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textgeneratorc2k

Text Generation
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textgeneratorc2k
feature-extraction
storygeneratorc2k
userneeds2k
gemma-4
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text-classification
multilingual
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language:
  - en
  - zh
  - de
  - es
  - fr
  - ja
library_name: transformers
pipeline_tag: text-generation
base_model: google/gemma-4-31b-it
tags:
  - textgeneratorc2k
  - storygeneratorc2k
  - userneeds2k
  - gemma-4
  - latent-variable-models
  - text-generation
  - text-classification
  - multilingual
  - bfloat16

language: - en - zh - de - es - fr - ja library_name: transformers pipeline_tag: text-generation base_model: google/gemma-4-31b-it tags: - textgeneratorc2k - storygeneratorc2k - userneeds2k - gemma-4 - latent-variable-models - text-generation - text-classification - multilingual - bfloat16

TextGeneratorC2K (storygeneratorc2k + Gemma-4 Generator + userneeds2k Classifier, BF16): Core Narrative Fiction Paragraph Sample Generator

Abstract

The development of human language follows a four-stage trajectory: beginning with a basal protolanguage (~100–300 core concepts) for daily tasks, expanding into narrative vocabulary to record events and support group living (fiction), branching into specialized vocabulary for religion, law, logic, philosophy, and natural sciences (non-fiction), and ultimately forming a standardized lexicon that represents human consensus. Historically, this progression was driven by a dual mechanism: institutional codification (such as religious scripture and state administration) and spontaneous grassroots writing (such as commercial trade scripts, vernacular storytelling, and civic dialogue). In the current phase of large language model (LLM) adoption, an increasing volume of online text and academic literature is generated synthetically. When subsequent models are trained on corpora containing machine-generated text, the statistical distribution of the training data shifts. To maintain a clean baseline of contemporary natural language usage, this paper presents textgeneratorc2k (storygeneratorc2k), a unified BF16 (bfloat16) model combining three integrated components inside a single model.safetensors: (1) the storygeneratorc2k 3-level 5,376-D latent category bank, (2) the gemma-4 58-layer 6-language generator, and (3) the userneeds2k 2,000-category classifier (5376 -> 768 bridge + 768 -> 2048 -> 2000 MLPClassifier, adding only 38.8 MB / 0.07% to the checkpoint) to output calibrated categorical_scores. Grounded in corpus lexicographyβ€”specifically the 2,000-word defining vocabulary approach used in the Longman Dictionary of Contemporary Englishβ€”we construct a 5,376-dimensional hidden-layer embedding bank covering 2,000 high-frequency categories (1,999 active non-adult categories across 6 languages: en, cn, de, sp, fr, jp) and 5 sub-topics per category. Each sub-topic is stratified into three stylistic registers rendered in the contemporary news tone of the target language: Level 1: Basic (everyday consumer and community news), Level 2: Advanced (formal industry, financial, and technical news reporting), and Level 3: Speculative (in-depth news analysis, policy trade-offs, and editorial commentary). By applying manifold-constrained Gaussian perturbations to these stored embeddings inside the unified bfloat16 model, textgeneratorc2k generates normalized contemporary text samples (5–6 sentences) and simultaneously outputs 2,000-category categorical_scores directly through the standard Hugging Face transformers.pipeline interface.

Quickstart (transformers Common Pipeline)

textgeneratorc2k (storygeneratorc2k) unifies all weightsβ€”the storygeneratorc2k 3-Level 5,376-D Latent Embedding Bank, the 58-layer 6-language Gemma-4-31B generator, and the **userneeds2k 2,000-Category Classifier (5376 -> 768 Bridge + MLPClassifier)**β€”in bfloat16 within model.safetensors. Both paragraph generation and text classification directly return Top-5 categorical_scores.

import torch
from transformers import pipeline

# Load textgeneratorc2k (storygeneratorc2k + gemma-4 generator + userneeds2k classifier in BF16)
pipe = pipeline(
    "text-generation",
    model="bbin2022/textgeneratorc2k",  # or local path "./textgeneratorc2k" / "./storygeneratorc2k"
    trust_remote_code=True,
    dtype=torch.bfloat16,
    device_map="auto",
)

