Comparison
OneCompression vs LLM-Finetuning-Toolkit
Verdict
Pick OneCompression if oneCompression is a Python library for compressing large language models via quantization, supporting CUDA on Linux and MPS on macOS; pick LLM-Finetuning-Toolkit if facilitates fine-tuning of open-source LLMs with features for ablation studies and unit testing.
Markdown twin · OneCompression alternatives · LLM-Finetuning-Toolkit alternatives
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Trust & integrity
| Signal | OneCompression | LLM-Finetuning-Toolkit |
|---|---|---|
| Maintenance | Very active (1d since push) As of 3w · github_public_v1 | Slowing (111d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) 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
- OneCompression
- Python package for LLM compression
- LLM-Finetuning-Toolkit
- Toolkit for fine-tuning and testing open-source large language models
Stars
- OneCompression
- 398
- LLM-Finetuning-Toolkit
- 870
Forks
- OneCompression
- 18
- LLM-Finetuning-Toolkit
- 107
Open issues
- OneCompression
- 7
- LLM-Finetuning-Toolkit
- 16
Language
- OneCompression
- Python
- LLM-Finetuning-Toolkit
- Python
Adopt for
- OneCompression
- OneCompression is a Python library for compressing large language models via quantization, supporting CUDA on Linux and MPS on macOS.
- LLM-Finetuning-Toolkit
- Facilitates fine-tuning of open-source LLMs with features for ablation studies and unit testing
Persona
- OneCompression
- -
- LLM-Finetuning-Toolkit
- -
Runtime
- OneCompression
- -
- LLM-Finetuning-Toolkit
- -
License
- OneCompression
- MIT
- LLM-Finetuning-Toolkit
- Apache-2.0
Last pushed
- OneCompression
- Jul 31, 2026
- LLM-Finetuning-Toolkit
- May 4, 2026
Categories
- OneCompression
- LLM Frameworks, Model Training
- LLM-Finetuning-Toolkit
- LLM Frameworks, Model Training
Trust and health
Maintenance
- OneCompression
- Very active (96%)
- LLM-Finetuning-Toolkit
- Slowing (36%)
Days since push
- OneCompression
- 1d
- LLM-Finetuning-Toolkit
- 111d
Open issues (now)
- OneCompression
- 7
- LLM-Finetuning-Toolkit
- 16
Stars delta
- OneCompression
- Unknown
- LLM-Finetuning-Toolkit
- -2 (30d)
Open issues delta
- OneCompression
- Unknown
- LLM-Finetuning-Toolkit
- 0 (30d)
Full report
- OneCompression
- Trust report
- LLM-Finetuning-Toolkit
- Trust report
Choose OneCompression if…
- License: OneCompression is MIT, LLM-Finetuning-Toolkit is Apache-2.0.
- Tags unique to OneCompression: compression, cuda, deepspeed, gptq.
- For CUDA quantum compression on Linux-based systems where PyTorch version 2.10 or later is required for vLLM serving with `cu130` index
When NOT to use OneCompression
- If your environment strictly requires CUDA versions other than 'cu130' as vLLM is only available with the latter
- When running on CPUs or non-Linux OS without NVIDIA GPU, since certain functionalities like vLLM serving and specific CUDA extras won't work
Choose LLM-Finetuning-Toolkit if…
- License: LLM-Finetuning-Toolkit is Apache-2.0, OneCompression is MIT.
- Tags unique to LLM-Finetuning-Toolkit: ablation-study, classification, falcon, fine-tuning.
- LLM-Finetuning-Toolkit ships Docker support for self-hosted deployment.
- When working specifically with Falcon, Flan-T5, LLama2, Mistral-7B or Zephyr models due to inbuilt support
When NOT to use LLM-Finetuning-Toolkit
- If prioritizing proprietary LLMs not listed as supported within the toolkit
- When working with languages other than Python, since toolkit is exclusively for Python environments
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (FujitsuResearch/OneCompression) · observed Aug 2, 2026
- GitHub forks (FujitsuResearch/OneCompression) · observed Aug 2, 2026
- Last push (FujitsuResearch/OneCompression) · observed Jul 31, 2026
- License file (MIT) · observed Aug 2, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (georgian-io/LLM-Finetuning-Toolkit) · observed Aug 24, 2026
- GitHub forks (georgian-io/LLM-Finetuning-Toolkit) · observed Aug 24, 2026
- Last push (georgian-io/LLM-Finetuning-Toolkit) · observed May 4, 2026
- License file (Apache-2.0) · observed Aug 24, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: OneCompression 398 · LLM-Finetuning-Toolkit 870 (synced Aug 2, 2026).
Common questions
- What is the difference between OneCompression and LLM-Finetuning-Toolkit?
- OneCompression: Python package for LLM compression. LLM-Finetuning-Toolkit: Toolkit for fine-tuning and testing open-source large language models. See the comparison table for live GitHub stats and shared categories.
- When should I choose OneCompression over LLM-Finetuning-Toolkit?
- Choose OneCompression over LLM-Finetuning-Toolkit when License: OneCompression is MIT, LLM-Finetuning-Toolkit is Apache-2.0; Tags unique to OneCompression: compression, cuda, deepspeed, gptq; For CUDA quantum compression on Linux-based systems where PyTorch version 2.10 or later is required for vLLM serving with
cu130index. - When should I choose LLM-Finetuning-Toolkit over OneCompression?
- Choose LLM-Finetuning-Toolkit over OneCompression when License: LLM-Finetuning-Toolkit is Apache-2.0, OneCompression is MIT; Tags unique to LLM-Finetuning-Toolkit: ablation-study, classification, falcon, fine-tuning; LLM-Finetuning-Toolkit ships Docker support for self-hosted deployment; When working specifically with Falcon, Flan-T5, LLama2, Mistral-7B or Zephyr models due to inbuilt support.
- When should I avoid OneCompression?
- If your environment strictly requires CUDA versions other than 'cu130' as vLLM is only available with the latter When running on CPUs or non-Linux OS without NVIDIA GPU, since certain functionalities like vLLM serving and specific CUDA extras won't work
- When should I avoid LLM-Finetuning-Toolkit?
- If prioritizing proprietary LLMs not listed as supported within the toolkit When working with languages other than Python, since toolkit is exclusively for Python environments
- Is OneCompression or LLM-Finetuning-Toolkit more popular on GitHub?
- LLM-Finetuning-Toolkit has more GitHub stars (870 vs 398). Stars measure visibility, not whether either tool fits your constraints.
- Are OneCompression and LLM-Finetuning-Toolkit open source?
- Yes - both are open-source projects on GitHub (OneCompression: MIT, LLM-Finetuning-Toolkit: Apache-2.0).
- Where can I find alternatives to OneCompression or LLM-Finetuning-Toolkit?
- GraphCanon lists graph-backed alternatives at OneCompression alternatives and LLM-Finetuning-Toolkit alternatives (OneCompression markdown twin, LLM-Finetuning-Toolkit 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, OneCompression or LLM-Finetuning-Toolkit?
- OneCompression: Very active. LLM-Finetuning-Toolkit: 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 OneCompression and LLM-Finetuning-Toolkit?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: OneCompression trust report; LLM-Finetuning-Toolkit trust report.