---
title: "P-tuning-v2 vs alpaca-lora"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/thudm-p-tuning-v2-vs-tloen-alpaca-lora"
tools: ["thudm-p-tuning-v2", "tloen-alpaca-lora"]
---

# P-tuning-v2 vs alpaca-lora

*GraphCanon updated Aug 3, 2026*

## Verdict

Pick P-tuning-v2 if p-tuning-v2 is an optimized deep prompt tuning strategy that can be used for tasks like natural language processing and parameter-efficient learning where few parameters need to be adjusted compared to full fine-tuning; 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.

[P-tuning-v2](https://github.com/THUDM/P-tuning-v2) reports 2.1k GitHub stars, 213 forks, and 35 open issues, last pushed Nov 16, 2023. [alpaca-lora](https://github.com/tloen/alpaca-lora) has 19k stars, 2.2k forks, and 365 open issues, last pushed Jul 29, 2024. Figures are from public GitHub metadata via [P-tuning-v2's repository](https://github.com/THUDM/P-tuning-v2) and [alpaca-lora's repository](https://github.com/tloen/alpaca-lora).

| | [P-tuning-v2](/tools/thudm-p-tuning-v2.md) | [alpaca-lora](/tools/tloen-alpaca-lora.md) |
| --- | --- | --- |
| Tagline | Optimized deep prompt tuning strategy comparable to fine-tuning across scales and tasks | Instruct-tune LLaMA on consumer hardware |
| Stars | 2,077 | 18,912 |
| Forks | 213 | 2,180 |
| Open issues | 35 | 365 |
| Language | Python | Jupyter Notebook |
| Adopt for | P-tuning-v2 is an optimized deep prompt tuning strategy that can be used for tasks like natural language processing and parameter-efficient learning where few parameters need to be adjusted compared to full fine-tuning. | alpaca-lora is an instruct tuning repository for the LLaMA model designed to work with consumer-grade hardware through Docker integration. |
| Persona | - | developer harness |
| Runtime | - | - |
| License | P-tuning-v2 is provided under the Apache-2.0 license which permits free use, modification and distribution as long as copyright and license notice are preserved. | 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. |
| Categories | Model Training | Inference & Serving, LLM Frameworks, Model Training |

## Trust and health

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

| | [P-tuning-v2](/tools/thudm-p-tuning-v2.md) | [alpaca-lora](/tools/tloen-alpaca-lora.md) |
| --- | --- | --- |
| Days since push | 990d | 734d |
| Open issues (now) | 35 | 365 |
| Owner type | Organization | User |
| Full report | [trust report](/tools/thudm-p-tuning-v2/trust.md) | [trust report](/tools/tloen-alpaca-lora/trust.md) |

## Decision facts: P-tuning-v2

- **Requirements:** Min 8 GB RAM; Experiments were conducted on NVIDIA DGX-A100; however, RTX 3090 or RTX 2080 Ti GPUs can suffice given certain conditions.; CUDA version should be at least 11.1 and specific versions of pytorch (1.7.1), torchvision (0.8.2) and torchaudio (0.7.2).; A conda environment setup with Python 3.8.5 is recommended for optimal performance.
- **Adopt for:** P-tuning-v2 is an optimized deep prompt tuning strategy that can be used for tasks like natural language processing and parameter-efficient learning where few parameters need to be adjusted compared to full fine-tuning.
- **License detail:** P-tuning-v2 is provided under the Apache-2.0 license which permits free use, modification and distribution as long as copyright and license notice are preserved.

## 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

## Choose when

### Choose P-tuning-v2 if…

- P-tuning-v2 is primarily Python; alpaca-lora is Jupyter Notebook.
- Requirements: Min 8 GB RAM; Experiments were conducted on NVIDIA DGX-A100; however, RTX 3090 or RTX 2080 Ti GPUs can suffice given certain conditions.; CUDA version should be at least 11.1 and specific versions of pytorch (1.7.1), torchvision (0.8.2) and torchaudio (0.7.2).; A conda environment setup with Python 3.8.5 is recommended for optimal performance..
- Tags unique to P-tuning-v2: natural-language-processing, p-tuning, parameter-efficient-learning, pretrained-language-model.
- For scenarios requiring efficient use of resources, as P-tuning v2 requires fewer parameters than traditional fine-tuning methods.

