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Purpose: This adapter is a style evaluator / judge, not a fiction writer.
Fuente del modelo
Descripción de la fuente
Purpose: This adapter is a style evaluator / judge, not a fiction writer.
It classifies and scores English prose against a 14-metric Leech & Short–derived rubric and emits structured JSON style_profiles (plus short natural-language judgments). Those judgments power a bake-off and later used to train / score a stylish fiction writer and assemble long-form novels that stay on voice, style, and tone.
Fuentes
1 fuenteVerificado 22 sept
Artefactos del modelo
1 artefactoExtractos de fuentes
2 extractos| Role | This model | Not this model |
|---|---|---|
| Judge | Yes — classify / score passages | — |
| Steer | Provides signal for steering cards | Does not write novels itself |
| Writer | Used later to train / score a writer | Do not treat this LoRA as a story generator |
Companion GGUFs (merged + quantized for LM Studio / llama.cpp): alexokita/gemma4-style-step3000-GGUF.
style_profile JSON for a prose passageRecommended inference settings (LM Studio / llama.cpp): temperature 1.0, top_p 0.95, top_k 64, thinking off for JSON / classification tasks. Prefer the Q4_K_M or Q5_K_M GGUF for day-to-day use on a 24 GB GPU.
| Item | Value |
|---|---|
| Base | Gemma 4 26B-A4B instruct QAT weights (unsloth/gemma-4-26B-A4B-it-qat-q4_0-unquantized, HF parent of the QAT GGUF family) |
| Method | LoRA SFT via Unsloth + TRL on DGX Spark (spark-4f07, ~128 GB unified Blackwell + CUDA) |
| Config | train/train_config.gemma4_spark.toml |
| LoRA | rank 16, alpha 16, text-only MoE targets (experts.gate_up_proj, experts.down_proj + attention/MLP projections) |
| Sequence | 2048 tokens |
| Batch | microbatch 8 × grad accum 2 (effective 16) |
| Steps | 3000 (final checkpoint; best eval loss ≈ 2.378) |
| LR | 2e-4 with warmup |
| Chat template | Non-thinking gemma-4 (JSON-friendly) |
Training mix (Phase 3 instruction pairs from classified prose):
Approx. 430k train / 48k validation pairs under train/style_training/.
Styled fiction chunks labeled with metadata.style_profile, then converted to instruction pairs. Corpora include (HF sources):
Labels were produced by the Phase 2 pipeline (tools/style_classification/run_pipeline.py): computable metrics (spaCy / textstat) plus LLM semantic metrics grounded in a Style-in-Fiction rubric and RAG knowledge base (source/style_rubric.json, source/extracted/style_knowledge.jsonl).
Derived from Leech & Short, Style in Fiction (2nd ed.), operationalized in source/style_rubric.json (stats.dimensions = 14).
| # | Metric ID | Category | How generated | Values / type |
|---|---|---|---|---|
| 1 | lexical_complexity | Lexical | LLM (Pass 1) | simple_colloquial / neutral / complex_literary |
| 2 | lexical_density | Lexical | Computable | continuous 0–1 (content-word ratio) |
| 3 | register | Lexical | LLM (Pass 1) | formal_literary, formal_technical, neutral_narrative, colloquial, dialect, archaic |
| 4 | sentence_complexity | Grammatical | LLM (Pass 1) |
How those labels were generated:
style_knowledge.jsonl; distill checklist + 14 operational dimensions into style_rubric.json.--pass both: Pass 1 lexical/syntax/discourse + POV; Pass 2 tone / mind style / narrative distance / FID).Additional textual principles in the rubric (end_focus, segmentation, prose_rhythm, climax, ...) may appear in full profiles from the classifier pipeline; the 14 rows above are the core training/eval dimensions for this adapter.
Full profile:
Provide a complete style profile for this passage as JSON.
Passage:
...
Single dimension:
What tone does the author strike in this passage?
Passage:
...
Steering-card check (post–Phase 5B):
Does this passage match the following steering card?
List mismatches only (field: expected vs observed).