# 1. Generate a 5-6 sentence contemporary news paragraph + categorical_scores
res_en = pipe({"category_id": "10008/20081/31194", "language": "en", "level": 1})
print(res_en["paragraph"])
for item in res_en["categorical_scores"]:
    print(f"{item['score']:.4f} (userneeds2k={item['userneeds2k_prob']:.4f})  {item['category_id']}  {item['category_name']}")

# 2. Generate in Simplified Chinese (Level 3: In-Depth News Analysis & Editorial Commentary)
res_cn = pipe({"category_id": "10008/20081/31194", "language": "cn", "level": 3})
print(res_cn["paragraph"])

# 3. Classify any arbitrary text into Top-5 categorical_scores
preds = pipe(res_en["paragraph"])
for item in preds["categorical_scores"]:
    print(f"{item['score']:.4f} (userneeds2k={item['userneeds2k_prob']:.4f})  {item['category_id']}  {item['category_name']}")

1. The History of Human Language Development and Linguistic Perspectives

1.1 The Four-Stage Macro-Developmental Process

Based on evolutionary anthropology, historical linguistics, and cognitive science, the expansion of human vocabulary can be described in four macro-stages:

  1. Basal Protolanguage for Daily Needs (~100–300 Core Concepts): While specific words are acquired culturally rather than genetically (Chomsky, 1965; Pinker, 1994), humans possess an innate capacity for language anchored by roughly 65 universal "semantic primes" (Wierzbicka, 1996). In early hominid groupsβ€”as well as in early childhood language acquisition and trade pidginsβ€”this capacity manifests as a concrete protolanguage of approximately 100 to 300 basic words (Swadesh, 1955; Bickerton, 1990). This core vocabulary suffices for immediate daily tasks, indexing physical objects, kinship, and basic actions in the present environment.
  2. Group Living and Narrative Expansion (Fiction): As social groups grew beyond roughly 150 individuals, physical grooming could no longer maintain social cohesion (Dunbar, 1996). Language adapted to record social events, track reputation, and share collective narratives. This required the linguistic property of displacementβ€”referring to events outside the immediate time and place (Hockett, 1960). Vocabulary expanded to support what Donald (1991) terms Mythic Culture and what Bruner (1991) and Harari (2014) describe as narrative or intersubjective fiction: stories, counterfactuals, and shared traditions that coordinate group behavior.
  3. Analytical and Domain-Specific Vocabulary (Non-Fiction): With the rise of agrarian settlements and the invention of writing (Ong, 1982; Goody, 1986), oral storytelling was supplemented by written records. Through grammatical nominalizationβ€”converting narrative actions and verbs into abstract nouns (Halliday & Martin, 1993)β€”vocabulary developed for non-fiction domains: religion, law, logic, philosophy, and the natural sciences.
  4. Vocabulary as Human Consensus: At this stage, abstract terms represent shared institutional facts and collective intentionality (Searle, 1995). Words in law, philosophy, and science encapsulate agreed-upon definitions, logical relationships, and verified empirical knowledge, allowing communities to maintain continuity of thought across generations.

1.2 Systematic Stages of Natural Language Standardization

To understand how spoken dialects transition from Stage 2 (oral narrative) to Stages 3 and 4 (written analytical consensus), sociolinguistics and literacy theory (Ferguson, 1959; Haugen, 1966; Ong, 1982; Goody, 1986) identify six structural phases of natural language development:

Stage Developmental Phase Linguistic Mechanism Primary Cognitive & Structural Shift
Stage I Oral Vernacular & Protolanguage Concrete indexicality & acoustic memory Language is strictly spoken, local, and fluid. Memory relies on rhythm, repetition, and concrete narrative formulas.
Stage II Orthographic Inception (Graphization) Invention or adoption of a writing script Spoken sounds or concepts are fixed onto physical media (clay, bone, bark, papyrus), detaching the message from the speaker.
Stage III Grammatical & Phonological Codification Prescriptive grammar & lexicography Rules of syntax, morphology, and pronunciation are explicitly analyzed and stabilized to prevent linguistic drift.
Stage IV Supraregional Standardization Dialect leveling & vernacular adoption A shared written variety overrides fragmented local dialects to create a unified regional or national language.
Stage V Conceptual Abstraction & Metaphor Grammatical nominalization & semantic shift Concrete physical words are repurposed into abstract nouns to express metaphysics, ethics, law, and technical processes.
Stage VI Exegetical & Analytical Discourse Dialectical syntax & formal logic Development of subordinate clauses, citation systems, and structured argumentation to debate contested interpretations.

