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Comparison

LLM-Adapters vs alpaca-lora

Verdict

Pick LLM-Adapters if lLM-Adapters offers Python-based tools for efficient fine-tuning of language models with Apache-2.0 licensing; 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.

Markdown twin · LLM-Adapters alternatives · alpaca-lora alternatives

GraphCanon updated today

LLM-Adapters logo

LLM-Adapters

AGI-Edgerunners/LLM-Adapters

★ 1.2kpushed Mar 10, 2024
vs
alpaca-lora logo

alpaca-lora

tloen/alpaca-lora

★ 19kpushed Jul 29, 2024

Trust & integrity

SignalLLM-Adaptersalpaca-lora
Maintenance
Dormant (896d since push)
As of today · github_public_v1
Dormant (734d since push)
As of 3w · github_public_v1
Provenance
Not a fork · Organization account
As of today · github_public_v1
Not a fork · Personal account
As of 3w · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of 1mo · osv@v1
Published findings
As of 1mo · osv@v1
deps.dev advisories
Not queried
deps.dev@v1
Not queried
deps.dev@v1
OpenSSF Scorecard
Not queried
openssf-scorecard@v1
Not queried
openssf-scorecard@v1
LLM-Adapters trust reportalpaca-lora trust reportTrust methodology

Tagline

LLM-Adapters
Code for EMNLP 2023 Paper on Parameter-Efficient Fine-Tuning of LLMs
alpaca-lora
Instruct-tune LLaMA on consumer hardware

Stars

LLM-Adapters
1.2k
alpaca-lora
19k

Forks

LLM-Adapters
115
alpaca-lora
2.2k

Open issues

LLM-Adapters
55
alpaca-lora
365

Language

LLM-Adapters
Python
alpaca-lora
Jupyter Notebook

Adopt for

LLM-Adapters
LLM-Adapters offers Python-based tools for efficient fine-tuning of language models with Apache-2.0 licensing.
alpaca-lora
alpaca-lora is an instruct tuning repository for the LLaMA model designed to work with consumer-grade hardware through Docker integration.

Persona

LLM-Adapters
-
alpaca-lora
developer harness

Runtime

LLM-Adapters
-
alpaca-lora
-

License

LLM-Adapters
Apache-2.0
alpaca-lora
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.

Last pushed

LLM-Adapters
Mar 10, 2024
alpaca-lora
Jul 29, 2024

Categories

LLM-Adapters
LLM Frameworks, Model Training
alpaca-lora
Inference & Serving, LLM Frameworks, Model Training

Trust and health

Days since push

LLM-Adapters
896d
alpaca-lora
734d

Open issues (now)

LLM-Adapters
55
alpaca-lora
365

Stars delta

LLM-Adapters
-1 (30d)
alpaca-lora
Unknown

Open issues delta

LLM-Adapters
0 (30d)
alpaca-lora
Unknown

Owner type

LLM-Adapters
Organization
alpaca-lora
User

OSV dependency advisories

LLM-Adapters
No lockfile (source not queried)
alpaca-lora
Published findings

Full report

LLM-Adapters
Trust report
alpaca-lora
Trust report

Choose LLM-Adapters if…

  • LLM-Adapters is primarily Python; alpaca-lora is Jupyter Notebook.
  • Tags unique to LLM-Adapters: adapters, fine-tuning, large language models, parameter-efficient.
  • Optimizing resource usage when you need to fine-tune large language models without altering their core parameters

When NOT to use LLM-Adapters

  • You require a full retraining approach that modifies all model weights, not just adapters
  • Your project timeline does not allow for integrating and testing new methodologies from recent papers like EMNLP 2023
GitHubDetails

Choose alpaca-lora if…

  • alpaca-lora is primarily Jupyter Notebook; LLM-Adapters 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.
  • 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 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.
GitHubDetails

Explore

LLM-Adapters trust report →alpaca-lora trust report →LLM Frameworks category →Model Training category →Inference & Serving category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: LLM-Adapters 1.2k · alpaca-lora 19k (synced Aug 24, 2026).

Common questions

What is the difference between LLM-Adapters and alpaca-lora?
LLM-Adapters: Code for EMNLP 2023 Paper on Parameter-Efficient Fine-Tuning of LLMs. alpaca-lora: Instruct-tune LLaMA on consumer hardware. See the comparison table for live GitHub stats and shared categories.
When should I choose LLM-Adapters over alpaca-lora?
Choose LLM-Adapters over alpaca-lora when LLM-Adapters is primarily Python; alpaca-lora is Jupyter Notebook; Tags unique to LLM-Adapters: adapters, fine-tuning, large language models, parameter-efficient; Optimizing resource usage when you need to fine-tune large language models without altering their core parameters.
When should I choose alpaca-lora over LLM-Adapters?
Choose alpaca-lora over LLM-Adapters when alpaca-lora is primarily Jupyter Notebook; LLM-Adapters 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; 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 LLM-Adapters?
You require a full retraining approach that modifies all model weights, not just adapters Your project timeline does not allow for integrating and testing new methodologies from recent papers like EMNLP 2023
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 LLM-Adapters or alpaca-lora more popular on GitHub?
alpaca-lora has more GitHub stars (18,912 vs 1,233). Stars measure visibility, not whether either tool fits your constraints.
Are LLM-Adapters and alpaca-lora open source?
Yes - both are open-source projects on GitHub (LLM-Adapters: Apache-2.0, alpaca-lora: Apache-2.0).
Where can I find alternatives to LLM-Adapters or alpaca-lora?
GraphCanon lists graph-backed alternatives at LLM-Adapters alternatives and alpaca-lora alternatives (LLM-Adapters markdown twin, alpaca-lora markdown twin), 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 mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
Which is better maintained, LLM-Adapters or alpaca-lora?
LLM-Adapters: 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 LLM-Adapters and alpaca-lora?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: LLM-Adapters trust report; alpaca-lora trust report.

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