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Comparison

surogate vs alpaca-lora

Verdict

Pick surogate if surogate is a C++-based repository that accelerates training and fine-tuning for generative AI models using CUDA on NVIDIA GPUs; 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 · surogate alternatives · alpaca-lora alternatives

GraphCanon updated 1d

surogate logo

surogate

invergent-ai/surogate

★ 813pushed Aug 23, 2026
vs
alpaca-lora logo

alpaca-lora

tloen/alpaca-lora

★ 19kpushed Jul 29, 2024

Trust & integrity

Signalsurogatealpaca-lora
Maintenance
Very active (1d since push)
As of 1d · github_public_v1
Dormant (734d since push)
As of 3w · github_public_v1
Provenance
Not a fork · Organization account
As of 1d · 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
surogate trust reportalpaca-lora trust reportTrust methodology

Tagline

surogate
Training/Fine-tuning at the speed of light
alpaca-lora
Instruct-tune LLaMA on consumer hardware

Stars

surogate
813
alpaca-lora
19k

Forks

surogate
8
alpaca-lora
2.2k

Open issues

surogate
7
alpaca-lora
365

Language

surogate
C++
alpaca-lora
Jupyter Notebook

Adopt for

surogate
surogate is a C++-based repository that accelerates training and fine-tuning for generative AI models using CUDA on NVIDIA GPUs
alpaca-lora
alpaca-lora is an instruct tuning repository for the LLaMA model designed to work with consumer-grade hardware through Docker integration.

Persona

surogate
-
alpaca-lora
developer harness

Runtime

surogate
-
alpaca-lora
-

License

surogate
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

surogate
Aug 23, 2026
alpaca-lora
Jul 29, 2024

Categories

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

Trust and health

Maintenance

surogate
Very active (96%)
alpaca-lora
Dormant (18%)

Days since push

surogate
1d
alpaca-lora
734d

Open issues (now)

surogate
7
alpaca-lora
365

Stars delta

surogate
+7 (30d)
alpaca-lora
Unknown

Open issues delta

surogate
+1 (30d)
alpaca-lora
Unknown

Owner type

surogate
Organization
alpaca-lora
User

OSV dependency advisories

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

Full report

surogate
Trust report
alpaca-lora
Trust report

Choose surogate if…

  • surogate is primarily C++; alpaca-lora is Jupyter Notebook.
  • Tags unique to surogate: cuda, deep-learning, fine-tuning, generative-ai.
  • When needing rapid training and fine-tuning capabilities for generative AI models that take full advantage of NVIDIA GPU acceleration via CUDA.

When NOT to use surogate

  • If working in an environment without access to NVIDIA GPUs, as surogate leverages CUDA for its speed optimizations specifically designed for these hardware configurations.
  • When looking to use a more accessible language like Python for training and fine-tuning, since surogate is based on C++ which may offer less ease-of-use.
GitHubDetails

Choose alpaca-lora if…

  • alpaca-lora is primarily Jupyter Notebook; surogate is C++.
  • 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, 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 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

surogate trust report →alpaca-lora trust report →Model Training category →Inference & Serving category →LLM Frameworks 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: surogate 813 · alpaca-lora 19k (synced Aug 24, 2026).

Common questions

What is the difference between surogate and alpaca-lora?
surogate: Training/Fine-tuning at the speed of light. alpaca-lora: Instruct-tune LLaMA on consumer hardware. See the comparison table for live GitHub stats and shared categories.
When should I choose surogate over alpaca-lora?
Choose surogate over alpaca-lora when surogate is primarily C++; alpaca-lora is Jupyter Notebook; Tags unique to surogate: cuda, deep-learning, fine-tuning, generative-ai; When needing rapid training and fine-tuning capabilities for generative AI models that take full advantage of NVIDIA GPU acceleration via CUDA.
When should I choose alpaca-lora over surogate?
Choose alpaca-lora over surogate when alpaca-lora is primarily Jupyter Notebook; surogate is C++; 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, 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 surogate?
If working in an environment without access to NVIDIA GPUs, as surogate leverages CUDA for its speed optimizations specifically designed for these hardware configurations. When looking to use a more accessible language like Python for training and fine-tuning, since surogate is based on C++ which may offer less ease-of-use.
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 surogate or alpaca-lora more popular on GitHub?
alpaca-lora has more GitHub stars (18,912 vs 813). Stars measure visibility, not whether either tool fits your constraints.
Are surogate and alpaca-lora open source?
Yes - both are open-source projects on GitHub (surogate: Apache-2.0, alpaca-lora: Apache-2.0).
Where can I find alternatives to surogate or alpaca-lora?
GraphCanon lists graph-backed alternatives at surogate alternatives and alpaca-lora alternatives (surogate 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, surogate or alpaca-lora?
surogate: Very active. 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 surogate and alpaca-lora?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: surogate trust report; alpaca-lora trust report.

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