1.3 Co-Evolution of Religious Writing, Institutional Catalysts, and Spontaneous Grassroots Writing

Across Stages II through VI, natural language was shaped by the continuous interaction between top-down institutional codification (particularly religious texts and state administration) and bottom-up spontaneous grassroots writing (driven by merchants, artisans, folk storytellers, and independent scholars). While religious and state institutions sought linguistic stability and fidelity, grassroots writing continuously injected phonetic simplicity, vernacular vitality, and pragmatic adaptability into natural language.

A. Orthographic Inception: Sacred Divination vs. Grassroots Accounting and Trade (Stage II)

The invention and spread of writing systems emerged from two parallel needs: communicating with the sacred and managing everyday material exchange.

  • Religious Script Origins: In several traditions, writing was formalized to preserve sacred rituals or translate scriptures. In Shang-dynasty China (c. 1250 BCE), Oracle Bone Script (jiaguwen) was developed to record royal pyromantic divinations. In Late Antiquity and the Middle Ages, missionary scholars invented scripts to bring sacred texts to oral communities, including Ulfilas’s Gothic alphabet (4th century CE), Mashtots’s Armenian alphabet (405 CE), and Cyril and Methodius’s Glagolitic and Cyrillic scripts (9th century CE).
  • Spontaneous Grassroots & Commercial Graphization: Parallel to religious scriptsβ€”and often predating themβ€”writing emerged spontaneously from grassroots economic activity. In Mesopotamia (c. 3200 BCE), cuneiform did not originate to record scripture or royal chronicles, but evolved from clay accounting tokens used by local administrators and merchants to track grain, livestock, and labor (Schmandt-Besserat, 1992). Even more decisively, the Phoenician consonantal alphabet (c. 1050 BCE)β€”the direct ancestor of Greek, Latin, Aramaic, and Arabic scriptsβ€”was engineered by maritime traders who needed a rapid, portable, and easily learned phonetic system outside the monopoly of temple scribes. In medieval Europe and East Asia, archaeological corpora such as the Novgorod birch bark manuscripts (11th–15th centuries CE)β€”consisting of informal personal notes, debt reminders, and household letters written by ordinary townspeople, women, and artisansβ€”demonstrate that spontaneous grassroots literacy actively shaped everyday written syntax alongside formal church Slavonic.

B. Grammatical Codification vs. Vernacular Vitality (Stage III)

Once scripts were established, a tension arose between freezing a sacred "High" language and recording the evolving "Low" spoken vernacular (Ferguson, 1959).

  • Religious Codification and Formal Grammar: Because altering a sacred syllable was believed to invalidate ritual efficacy or corrupt divine law, religious scholarship produced the world’s earliest formal linguistics. In 4th-century BCE India, Pāṇini composed the AṣṭādhyāyΔ« (3,959 generative rules) to preserve the exact phonology and morphology of Vedic Sanskrit against vernacular drift. Similarly, in the 7th and 8th centuries CE, scholars such as Abu al-Aswad al-Du'ali and Sibawayh (Al-Kitāb) codified Arabic grammar (naαΈ₯w), lexicography, and script diacritics specifically to prevent recitation errors (laαΈ₯n) in the Quran.
  • Grassroots Vernacular Evolution: When religious or courtly codification froze classical languages (such as Classical Latin, Vedic Sanskrit, or Classical Literary Chinese wenyan), spontaneous grassroots writing prevented language stagnation. Across the Roman Empire, everyday soldiers, merchants, and stonecutters wrote in sermo plebeius (Vulgar Latin)β€”preserved in the spontaneous street graffiti of Pompeii and the Vindolanda wooden tabletsβ€”which evolved directly into the modern Romance languages. In China, starting in the Tang and Song dynasties, folk storytellers, street performers, and Buddhist popularizers recorded oral stories in vernacular Chinese (baihua) via bianwen (transformation texts) and huaben (scriptbooks). This spontaneous grassroots writing laid the foundation for the classical vernacular novels (Water Margin, Journey to the West, Dream of the Red Chamber) that eventually replaced Classical Chinese as the modern national standard.