### Choose alpaca-lora if…

- alpaca-lora is primarily Jupyter Notebook; P-tuning-v2 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, llama.
- Also covers Inference & Serving, LLM Frameworks.
- 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 NOT to use P-tuning-v2

- Avoid using P-tuning v2 when you have large-scale datasets and model architectures, as it might not be as effective compared to full fine-tuning methods.
- If your project requires a high level of customization beyond prompt tuning or continuous prompts do not align with the problem complexity.

## 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.

## Common questions

### What is the difference between P-tuning-v2 and alpaca-lora?

P-tuning-v2: Optimized deep prompt tuning strategy comparable to fine-tuning across scales and tasks. alpaca-lora: Instruct-tune LLaMA on consumer hardware. See the comparison table for live GitHub stats and shared categories.

### When should I choose P-tuning-v2 over alpaca-lora?

Choose P-tuning-v2 over alpaca-lora when P-tuning-v2 is primarily Python; alpaca-lora is Jupyter Notebook; Requirements: Min 8 GB RAM; Experiments were conducted on NVIDIA DGX-A100; however, RTX 3090 or RTX 2080 Ti GPUs can suffice given certain conditions.; CUDA version should be at least 11.1 and specific versions of pytorch (1.7.1), torchvision (0.8.2) and torchaudio (0.7.2).; A conda environment setup with Python 3.8.5 is recommended for optimal performance.; Tags unique to P-tuning-v2: natural-language-processing, p-tuning, parameter-efficient-learning, pretrained-language-model; For scenarios requiring efficient use of resources, as P-tuning v2 requires fewer parameters than traditional fine-tuning methods.

### When should I choose alpaca-lora over P-tuning-v2?

Choose alpaca-lora over P-tuning-v2 when alpaca-lora is primarily Jupyter Notebook; P-tuning-v2 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, llama; Also covers Inference & Serving, LLM Frameworks; 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 avoid P-tuning-v2?

Avoid using P-tuning v2 when you have large-scale datasets and model architectures, as it might not be as effective compared to full fine-tuning methods. If your project requires a high level of customization beyond prompt tuning or continuous prompts do not align with the problem complexity.

### 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.

### Is P-tuning-v2 or alpaca-lora more popular on GitHub?

alpaca-lora has more GitHub stars (18,912 vs 2,077). Stars measure visibility, not whether either tool fits your constraints.

### Are P-tuning-v2 and alpaca-lora open source?

Yes - both are open-source projects on GitHub (P-tuning-v2: Apache-2.0, alpaca-lora: Apache-2.0).

### Where can I find alternatives to P-tuning-v2 or alpaca-lora?

GraphCanon lists graph-backed alternatives at [P-tuning-v2 alternatives](/tools/thudm-p-tuning-v2/alternatives) and [alpaca-lora alternatives](/tools/tloen-alpaca-lora/alternatives) ([P-tuning-v2 markdown twin](/tools/thudm-p-tuning-v2/alternatives.md), [alpaca-lora markdown twin](/tools/tloen-alpaca-lora/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/thudm-p-tuning-v2-vs-tloen-alpaca-lora.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, P-tuning-v2 or alpaca-lora?

P-tuning-v2: Dormant. alpaca-lora: 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 P-tuning-v2 and alpaca-lora?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [P-tuning-v2 trust report](/tools/thudm-p-tuning-v2/trust); [alpaca-lora trust report](/tools/tloen-alpaca-lora/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=thudm-p-tuning-v2`](/api/graphcanon/graph?tool=thudm-p-tuning-v2)
- 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/_