Comparison
alpaca-lora vs exllama
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 exllama if exLlama provides a memory-efficient implementation of the LLaMa model with support for quantized weights, primarily aimed at users with NVIDIA GPUs from the 30-series onwards.
Markdown twin · alpaca-lora alternatives · exllama alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | alpaca-lora | exllama |
|---|---|---|
| Maintenance | Dormant (734d since push) As of 2w · github_public_v1 | Dormant (1041d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Personal account As of 2w · github_public_v1 |
| OSV dependency advisories | Published findings 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 |
Tagline
- alpaca-lora
- Instruct-tune LLaMA on consumer hardware
- exllama
- Memory-efficient rewrite of HF transformers for Llama with quantized weights
Stars
- alpaca-lora
- 19k
- exllama
- 2.9k
Forks
- alpaca-lora
- 2.2k
- exllama
- 220
Open issues
- alpaca-lora
- 365
- exllama
- 65
Language
- alpaca-lora
- Jupyter Notebook
- exllama
- Python
Adopt for
- alpaca-lora
- alpaca-lora is an instruct tuning repository for the LLaMA model designed to work with consumer-grade hardware through Docker integration.
- exllama
- ExLlama provides a memory-efficient implementation of the LLaMa model with support for quantized weights, primarily aimed at users with NVIDIA GPUs from the 30-series onwards.
Persona
- alpaca-lora
- developer harness
- exllama
- -
Runtime
- alpaca-lora
- -
- exllama
- -
License
- 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.
- exllama
- MIT
Last pushed
- alpaca-lora
- Jul 29, 2024
- exllama
- Sep 30, 2023
Categories
- alpaca-lora
- Inference & Serving, LLM Frameworks, Model Training
- exllama
- Inference & Serving, LLM Frameworks
Trust and health
Days since push
- alpaca-lora
- 734d
- exllama
- 1041d
Open issues (now)
- alpaca-lora
- 365
- exllama
- 65
Full report
- alpaca-lora
- Trust report
- exllama
- Trust report
Choose alpaca-lora if…
- alpaca-lora is primarily Jupyter Notebook; exllama is Python.
- License: alpaca-lora is Apache-2.0, exllama is MIT.
- 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, instruct-tune, llama, lora.
- Also covers Model Training.
- 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.
Choose exllama if…
- exllama is primarily Python; alpaca-lora is Jupyter Notebook.
- License: exllama is MIT, alpaca-lora is Apache-2.0.
- Tags unique to exllama: llama model, memory-efficient, nvidia gpu, python.
- - When deploying LLaMa models on NVIDIA GPUs from the 30-series or later that have strong FP16 support.
When NOT to use exllama
- - If you are operating older GPUs such as Pascal series, which lack robust FP16 support; alternatives like AutoGPTQ might perform better.
- - In scenarios that involve AMD GPU hardware (due to limited testing and optimization efforts).
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (tloen/alpaca-lora) · observed Aug 3, 2026
- GitHub forks (tloen/alpaca-lora) · observed Aug 3, 2026
- Last push (tloen/alpaca-lora) · observed Jul 29, 2024
- License file (Apache-2.0) · observed Aug 3, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (turboderp/exllama) · observed Aug 7, 2026
- GitHub forks (turboderp/exllama) · observed Aug 7, 2026
- Last push (turboderp/exllama) · observed Sep 30, 2023
- License file (MIT) · observed Aug 7, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: alpaca-lora 19k · exllama 2.9k (synced Aug 3, 2026).
Common questions
- What is the difference between alpaca-lora and exllama?
- alpaca-lora: Instruct-tune LLaMA on consumer hardware. exllama: Memory-efficient rewrite of HF transformers for Llama with quantized weights. See the comparison table for live GitHub stats and shared categories.
- When should I choose alpaca-lora over exllama?
- Choose alpaca-lora over exllama when alpaca-lora is primarily Jupyter Notebook; exllama is Python; License: alpaca-lora is Apache-2.0, exllama is MIT; 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, instruct-tune, llama, lora; Also covers Model Training; 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 exllama over alpaca-lora?
- Choose exllama over alpaca-lora when exllama is primarily Python; alpaca-lora is Jupyter Notebook; License: exllama is MIT, alpaca-lora is Apache-2.0; Tags unique to exllama: llama model, memory-efficient, nvidia gpu, python; - When deploying LLaMa models on NVIDIA GPUs from the 30-series or later that have strong FP16 support.
- When should I avoid alpaca-lora?
- When you require more advanced customization beyond what is offered through the
finetune.pyscript 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 exllama?
- - If you are operating older GPUs such as Pascal series, which lack robust FP16 support; alternatives like AutoGPTQ might perform better. - In scenarios that involve AMD GPU hardware (due to limited testing and optimization efforts).
- Is alpaca-lora or exllama more popular on GitHub?
- alpaca-lora has more GitHub stars (18,912 vs 2,937). Stars measure visibility, not whether either tool fits your constraints.
- Are alpaca-lora and exllama open source?
- Yes - both are open-source projects on GitHub (alpaca-lora: Apache-2.0, exllama: MIT).
- Where can I find alternatives to alpaca-lora or exllama?
- GraphCanon lists graph-backed alternatives at alpaca-lora alternatives and exllama alternatives (alpaca-lora markdown twin, exllama 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, alpaca-lora or exllama?
- alpaca-lora: Dormant. exllama: 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 alpaca-lora and exllama?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: alpaca-lora trust report; exllama trust report.