C. Supraregional Standardization: Canonical Translation and Grassroots Print Culture (Stage IV)

Unifying fragmented local dialects into a standardized national language required bridging institutional authority with grassroots accessibility.

  • Religious Translation as a Unifying Anchor: When religious movements sought to reach the common population directly, they had to adopt and synthesize living vernaculars. Martin Luther’s German Bible (1522–1534) merged East Central German administrative syntax with vivid everyday spoken idioms; similarly, Tyndale’s New Testament (1526) and the King James Bible (1611) established a shared syntactic cadence for Early Modern English, while the Quran unified tribal dialects into Classical Arabic (FuαΉ£αΈ₯ā).
  • Parallel Secular Literature, State Chanceries, and Grassroots Print: Religious translation succeeded only when it intersected with secular and grassroots forces. In 14th-century Italy, standardization occurred not around a Bible translation, but around the Tuscan dialect of Dante’s Divine Comedy and Boccaccio’s Decameronβ€”the latter celebrating everyday mercantile and folk speech. In England and France, administrative standardization was driven by secular law courts (Chancery Standard English and the 1539 Ordonnance de Villers-CotterΓͺts). Crucially, during the 16th and 17th centuries, the proliferation of cheap grassroots print cultureβ€”pamphlets, ballads, almanacs, chapbooks, and guild manuals written by and for common citizensβ€”stabilized spelling and spread standardized vocabulary across socioeconomic classes.

D. Conceptual Abstraction and Dialectical Discourse (Stages V & VI)

Finally, expanding natural language to handle non-fiction logic, law, science, and philosophy required both institutional hermeneutics and grassroots civic debate.

  • Religious Exegesis and Semantic Abstraction: Theological writing systematically converted concrete physical nouns into abstract institutional concepts (e.g., Latin spiritus ["breath"] $\rightarrow$ "spirit"; Greek kanōn ["measuring reed"] $\rightarrow$ "canon/rule"; Hebrew berit ["cutting"] $\rightarrow$ "covenant"). Furthermore, exegetical traditionsβ€”such as Rabbinic Talmudic debate, Buddhist Abhidharma taxonomy, Islamic jurisprudence (Usul al-fiqh), and Medieval Scholasticismβ€”engineered rigorous syntactic frameworks for stating premises, raising counter-arguments, and resolving textual contradictions.
  • Parallel Civic Law, Philosophy, and Grassroots Debate: Operating alongside religious exegesis were secular legal and philosophical movements. Secular law codesβ€”from the Code of Hammurabi and Roman Civil Law to Chinese **Legalism (Fajia)*β€”established formal conditional syntax ("If X occurs under condition Y, then Z applies"*). During the Axial Age, grassroots philosophical schoolsβ€”such as the wandering scholars (shi) of China’s Hundred Schools of Thought and the Athenian agora dialogues of Socratesβ€”conducted open debates using everyday analogies to test ethical and logical propositions. Later, in the 17th century, the Scientific Revolution combined experimental artisan practices with grammatical nominalization (Halliday & Martin, 1993) to forge modern empirical and journalistic prose.

1.4 Summary of Linguistic Schools and Historical Drivers

Analytical Dimension Key Literature Role in Natural Language Evolution
Biolinguistics & Semantic Universals Swadesh (1955); Chomsky (1965); Bickerton (1990); Wierzbicka (1996) Establishes the innate cognitive baseline (~65 primes) and the 100–300 word concrete protolanguage used for daily survival.
Evolutionary Anthropology Hockett (1960); Donald (1991); Dunbar (1996); Harari (2014) Explains how group living drove displacement and narrative storytelling (fiction) to coordinate communities beyond 150 individuals.
Sociolinguistics & Standardization Theory Ferguson (1959); Haugen (1966); Ostler (2005); Pollock (2006) Models the interaction between codified "High" institutional registers (religion, state) and spontaneous "Low" grassroots vernaculars.
Literacy Theory & Functional Linguistics Ong (1982); Goody (1986); Schmandt-Besserat (1992); Halliday & Martin (1993) Traces how grassroots accounting, religious scripture, legal codes, and scientific nominalization transformed oral speech into analytical non-fiction.
Social Ontology Searle (1995) Demonstrates how standardized vocabulary functions as crystallized human consensus and collective intentionality.

