instruct tuning repository for the LLaMA model designed to work with consumer-grade hardware through Docker integration. | `Chinese-LLaMA-Alpaca` is a repository that includes Chinese versions of the LLaMA and Alpaca large language models, specifically designed with extended Chinese vocabulary and fine-tuned on Chinese instruction data for a |
| Persona | developer harness | - |
| Runtime | - | - |
| License | The Apache-2.0 license applies, allowing wide-ranging reuse and distribution of the software, provided that copyright notices are included and applicable files accompany distributed executables. | The repository is licensed under Apache-2.0 which allows you to use the content openly in both commercial and non-commercial projects provided you adhere to the licensing terms. |
| Categories | Inference & Serving, LLM Frameworks, Model Training | LLM Frameworks, Model Training |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [alpaca-lora](/tools/tloen-alpaca-lora.md) | [Chinese-LLaMA-Alpaca](/tools/ymcui-chinese-llama-alpaca.md) |
| --- | --- | --- |
| Maintenance | Dormant (18%) | Slowing (36%) |
| Days since push | 734d | 119d |
| Open issues (now) | 365 | 6 |
| Stars delta | Unknown | -9 (30d) |
| Open issues delta | Unknown | 0 (30d) |
| Full report | [trust report](/tools/tloen-alpaca-lora/trust.md) | [trust report](/tools/ymcui-chinese-llama-alpaca/trust.md) |

## Decision facts: alpaca-lora

- **Pricing:** freemium - The source code is freely available under the Apache-2.0 license, but costs associated with hardware and cloud services for running Docker may apply.
- **Adopt for:** alpaca-lora is an instruct tuning repository for the LLaMA model designed to work with consumer-grade hardware through Docker integration.
- **License detail:** The Apache-2.0 license applies, allowing wide-ranging reuse and distribution of the software, provided that copyright notices are included and applicable files accompany distributed executables.
- **Persona:** developer harness

## Decision facts: Chinese-LLaMA-Alpaca

- **Pricing:** freemium - Access to `Chinese-LLaMA-Alpaca` models and documentation is free, but commercial usage should adhere to open-source licensing requirements.
- **Adopt for:** `Chinese-LLaMA-Alpaca` is a repository that includes Chinese versions of the LLaMA and Alpaca large language models, specifically designed with extended Chinese vocabulary and fine-tuned on Chinese instruction data for a
- **License detail:** The repository is licensed under Apache-2.0 which allows you to use the content openly in both commercial and non-commercial projects provided you adhere to the licensing terms.

## Choose when

### Choose alpaca-lora if…

- alpaca-lora is primarily Jupyter Notebook; Chinese-LLaMA-Alpaca is Python.
- Pricing: The source code is freely available under the Apache-2.0 license, but costs associated with hardware and cloud services for running Docker may apply..
- Tags unique to alpaca-lora: consumer hardware, docker, instruct-tune, lora.
- Also covers Inference & Serving.
- alpaca-lora ships Docker support for self-hosted deployment.
- When you have limited GPU resources but want to perform instruction-fine-tuning on the LLaMA model, and your setup supports basic Docker.

### Choose Chinese-LLaMA-Alpaca if…

- Chinese-LLaMA-Alpaca is primarily Python; alpaca-lora is Jupyter Notebook.
- Pricing: Access to `Chinese-LLaMA-Alpaca` models and documentation is free, but commercial usage should adhere to open-source licensing requirements..
- Tags unique to Chinese-LLaMA-Alpaca: alpaca, large language models, nlp, pre-trained-language-models.
- You should consider using `Chinese-LLaMA-Alpaca` if your project involves local deployment and training using CPUs or GPUs, particularly if the focus is on Chinese language texts. These models are pre

## When NOT to use alpaca-lora

- When you require more advanced customization beyond what is offered through the `finetune.py` script parameters or Jupyter Notebook interface.
- For teams with high-performance computing resources aiming for optimal performance, as alpaca-lora is optimized for use on consumer-grade hardware.

## When NOT to use Chinese-LLaMA-Alpaca

- `Chinese-LLaMA-Alpaca` might not be the best fit if your project requires extensive multi-language support beyond Chinese.
- If you are exclusively targeting markets where English is the primary language, and fine-tuning or additional pre-training on Chinese data does not add value to your application.
- Avoid using this tool if you require a model that has been extensively trained across global datasets for broad international comprehension, as `Chinese-LLaMA-Alpaca` is focused heavily on Chinese.

