Pollock Mini LM · 125M
A compact language-model release. Model card, weights and usage information are available in the public repository.
FABRYKA AI / PUBLICATIONS & RESEARCH OUTPUTS
Artifacts behind
the research.
Public models, datasets, evaluation infrastructure and technical documentation. Browse the original repositories for versions, licenses and usage information.
01 / MODELS & DATA
Inspect. Reuse.
Build on the work.
These links point to public research artifacts from SlayerLab. Repository documentation defines each artifact’s scope and limitations.
Model repository ↗, Pollock Mini LM · 125M
Polish DynaWord
A public Polish-language dataset supporting our work on language-model training and data composition.
GoLLeM v4 · 250M PL
A Polish language-model repository with published weights and model documentation.
02 / BENCHMARKS & TECHNICAL DOCUMENTATION
Make the comparison
inspectable.
Evaluation resources and practical documentation support the research workflow. They are listed by artifact type, separately from papers and technical reports.
CodeSOTA
Model comparisons and benchmark evidence. Individual entries identify their sources and evaluation context.
Token probabilities & distillation
API documentation and examples for collecting token probabilities and exploring distillation.
Research notes & methodology
Dated experiment write-ups and evaluation methodology, preserved under Fabryka AI.
FABRYKA AI NOTES / REPORTS & PROPOSALS
Research in writing.
Technical reports and proposals shared by SlayerLab, Fabryka AI and collaborators. Each entry retains its authors, version and result status from the source repository.
Research proposal · Author-reviewed
A data queue with quarantine: proposal for online data selection in GoLLeM-v6
No results
Read illustrated article →, A data queue with quarantine: proposal for online data selection in GoLLeM-v6Read PDF ↗, A data queue with quarantine: proposal for online data selection in GoLLeM-v6Metadata & attribution ↗, A data queue with quarantine: proposal for online data selection in GoLLeM-v6
Technical report · Draft
GoLLeM-v5: Small Language Models for the Glint Tiny-ML Leaderboard. Measurement, Method and Negative Results
Preliminary results
Read illustrated article →, GoLLeM-v5: Small Language Models for the Glint Tiny-ML Leaderboard. Measurement, Method and Negative ResultsRead PDF ↗, GoLLeM-v5: Small Language Models for the Glint Tiny-ML Leaderboard. Measurement, Method and Negative ResultsMetadata & attribution ↗, GoLLeM-v5: Small Language Models for the Glint Tiny-ML Leaderboard. Measurement, Method and Negative Results
“Author-reviewed” is the repository’s review status; it does not indicate journal peer review. Drafts and proposals are works in progress. See each entry’s metadata for licensing; an undecided license does not grant reuse rights.
Browse Fabryka AI Notes on GitHub ↗OPEN RESEARCH / ACROSS THE REPOSITORIES
More questions.
More evidence.
Nine selected notes across six public repositories: small models, Polish tokenizers, evaluation and data preparation. Browse measured results, provisional pilots and technical methods with their source records.
RESEARCH ATLAS / FOLLOW THE CONNECTIONS
What builds on what?
From data and representation to measurement and decisions. Select a note to see the connection, its purpose and the source evidence.
Documented reuseConceptual connectionArrows show relationships, not a timeline.
Scroll the diagram sideways, or select a note below.
01 / REPRESENTATION
Polish BPE
A handwritten byte-level BPE is compared with Hugging Face variants at the same 6,756-token vocabulary. The question is how implementation and pretokenisation change compression.
The reported range is 833–844 tokens on a small held-out. It does not establish a downstream model-quality gain.
What connects it — and why
- Conceptual connection
Polish BPE → Probierz
Different token counts → a common unit
The BPE experiment shows that token counts depend on tokenisation. This motivates looking beyond per-token quantities when comparing models. The recorded panel uses bits per byte; this is a methodological connection, not evidence that Probierz used this tokenizer.
02 / DATA QUALITY
Dataset cleaning
The audit compares the existing data pipeline with published corpus-cleaning approaches and identifies candidate quality, deduplication and filtering checks.
This is a gap analysis for future experiments, not a measured cleaning ablation.
What connects it — and why
- Conceptual connection
Dataset cleaning → Polish agreement probe
Audit overlap before interpreting scores
Data-cleaning research includes evaluation-overlap checks. The Ladder pilot explicitly leaves training-overlap review pending. Both address the same validity problem; no shared implementation is established by these notes.
Dataset cleaning: source ↗ · Polish agreement probe: source ↗
03 / MEASUREMENT
Probierz
Probierz defines measurement dimensions, provenance and overlap checks, and a programme of falsifiable evaluation. It also provides scoring tools used by the recorded model panel.
The panel is evidence for particular metrics, not validation of the entire multidimensional measurement programme.
What connects it — and why
- Conceptual connection
Polish BPE → Probierz
Different token counts → a common unit
The BPE experiment shows that token counts depend on tokenisation. This motivates looking beyond per-token quantities when comparing models. The recorded panel uses bits per byte; this is a methodological connection, not evidence that Probierz used this tokenizer.