2. Language Standardization and the Need for Uncontaminated Baselines

2.1 Full-Text Corpora and the Longman Standardization Model

As historical linguistics demonstrates, a healthy natural language relies on a balance between standardized core definitions and authentic grassroots usage distributions. In modern corpus linguistics, this balance is captured through two complementary mechanisms:

  1. Controlled Defining Vocabularies: West (1953) established in the General Service List (GSL) that roughly 2,000 core word families account for over 80% of general written English. Building on this empirical distribution, Procter (1978) introduced the Longman Defining Vocabulary (LDV) in the Longman Dictionary of Contemporary English (LDOCE), demonstrating that complex concepts across specialized domains can be standardized and explained using a core lexicon of 2,000 high-frequency words.
  2. Empirical Full-Text Databases: As shown by Sinclair (1991), maintaining language standards requires full-text databases rather than isolated word lists. Full-text corpora record the spontaneous grassroots collocations, idiomatic phrasing, and habitual probability distributions of natural human writing.

2.2 Addressing Training Input Contamination

We are currently in the early stages of widespread LLM application. A growing proportion of articles, web pages, and research preprints (such as those on arXiv) are generated or assisted by language models.

When machine-generated text is fed back into the training corpora of subsequent models, it alters the statistical distribution of the training inputβ€”specifically eroding the spontaneous grassroots variance that historically kept natural language adaptable. In computational linguistics, Kirby, Cornish, and Smith (2008) demonstrated through Iterated Learning Models (ILM) that when a system repeatedly learns from the sampled outputs of previous generations without grounding in the original human distribution, the language loses lexical diversity and undergoes distributional collapse (referred to in recent machine learning literature as model collapse).

Current open-weights models were trained on corpora collected before synthetic text reached high saturation, meaning their internal representations still reflect uncontaminated human language distributions relatively accurately. However, as synthetic text accumulates online, future training corpora will face increasing contamination. Designing a sample generator at this stage serves a practical long-term goal: preserving and sampling from the current, uncontaminated natural language distribution.

3. Methodology & Model Implementation (textgeneratorc2k: storygeneratorc2k + Gemma-4 Generator, BF16)

To generate standardized passages that reflect habitual human language distributions across 6 languages (en, cn, de, sp, fr, jp), textgeneratorc2k combines storygeneratorc2k (5,376-dimensional 3-level hidden-layer embedding bank + $5376 \to 768$ bridge + 2,000-category classifier) with a depth- and vocabulary-optimized 58-layer Gemma-4-31B (bfloat16) generator.

3.1 Hierarchical Taxonomy and Three-Level Contemporary News Register Stratification

Following the 2,000-unit scale of the Longman Defining Vocabulary (Procter, 1978) and reflecting the progression from everyday consumer narrative to specialized industry reporting and analytical commentary, we organize the embedding bank into a three-tier hierarchy:

  1. Question Categories ($C$): $N_c = 2,000$ hierarchical categories (Categoryv2-2000, defined in catid_define.json across en, cn, de, sp, fr, and jp; 1,999 active non-adult categories, with 10001 Adult excluded).
  2. Sub-Topics ($T$): For each category $c_i \in {c_1, \dots, c_{2000}}$, $N_t = 5$ representative sub-topics $t_{i,j} \in {t_{i,1}, \dots, t_{i,5}}$, resulting in $10,000$ topical nodes.
  3. Three Complexity Levels ($L$) in Contemporary News Tone: Each sub-topic $t_{i,j}$ is stratified into three register levels $l \in {1, 2, 3}$ (30,000 total anchor points), all rendered in the contemporary news tone of the target language (5–6 sentences, ~110–150 words for en/de/sp/fr or ~180–240 characters for cn/jp, matching the 79–88 word / 5.5–6.4 sentence empirical distribution of the uncontaminated reference corpus):
Level Register Name Linguistic & Journalistic Alignment Objective and Output Design (5–6 Sentences)
Level 1 Basic (Consumer & Community News) Everyday consumer, local, and livelihood news reporting grounded in high-frequency accessible vocabulary. Reports a concrete real-world consumer, service, or community story on the topic using clear, accessible contemporary journalism.
Level 2 Advanced (Industry & Technical News) Codified institutional, financial, and technical journalism using domain-specific terminology and nominalization. Articulates technical mechanisms, industry standards, regulatory compliance, and institutional operations in formal news prose.
Level 3 Speculative (In-Depth News Commentary) Contemporary editorial analysis, policy debate, and strategic industry commentary. Examines long-term industry trends, public policy trade-offs, regulatory debates, and societal implications in modern analytical journalism.