Card: { "pov": "first_person", "register": "colloquial", "tone": "tense", ... }
Passage:
...
| Artifact | Description |
|---|---|
adapter_model.safetensors + adapter_config.json | PEFT LoRA @ step 3000 (~1.9 GB) |
| Tokenizer / chat template | Gemma 4 non-thinking template |
| GGUFs | See alexokita/gemma4-style-step3000-GGUF (Q5_K_M ~18 GB, Q4_K_M ~16 GB) |
@software{gemma4_style_lora_step3000,
title = {Gemma 4 Style LoRA — prose style evaluator},
author = {Okita, Alex},
year = {2026},
url = {https://huggingface.co/alexokita/gemma4-style-lora-step3000}
}
Use of the base Gemma 4 weights is subject to the Gemma license. This adapter inherits those terms for combined use.
--- library_name: peft base_model: google/gemma-4-26B-A4B-it base_model_relation: adapter tags: - lora - sft - style-classification - prose-evaluator - stylistics - unsloth - trl - gemma4 license: gemma language: - en pipeline_tag: text-generation --- # Gemma 4 Style LoRA (step 3000) — prose style evaluator **Purpose:** This adapter is a **style evaluator / judge**, not a fiction writer. It classifies and scores English prose against a **14-metric Leech & Short–derived rubric** and emits structured JSON `style_profile`s (plus short natural-language judgments). Those judgments power a bake-off and later **steering cards** used to train / score a stylish fiction writer and assemble long-form novels that stay on voice, style, and tone. | Role | This model | Not this model | |---|---|---| | **Judge** | Yes — classify / score passages | — | | **Steer** | Provides signal for steering cards | Does not write novels itself | | **Writer** | Used later to train / score a writer | Do not treat this LoRA as a story generator | Companion GGUFs (merged + quantized for LM Studio / llama.cpp): [`alexokita/gemma4-style-step3000-GGUF`](https://huggingface.co/alexokita/gemma4-style-step3000-GGUF). ## Intended use - Produce a full `style_profile` JSON for a prose passage - Answer single-dimension questions (tone, POV, figurative density, FID, …) with a short rationale - Score whether a draft matches a target **steering card** (voice / style / tone) - Run agreement bake-offs vs held-out labeled prose (and Style-in-Fiction exemplars) - After a successful bake-off: bulk re-label corpora and drive writer ↔ evaluator loops **Recommended inference settings (LM Studio / llama.cpp):** temperature `1.0`, top_p `0.95`, top_k `64`, **thinking off** for JSON / classification tasks. Prefer the Q4_K_M or Q5_K_M GGUF for day-to-day use on a 24 GB GPU. ## Out of scope - Open-ended story generation or “write a novel” - Plot / character consistency (separate stack) - Overall literary quality preference ranking without a human agreement study ## How it was trained | Item | Value | |---|---| | **Base** | Gemma 4 26B-A4B instruct QAT weights (`unsloth/gemma-4-26B-A4B-it-qat-q4_0-unquantized`, HF parent of the QAT GGUF family) | | **Method** | LoRA SFT via Unsloth + TRL on **DGX Spark** (`spark-4f07`, ~128 GB unified Blackwell + CUDA) | | **Config** | `tr...
Source context: 8 downloads · 0 likes · Pipeline text-generation · Library peft · Repo alexokita/gemma4-style-lora-step3000
| Host | Spark only — RTX 3090 is for quantized inference, not training |
simple_paratactic / moderate / complex_hypotactic |
| 5 | sentence_length_mean | Grammatical | Computable | mean words/sentence |
| 6 | subordination_ratio | Grammatical | Computable | hypotaxis proxy |
| 7 | figurative_density | Figurative | LLM (Pass 1) | low / moderate / high |
| 8 | cohesion | Cohesion | LLM (Pass 1) | loose / standard / tight |
| 9 | dialogue_ratio | Grammatical | Computable | continuous 0–1 (direct speech share) |
| 10 | pov | Viewpoint | LLM (Pass 1) | first_person, second_person, third_limited, third_omniscient, mixed |
| 11 | mind_style | Viewpoint | LLM (Pass 2) | standard / distinct / deviant |
| 12 | narrative_distance | Viewpoint | LLM (Pass 2) | intimate / moderate / distant |
| 13 | free_indirect_discourse | Viewpoint | LLM (Pass 2) | none / sparse / moderate / heavy |
| 14 | tone | Context | LLM (Pass 2) | neutral, lyrical, sardonic, melancholic, comedic, tense, contemplative |