## Common questions

### What is the difference between alpaca-lora and Chinese-LLaMA-Alpaca?

alpaca-lora: Instruct-tune LLaMA on consumer hardware. Chinese-LLaMA-Alpaca: Chinese LLaMA & Alpaca Large Language Models for Local CPU/GPU Training and Deployment. See the comparison table for live GitHub stats and shared categories.

### When should I choose alpaca-lora over Chinese-LLaMA-Alpaca?

Choose alpaca-lora over Chinese-LLaMA-Alpaca when alpaca-lora is primarily Jupyter Notebook; Chinese-LLaMA-Alpaca is Python; Pricing: The source code is freely available under the Apache-2.0 license, but costs associated with hardware and cloud services for running Docker may apply.; Tags unique to alpaca-lora: consumer hardware, docker, instruct-tune, lora; Also covers Inference & Serving; alpaca-lora ships Docker support for self-hosted deployment; When you have limited GPU resources but want to perform instruction-fine-tuning on the LLaMA model, and your setup supports basic Docker.

### When should I choose Chinese-LLaMA-Alpaca over alpaca-lora?

Choose Chinese-LLaMA-Alpaca over alpaca-lora when Chinese-LLaMA-Alpaca is primarily Python; alpaca-lora is Jupyter Notebook; Pricing: Access to `Chinese-LLaMA-Alpaca` models and documentation is free, but commercial usage should adhere to open-source licensing requirements.; Tags unique to Chinese-LLaMA-Alpaca: alpaca, large language models, nlp, pre-trained-language-models; You should consider using `Chinese-LLaMA-Alpaca` if your project involves local deployment and training using CPUs or GPUs, particularly if the focus is on Chinese language texts. These models are pre.

### When should I avoid alpaca-lora?

When you require more advanced customization beyond what is offered through the `finetune.py` script parameters or Jupyter Notebook interface. For teams with high-performance computing resources aiming for optimal performance, as alpaca-lora is optimized for use on consumer-grade hardware.

### When should I avoid Chinese-LLaMA-Alpaca?

`Chinese-LLaMA-Alpaca` might not be the best fit if your project requires extensive multi-language support beyond Chinese. If you are exclusively targeting markets where English is the primary language, and fine-tuning or additional pre-training on Chinese data does not add value to your application. Avoid using this tool if you require a model that has been extensively trained across global datasets for broad international comprehension, as `Chinese-LLaMA-Alpaca` is focused heavily on Chinese.

### Is alpaca-lora or Chinese-LLaMA-Alpaca more popular on GitHub?

Chinese-LLaMA-Alpaca has more GitHub stars (18,933 vs 18,912). Stars measure visibility, not whether either tool fits your constraints.

### Are alpaca-lora and Chinese-LLaMA-Alpaca open source?

Yes - both are open-source projects on GitHub (alpaca-lora: Apache-2.0, Chinese-LLaMA-Alpaca: Apache-2.0).

### Where can I find alternatives to alpaca-lora or Chinese-LLaMA-Alpaca?

GraphCanon lists graph-backed alternatives at [alpaca-lora alternatives](/tools/tloen-alpaca-lora/alternatives) and [Chinese-LLaMA-Alpaca alternatives](/tools/ymcui-chinese-llama-alpaca/alternatives) ([alpaca-lora markdown twin](/tools/tloen-alpaca-lora/alternatives.md), [Chinese-LLaMA-Alpaca markdown twin](/tools/ymcui-chinese-llama-alpaca/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/tloen-alpaca-lora-vs-ymcui-chinese-llama-alpaca.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, alpaca-lora or Chinese-LLaMA-Alpaca?

alpaca-lora: Dormant. Chinese-LLaMA-Alpaca: 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 alpaca-lora and Chinese-LLaMA-Alpaca?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [alpaca-lora trust report](/tools/tloen-alpaca-lora/trust); [Chinese-LLaMA-Alpaca trust report](/tools/ymcui-chinese-llama-alpaca/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=tloen-alpaca-lora`](/api/graphcanon/graph?tool=tloen-alpaca-lora)
- 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/_