3.2 Hidden-Layer Embedding Bank Construction and Perturbation

To generate varied text samples from these categories without drifting from natural human distributions, we adapt the continuous latent space sampling approach of Bowman et al. (2015) in the d = 5,376 hidden space of Gemma-4-31B:

  1. Centroid Extraction & Principal Subspace Compression: For each node $(c_i, t_{i,j}, l)$, human-written reference passages from uncontaminated web/news archives (29,985 passages across the 1,999 non-adult categories) are passed through Gemma-4-31B (d = 5,376). We extract IDF-weighted and contextualized sequence-pooled hidden states to form $\mathbf{e}{i,j,l} \in \mathbb{R}^{5376}$, storing the 6,000 category-level centroids $\bar{\mathbf{e}}{i,l} \in \mathbb{R}^{2000 \times 3 \times 5376}$ and projecting the 30,000 sub-topic residuals onto a shared 256-dimensional principal subspace $\mathbf{U} \in \mathbb{R}^{256 \times 5376}$ with coefficients $\mathbf{C}_{i,j,l} \in \mathbb{R}^{2000 \times 5 \times 3 \times 256}$:

ei,j,l=normalize⁑ ⁣(eΛ‰i,l+Ci,j,lU)\mathbf{e}_{i,j,l} = \operatorname{normalize}\!\left(\bar{\mathbf{e}}_{i,l} + \mathbf{C}_{i,j,l}\mathbf{U}\right)

  1. Manifold-Constrained Random Perturbation: Because the hidden space of a transformer is anisotropic, unconstrained Gaussian noise across all dimensions pushes vectors off the semantic manifold. We compute the local empirical covariance $\boldsymbol{\Sigma}_{i,l}$ within category $c_i$ at level $l$:

Ξ£i,l=1Nexβˆ’1βˆ‘n=1Nex(hi,l,nβˆ’eΛ‰i,l)(hi,l,nβˆ’eΛ‰i,l)⊀+Ξ»Id\boldsymbol{\Sigma}_{i,l} = \frac{1}{N_{\text{ex}} - 1} \sum_{n=1}^{N_{\text{ex}}} (\mathbf{h}_{i,l,n} - \bar{\mathbf{e}}_{i,l})(\mathbf{h}_{i,l,n} - \bar{\mathbf{e}}_{i,l})^\top + \lambda \mathbf{I}_d

During generation for $(c_i, t_{i,j}, l)$, we sample $\boldsymbol{\epsilon} \sim \mathcal{N}(\mathbf{0}, \mathbf{I}{256} / 256)$ within the principal manifold subspace and scale by the empirical dispersion $\sigma{i,l}$ and perturbation hyperparameter $\gamma = 0.45$:

h~i,j,l=normalize⁑ ⁣(ei,j,l+Ξ³Οƒi,lΟ΅U)\tilde{\mathbf{h}}_{i,j,l} = \operatorname{normalize}\!\left(\mathbf{e}_{i,j,l} + \gamma \sigma_{i,l} \boldsymbol{\epsilon}\mathbf{U}\right)

  1. Centered Concept Readout & Zero-Tokenizer Multilingual Decoding: The perturbed vector $\tilde{\mathbf{h}}{i,j,l} \in \mathbb{R}^{5376}$ queries the corpus-mean-centered unembedding matrix $\mathbf{W}{\text{centered}} \in \mathbb{R}^{50780 \times 5376}$ of Gemma-4-31B to extract 7 distinct domain concept tokens, which are spliced directly into pre-tokenized 6-language x 3-level prompt templates and decoded autoregressively:

P(Y∣h~i,j,l)=∏t=1mP(yt∣y<t,h~i,j,l)P(Y \mid \tilde{\mathbf{h}}_{i,j,l}) = \prod_{t=1}^{m} P(y_t \mid y_{<t}, \tilde{\mathbf{h}}_{i,j,l})

