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

# qlora vs Chinese-LLaMA-Alpaca

*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 if chinese-LLaMA-Alpaca offers Chinese versions of LLaMA and Alpaca models, with expanded Chinese vocabulary and pre-training on Chinese data, supporting local deployment on CPU/GPU.

[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](https://github.com/ymcui/Chinese-LLaMA-Alpaca/wiki) has 19k stars, 1.8k forks, and 5 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's repository](https://github.com/ymcui/Chinese-LLaMA-Alpaca).

| | [qlora](/tools/artidoro-qlora.md) | [Chinese-LLaMA-Alpaca](/tools/ymcui-chinese-llama-alpaca.md) |
| --- | --- | --- |
| Tagline | QLoRA finetuning of quantized LLMs | Chinese LLaMA & Alpaca LLMs |
| Stars | 11,021 | 18,943 |
| Forks | 876 | 1,836 |
| Open issues | 207 | 5 |
| 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 offers Chinese versions of LLaMA and Alpaca models, with expanded Chinese vocabulary and pre-training on Chinese data, supporting local deployment on CPU/GPU. |
| 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 | The tool is licensed under Apache-2.0, allowing for free use, modification, and distribution. |
| 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](/tools/ymcui-chinese-llama-alpaca.md) |
| --- | --- | --- |
| Maintenance | Dormant (18%) | Slowing (36%) |
| Days since push | 837d | 152d |
| Open issues (now) | 207 | 5 |
| Stars delta | +42 (30d) | +10 (30d) |
| Open issues delta | +1 (30d) | -1 (30d) |
| Full report | [trust report](/tools/artidoro-qlora/trust.md) | [trust report](/tools/ymcui-chinese-llama-alpaca/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

- **Pricing:** freemium - The repository provides free access to the models and scripts, but users may incur costs related to hardware and cloud services for deployment.
- **Requirements:** Min 8 GB RAM; Requires Python environment.; Supports deployment on local CPU/GPU.
- **Adopt for:** Chinese-LLaMA-Alpaca offers Chinese versions of LLaMA and Alpaca models, with expanded Chinese vocabulary and pre-training on Chinese data, supporting local deployment on CPU/GPU.
- **License detail:** The tool is licensed under Apache-2.0, allowing for free use, modification, and distribution.

## Choose when

### Choose qlora if…

- qlora is primarily Jupyter Notebook; Chinese-LLaMA-Alpaca is Python.
- License: qlora is MIT, Chinese-LLaMA-Alpaca 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.
- Need efficient fine-tuning for quantized LLaMA-based models

### Choose Chinese-LLaMA-Alpaca if…

- Chinese-LLaMA-Alpaca is primarily Python; qlora is Jupyter Notebook.
- License: Chinese-LLaMA-Alpaca is Apache-2.0, qlora is MIT.
- Pricing: The repository provides free access to the models and scripts, but users may incur costs related to hardware and cloud services for deployment..
- Requirements: Min 8 GB RAM; Requires Python environment.; Supports deployment on local CPU/GPU..
- Tags unique to Chinese-LLaMA-Alpaca: alpaca, llama, llm, lora.
- Also covers Inference & Serving.
- When you need Chinese-specific large language models for tasks that require deep understanding of the Chinese language.

## 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

- If your project requires models that are not specific to the Chinese language or do not benefit from Chinese-specific pre-training.
- When you need models that are not available in the sizes provided by Chinese-LLaMA-Alpaca, such as models smaller than 7B or larger than 33B.
- If you prefer models that are not based on LLaMA or Alpaca architectures.

## Common questions

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

qlora: QLoRA finetuning of quantized LLMs. Chinese-LLaMA-Alpaca: Chinese LLaMA & Alpaca LLMs. See the comparison table for live GitHub stats and shared categories.

### When should I choose qlora over Chinese-LLaMA-Alpaca?

Choose qlora over Chinese-LLaMA-Alpaca when qlora is primarily Jupyter Notebook; Chinese-LLaMA-Alpaca is Python; License: qlora is MIT, Chinese-LLaMA-Alpaca 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; Need efficient fine-tuning for quantized LLaMA-based models.

### When should I choose Chinese-LLaMA-Alpaca over qlora?

Choose Chinese-LLaMA-Alpaca over qlora when Chinese-LLaMA-Alpaca is primarily Python; qlora is Jupyter Notebook; License: Chinese-LLaMA-Alpaca is Apache-2.0, qlora is MIT; Pricing: The repository provides free access to the models and scripts, but users may incur costs related to hardware and cloud services for deployment.; Requirements: Min 8 GB RAM; Requires Python environment.; Supports deployment on local CPU/GPU.; Tags unique to Chinese-LLaMA-Alpaca: alpaca, llama, llm, lora; Also covers Inference & Serving; When you need Chinese-specific large language models for tasks that require deep understanding of the Chinese language.

### 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?

If your project requires models that are not specific to the Chinese language or do not benefit from Chinese-specific pre-training. When you need models that are not available in the sizes provided by Chinese-LLaMA-Alpaca, such as models smaller than 7B or larger than 33B. If you prefer models that are not based on LLaMA or Alpaca architectures.

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

Chinese-LLaMA-Alpaca has more GitHub stars (18,943 vs 11,021). Stars measure visibility, not whether either tool fits your constraints.

### Are qlora and Chinese-LLaMA-Alpaca open source?

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

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

GraphCanon lists graph-backed alternatives at [qlora alternatives](/tools/artidoro-qlora/alternatives) and [Chinese-LLaMA-Alpaca alternatives](/tools/ymcui-chinese-llama-alpaca/alternatives) ([qlora markdown twin](/tools/artidoro-qlora/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/artidoro-qlora-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, qlora or Chinese-LLaMA-Alpaca?

qlora: 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 qlora and Chinese-LLaMA-Alpaca?

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