Comparison
femtoGPT vs hazm
Verdict
Pick femtoGPT if a minimalistic GPT-style language model framework in Rust, suitable for both CPU and GPU inference and training via OpenCL; pick hazm if hazm is a Persian NLP Toolkit used for dependency parsing, embeddings, lemmatization, normalization, POS tagging and tokenization in Python.
Markdown twin · femtoGPT alternatives · hazm alternatives
GraphCanon updated Sep 20, 2026
9views this month
Trust & integrity
| Signal | femtoGPT | hazm |
|---|---|---|
| Maintenance | Slowing (321d since push) As of Sep 7, 2026 · github_public_v1 | Slowing (171d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 7, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · 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
- femtoGPT
- Pure Rust implementation of a minimal Generative Pretrained Transformer
- hazm
- Persian NLP Toolkit for dependency parsing, embeddings, lemmatization, normalization, POS tagging, and tokenization
Stars
- femtoGPT
- 936
- hazm
- 1.4k
Forks
- femtoGPT
- 67
- hazm
- 205
Open issues
- femtoGPT
- 10
- hazm
- 8
Language
- femtoGPT
- Rust
- hazm
- Python
Adopt for
- femtoGPT
- A minimalistic GPT-style language model framework in Rust, suitable for both CPU and GPU inference and training via OpenCL.
- hazm
- Hazm is a Persian NLP Toolkit used for dependency parsing, embeddings, lemmatization, normalization, POS tagging and tokenization in Python.
Persona
- femtoGPT
- developer harness
- hazm
- -
Runtime
- femtoGPT
- -
- hazm
- -
License
- femtoGPT
- MIT License, permitting any use as long as all copyright and license information are retained.
- hazm
- MIT
Last pushed
- femtoGPT
- Oct 21, 2025
- hazm
- Apr 1, 2026
Categories
- femtoGPT
- LLM Frameworks, Model Training
- hazm
- Data & Retrieval, Model Training
Trust and health
Days since push
- femtoGPT
- 321d
- hazm
- 171d
Open issues (now)
- femtoGPT
- 10
- hazm
- 8
Stars delta
- femtoGPT
- +1 (30d)
- hazm
- +12 (30d)
Open issues delta
- femtoGPT
- 0 (30d)
- hazm
- +1 (30d)
Owner type
- femtoGPT
- User
- hazm
- Organization
Full report
- femtoGPT
- Trust report
- hazm
- Trust report
Choose femtoGPT if…
- femtoGPT is primarily Rust; hazm is Python.
- 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.
- Also covers LLM Frameworks.
- 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.
Choose hazm if…
- hazm is primarily Python; femtoGPT is Rust.
- Tags unique to hazm: dependency-parser, embeddings, lemmatization, natural language processing.
- Also covers Data & Retrieval.
- When working exclusively with Farsi language texts where comprehensive processing tools like dependency parsing or POS tagging are required.
When NOT to use hazm
- If the project involves languages other than Persian, as Hazm lacks capabilities for multilingual support beyond Farsi.
- When advanced machine learning models requiring extensive training data and resources are necessary; Hazm provides utilities but does not include state-of-the-art models.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (keyvank/femtoGPT) · observed Sep 20, 2026
- GitHub forks (keyvank/femtoGPT) · observed Sep 20, 2026
- Last push (keyvank/femtoGPT) · observed Oct 21, 2025
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (roshan-research/hazm) · observed Sep 20, 2026
- GitHub forks (roshan-research/hazm) · observed Sep 20, 2026
- Last push (roshan-research/hazm) · observed Apr 1, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: femtoGPT 936 · hazm 1.4k (synced Sep 20, 2026).
Common questions
- What is the difference between femtoGPT and hazm?
- femtoGPT: Pure Rust implementation of a minimal Generative Pretrained Transformer. hazm: Persian NLP Toolkit for dependency parsing, embeddings, lemmatization, normalization, POS tagging, and tokenization. See the comparison table for live GitHub stats and shared categories.
- When should I choose femtoGPT over hazm?
- Choose femtoGPT over hazm when femtoGPT is primarily Rust; hazm is Python; 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; Also covers LLM Frameworks; 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 choose hazm over femtoGPT?
- Choose hazm over femtoGPT when hazm is primarily Python; femtoGPT is Rust; Tags unique to hazm: dependency-parser, embeddings, lemmatization, natural language processing; Also covers Data & Retrieval; When working exclusively with Farsi language texts where comprehensive processing tools like dependency parsing or POS tagging are required.
- 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.
- When should I avoid hazm?
- If the project involves languages other than Persian, as Hazm lacks capabilities for multilingual support beyond Farsi. When advanced machine learning models requiring extensive training data and resources are necessary; Hazm provides utilities but does not include state-of-the-art models.
- Is femtoGPT or hazm more popular on GitHub?
- hazm has more GitHub stars (1,424 vs 936). Stars measure visibility, not whether either tool fits your constraints.
- Are femtoGPT and hazm open source?
- Yes - both are open-source projects on GitHub (femtoGPT: MIT, hazm: MIT).
- Where can I find alternatives to femtoGPT or hazm?
- GraphCanon lists graph-backed alternatives at femtoGPT alternatives and hazm alternatives (femtoGPT markdown twin, hazm 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, femtoGPT or hazm?
- femtoGPT: Slowing. hazm: 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 femtoGPT and hazm?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: femtoGPT trust report; hazm trust report.