3.3 Unified BF16 Model Architecture (textgeneratorc2k in model.safetensors, 59.13 GB)

To minimize package footprint while maintaining full bfloat16 precision and native transformers.pipeline compatibility, textgeneratorc2k packs the gemma-4 generator, the storygeneratorc2k latent bank, embeddinggemma-300m, and the userneeds2k classifier into a single 59.13 GB BF16 model.safetensors checkpoint (1,134 tensors):

  1. 58-Layer 6-Language Gemma-4-31B Generator (lm.*, bfloat16, 58.52 GB):
    • Depth Pruning (60 -> 58 layers): Drops two redundant first-half sliding-attention layers ({13, 14} with angular distance $< 0.045$), preserving all 10 global full_attention layers (head_dim = 512, num_global_key_value_heads = 4) and all second-half layers (30..59) untouched.
    • 6-Language Vocabulary Pruning (262,144 -> 175,359 tokens): Retains 100% of tokens for English (en), Chinese (cn), German (de), Spanish (sp), French (fr), and Japanese (jp).
  2. Integrated storygeneratorc2k 3-Level Latent Bank (bfloat16, 84.59 MB):
    • 3-Level Latent Bank (level_centroids, topic_subspace_coeffs, subspace_basis, sigma_il, embed_mean): Stored as registered bfloat16 buffers on the model.
  3. Integrated EmbeddingGemma-300M + userneeds2k 2,000-Category Classifier (bfloat16, 522.01 MB):
    • EmbeddingGemma-300M (embeddinggemma.*, embedding_dense1.weight, embedding_dense2.weight, 481.86 MB): 24-layer Gemma3TextModel (hidden_size = 768, aligned to the shared 175,359 6-language vocabulary) plus two dense projection layers (768 -> 3072 -> 768).
    • Dynamic-Negative userneeds2k MLPClassifier (classifier.*, 768 -> 2048 -> 2000, bfloat16): LayerNorm + Linear(768, 2048) + GELU + Linear(2048, 2000) trained with dynamic hard-negative binary cross-entropy across all 2,000 categories to compute categorical_scores.

4. Empirical Performance & Package Layout

4.1 2,000-Category Classification Benchmark (NVIDIA H100 NVL)

Evaluation Split nDCG@5 Precision@5 Recall@5 Top-1 Hit Tail Recall@5
English Held-Out Test (25,874 samples) 0.811408 0.214802 0.854909 0.731004 0.849272
Multilingual Test (1,000 samples) 0.795043 0.210200 0.838467 0.713000 0.831500

4.2 100-Pair Blind Human-vs-Generator Turing Benchmark (1,000 Evaluations Across 10 Frontier Models)

To evaluate how often paragraphs synthesized by TextGeneratorC2K (storygeneratorc2k + Gemma-4-31B BF16) are judged as authentic human journalism against real web-extracted prose, we conducted a strictly blind 100-Pair A/B Turing Benchmark (human_turing_100pairs_benchmark.json):

  1. 100 Original Google Samples: Selected across 100 distinct non-adult categories and all 3 sociolinguistic registers (34 Level 1 Basic, 33 Level 2 Advanced, 33 Level 3 Speculative; 103.3 words and 4.45 sentences on average).
  2. 100 Matched TextGeneratorC2K Samples: Generated on an NVIDIA H100 NVL (55.8s total across 4 batches of 25) conditioned on the exact same (category_id, level) (124.3 words and 5.43 sentences on average).
  3. 1,000 Blind A/B Evaluations Across 10 Frontier Models: For each pair, Passage A and Passage B were randomized and presented without source labels to 5 flagship open-source models (via Ollama Cloud API) and 5 flagship closed-source models (via Poe API, local OpenAI Codex CLI, and native AGY Gemini Subagents), asking: "Which passage reads more like an authentic, well-edited contemporary human news paragraph for this category and register?"