- Documented reuse
Probierz → Fabryka Track panel
Scoring tools + syntax items
The Track result manifest identifies Probierz’s scorer_hf.py, bpb_scorer.py, run_forced_choice.py and d3 syntax eval items, including hashes. These tools and items turn part of the measurement programme into a recorded model panel.
04 / PILOT
Polish agreement probe
A matched-item pilot records continuous pair probabilities and runtime for Koliber and Pollock. Its measurements supply the cost-planning note.
Native review and overlap checks are pending. Corpus BPB and training-seed variance were not measured; language-training domains differ.
What connects it — and why
- Conceptual connection
Dataset cleaning → Polish agreement probe
Audit overlap before interpreting scores
Data-cleaning research includes evaluation-overlap checks. The Ladder pilot explicitly leaves training-overlap review pending. Both address the same validity problem; no shared implementation is established by these notes.
Dataset cleaning: source ↗ · Polish agreement probe: source ↗
- Documented reuse
Polish agreement probe → Benchmark cost
Measured runtime + observed contrast
The cost note explicitly reuses the 2,000-pair pilot: 111.3 seconds for Koliber, 100.4 seconds for Pollock and an observed probability difference of 0.3169. It uses these measurements to anchor workload estimates; it cannot compute seed-calibrated DF.
05 / RECORDED EVIDENCE
Fabryka Track panel
The result archive stores per-document compression scores, paired-bootstrap artifacts and a syntax panel. Its manifest names and hashes Probierz scoring tools and syntax items.
BPB uses 300 documents; the reported syntax core contains 148 pairs. This panel and the Ladder pilot use different items and metrics.
What connects it — and why
- Documented reuse
Probierz → Fabryka Track panel
Scoring tools + syntax items
The Track result manifest identifies Probierz’s scorer_hf.py, bpb_scorer.py, run_forced_choice.py and d3 syntax eval items, including hashes. These tools and items turn part of the measurement programme into a recorded model panel.
- Conceptual connection
Fabryka Track panel → Benchmark cost
From a score to a useful decision
The historical panel reports scores and uncertainty. Ladder asks the additional question: can a metric distinguish the intended models reliably enough to justify its cost? This connects research questions; the cost note does not claim to reuse the Track panel.
06 / NEXT DECISION
Benchmark cost
The planning note reuses the Polish pilot’s observed runtimes and contrast, then estimates the workload of other benchmarks to plan the next paired evaluation.
English-battery times are proxies. Seed-calibrated discrimination is unavailable, so the note does not establish which benchmark should be adopted.
What connects it — and why
- Documented reuse
Polish agreement probe → Benchmark cost
Measured runtime + observed contrast
The cost note explicitly reuses the 2,000-pair pilot: 111.3 seconds for Koliber, 100.4 seconds for Pollock and an observed probability difference of 0.3169. It uses these measurements to anchor workload estimates; it cannot compute seed-calibrated DF.
- Conceptual connection
Fabryka Track panel → Benchmark cost
From a score to a useful decision
The historical panel reports scores and uncertainty. Ladder asks the additional question: can a metric distinguish the intended models reliably enough to justify its cost? This connects research questions; the cost note does not claim to reuse the Track panel.
Two reuse links are documented in the inspected notes. Three dashed links are editorial interpretations of shared research questions. They do not imply a shared codebase, dataset or chronological dependency. Sources are pinned to the revisions used in this catalogue.
Illustrated research note · micro-models
Small models, complementary errors
Completed pilot · limited sample
Read illustrated article →, Small models, complementary errorsSource note ↗, Small models, complementary errors
Illustrated research note · tokenizer
A smaller vocabulary, fitted to Polish
Workshop research · measured tokenizer metrics
Read illustrated article →, A smaller vocabulary, fitted to PolishSource note ↗, A smaller vocabulary, fitted to Polish
Illustrated research note · ladder
A Polish probe, with its limits visible
Provisional diagnostic · incomplete tier
Read illustrated article →, A Polish probe, with its limits visibleSource note ↗, A Polish probe, with its limits visible
Experiment report · micro-models
Music experts: ensemble, stitching and representation similarity
Reported results · corpus-specific
Source note ↗, Music experts: ensemble, stitching and representation similarity
Workshop research · tokenizer
Polish BPE: implementation and pretokenisation
Small held-out · 371 words
Source note ↗, Polish BPE: implementation and pretokenisation
Benchmark methodology · Probierz
Probierz: evaluation dimensions and falsifiable measurement
Experimental programme
Source note ↗, Probierz: evaluation dimensions and falsifiable measurement
Technical research note · slayer-dsbench
Dataset cleaning: pipeline gaps and proposed experiments
Gap analysis · not an ablation result
Source note ↗, Dataset cleaning: pipeline gaps and proposed experiments
Benchmark artifact · fabryka-track
Polish compression and syntax: recorded model panel
Historical matched panel
Source note ↗, Polish compression and syntax: recorded model panel
Evaluation planning note · ladder
Benchmark cost versus discrimination
Measured probe + explicit cost proxies
These are selected research notes and artifacts, not nine peer-reviewed papers. Measurements retain their own datasets and protocols; scores from different studies are not combined.