Comparison
peft vs femtoGPT
Verdict
Pick peft if pEFT focuses on advanced techniques for efficiently tuning parameters in large models with Python; pick femtoGPT if a minimalistic GPT-style language model framework in Rust, suitable for both CPU and GPU inference and training via OpenCL.
Markdown twin · peft alternatives · femtoGPT alternatives
GraphCanon updated today
Trust & integrity
| Signal | peft | femtoGPT |
|---|---|---|
| Maintenance | Very active (1d since push) As of today · github_public_v1 | Slowing (290d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Personal account As of 2w · 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
- peft
- State-of-the-art Parameter-Efficient Fine-Tuning
- femtoGPT
- Pure Rust implementation of a minimal Generative Pretrained Transformer
Stars
- peft
- 22k
- femtoGPT
- 935
Forks
- peft
- 2.4k
- femtoGPT
- 67
Open issues
- peft
- 74
- femtoGPT
- 10
Language
- peft
- Python
- femtoGPT
- Rust
Adopt for
- peft
- PEFT focuses on advanced techniques for efficiently tuning parameters in large models with Python.
- femtoGPT
- A minimalistic GPT-style language model framework in Rust, suitable for both CPU and GPU inference and training via OpenCL.
Persona
- peft
- -
- femtoGPT
- developer harness
Runtime
- peft
- -
- femtoGPT
- -
License
- peft
- Apache-2.0
- femtoGPT
- MIT License, permitting any use as long as all copyright and license information are retained.
Last pushed
- peft
- Aug 22, 2026
- femtoGPT
- Oct 21, 2025
Categories
- peft
- LLM Frameworks, Model Training
- femtoGPT
- LLM Frameworks, Model Training
Trust and health
Maintenance
- peft
- Very active (96%)
- femtoGPT
- Slowing (36%)
Days since push
- peft
- 1d
- femtoGPT
- 290d
Open issues (now)
- peft
- 74
- femtoGPT
- 10
Stars delta
- peft
- +142 (30d)
- femtoGPT
- Unknown
Open issues delta
- peft
- +16 (30d)
- femtoGPT
- Unknown
Owner type
- peft
- Organization
- femtoGPT
- User
Full report
- peft
- Trust report
- femtoGPT
- Trust report
Choose peft if…
- peft is primarily Python; femtoGPT is Rust.
- License: peft is Apache-2.0, femtoGPT is MIT.
- Tags unique to peft: adapter, diffusion, fine-tuning, llm.
- When you need to fine-tune large language models but are constrained by compute resources or want to avoid overfitting.
When NOT to use peft
- If you require a tool that supports training from scratch, as PEFT is specifically designed for fine-tuning purposes only.
- When working on models where the full fine-tuning of all parameters is feasible or preferred due to ample compute resources and no concern over overfitting.
Choose femtoGPT if…
- femtoGPT is primarily Rust; peft is Python.
- License: femtoGPT is MIT, peft is Apache-2.0.
- Requirements: Requires the Rust toolchain installed on your system.; If targeting GPU usage, correct installation of GPU drivers along with OpenCL runtimes is necessary..
- Tags unique to femtoGPT: from-scratch, gpt, gpu, machine-learning.
- When you want a pure Rust implementation that provides an easy-to-understand basis for learning about the inner workings of AI models.
When NOT to use femtoGPT
- When high performance is required as femtoGPT operates relatively slower compared to optimized models, especially for large-scale training.
- If your project strictly needs CUDA-based optimization specific to NVIDIA GPUs, given that femtoGPT leverages OpenCL for GPU support.
- In cases where the project demands a fully tested and production-ready model; femtoGPT's architecture correctness is not guaranteed due to possible implementation errors.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (huggingface/peft) · observed Aug 23, 2026
- GitHub forks (huggingface/peft) · observed Aug 23, 2026
- Last push (huggingface/peft) · observed Aug 22, 2026
- License file (Apache-2.0) · observed Aug 23, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (keyvank/femtoGPT) · observed Aug 8, 2026
- GitHub forks (keyvank/femtoGPT) · observed Aug 8, 2026
- Last push (keyvank/femtoGPT) · observed Oct 21, 2025
- License file (MIT) · observed Aug 8, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: peft 22k · femtoGPT 935 (synced Aug 23, 2026).
Common questions
- What is the difference between peft and femtoGPT?
- peft: State-of-the-art Parameter-Efficient Fine-Tuning. femtoGPT: Pure Rust implementation of a minimal Generative Pretrained Transformer. See the comparison table for live GitHub stats and shared categories.
- When should I choose peft over femtoGPT?
- Choose peft over femtoGPT when peft is primarily Python; femtoGPT is Rust; License: peft is Apache-2.0, femtoGPT is MIT; Tags unique to peft: adapter, diffusion, fine-tuning, llm; When you need to fine-tune large language models but are constrained by compute resources or want to avoid overfitting.
- When should I choose femtoGPT over peft?
- Choose femtoGPT over peft when femtoGPT is primarily Rust; peft is Python; License: femtoGPT is MIT, peft is Apache-2.0; Requirements: Requires the Rust toolchain installed on your system.; If targeting GPU usage, correct installation of GPU drivers along with OpenCL runtimes is necessary.; Tags unique to femtoGPT: from-scratch, gpt, gpu, machine-learning; When you want a pure Rust implementation that provides an easy-to-understand basis for learning about the inner workings of AI models.
- When should I avoid peft?
- If you require a tool that supports training from scratch, as PEFT is specifically designed for fine-tuning purposes only. When working on models where the full fine-tuning of all parameters is feasible or preferred due to ample compute resources and no concern over overfitting.
- When should I avoid femtoGPT?
- When high performance is required as femtoGPT operates relatively slower compared to optimized models, especially for large-scale training. If your project strictly needs CUDA-based optimization specific to NVIDIA GPUs, given that femtoGPT leverages OpenCL for GPU support. In cases where the project demands a fully tested and production-ready model; femtoGPT's architecture correctness is not guaranteed due to possible implementation errors.
- Is peft or femtoGPT more popular on GitHub?
- peft has more GitHub stars (21,585 vs 935). Stars measure visibility, not whether either tool fits your constraints.
- Are peft and femtoGPT open source?
- Yes - both are open-source projects on GitHub (peft: Apache-2.0, femtoGPT: MIT).
- Where can I find alternatives to peft or femtoGPT?
- GraphCanon lists graph-backed alternatives at peft alternatives and femtoGPT alternatives (peft markdown twin, femtoGPT 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, peft or femtoGPT?
- peft: Very active. femtoGPT: 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 peft and femtoGPT?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: peft trust report; femtoGPT trust report.