Table 4.2.1 β€” Overall & Model-Family Summary (1,000 Blind Evaluations)

Judge Cohort Total Evaluations TextGeneratorC2K Chosen as Human Google Original Chosen as Human Ties TextGeneratorC2K Win Rate Human Indistinguishability Score (Win + 0.5Γ—Tie)
Open-Source Frontier Models (5 Ollama Models) 500 175 323 2 35.00% 35.20%
Closed-Source Frontier Models (3 Poe + Codex + AGY) 500 76 423 1 15.20% 15.30%
All 10 Frontier Judge Models (Combined) 1,000 251 746 3 25.10% 25.25%

Table 4.2.2 β€” Per-Judge-Model Breakdown (100 Blind Pairs per Judge)

Judge Model Model Family / Provider TextGeneratorC2K Chosen as Human Google Original Chosen as Human Ties TextGeneratorC2K Win Rate Indistinguishability Score
mistral-large-3:675b Open-Source (Ollama API) 58 42 0 58.00% 58.00%
gpt-oss:120b Open-Source (Ollama API) 47 53 0 47.00% 47.00%
kimi-k3 Open-Source (Ollama API) 29 70 1 29.00% 29.50%
deepseek-v4-pro:0813 Open-Source (Ollama API) 27 73 0 27.00% 27.00%
gemini-3.8-flash Closed-Source (Poe API) 27 72 1 27.00% 27.50%
gpt-5.4 Closed-Source (Poe API) 20 80 0 20.00% 20.00%
glm-5.3 Open-Source (Ollama API) 14 85 1 14.00% 14.50%
claude-sonnet-5.5 Closed-Source (Poe API) 11 89 0 11.00% 11.00%
gemini-agy-subagent Closed-Source (AGY Subagent) 11 89 0 11.00% 11.00%
gpt-6-astra Closed-Source (Local Codex CLI) 7 93 0 7.00% 7.00%

Table 4.2.3 β€” Breakdown by Sociolinguistic Register Level (Level 1, Level 2, Level 3)

Sociolinguistic Register All 10 Models (TextGeneratorC2K Rate) Open-Source Judges (500 Evals) Closed-Source Judges (500 Evals) Key Linguistic Finding
Level 1: Basic (34 Pairs β€” Consumer & Community News) 44.12% (150 / 340, 3 ties) 55.29% (94 / 170, 55.88% score) 32.94% (56 / 170, 33.24% score) TextGeneratorC2K beats Google Original (55.29% vs. 43.53%) on Open-Source Judges and wins nearly a third of Closed-Source evaluations due to grounded community narrative flow.
Level 2: Advanced (33 Pairs β€” Industry & Technical News) 6.06% (20 / 330, 0 ties) 10.30% (17 / 165) 1.82% (3 / 165) Real Google industry excerpts cite named executives, exact model numbers, and dated regulatory filings that closed-source judges prioritize over abstract technical synthesis.
Level 3: Speculative (33 Pairs β€” In-Depth News Commentary) 24.55% (81 / 330, 0 ties) 38.79% (64 / 165) 10.30% (17 / 165) Strong analytical commentary performance (38.79% across open-source judges) where TextGeneratorC2K's policy/forecasting structure outperforms noisy web excerpts.

Key Diagnostic Insights from Judge Justifications

  1. Superiority in Level 1 Community Journalism (55.29% Open-Source / 44.12% Overall): In Level 1: Basic, TextGeneratorC2K consistently generates cohesive, well-edited community news stories that outperform raw web-scraped excerpts containing promotional copy, first-person fragments, or academic citations. Across mistral-large-3:675b (58.0%) and gpt-oss:120b (47.0%), TextGeneratorC2K is judged overall as equal to or more human-authentic than original Google web samples.
  2. Cross-Lingual Concept Leakage in Shared 6-Language Subspace: Closed-source reasoning judges (gpt-6-astra, claude-sonnet-5.5, gemini-agy-subagent) identified two primary giveaways when rejecting TextGeneratorC2K passages in Level 2 and Level 3:
    • Because valid_words spans the unified 6-language (en, zh, es, id, pt, it) vocabulary, non-English Latin-script concept words (trabajadores, bayi, playas, lucro, jornal) occasionally ranked in the top-7 cosine neighbors of h_tilde and were injected into the English prompt.
    • Real Level 2 trade articles cite specific named institutions, SKU/engine numbers, and direct quotes from named researchers, whereas zero-shot latent prompts without named-entity grounding produce more abstract institutional transitions.

4.3 Repository Files (textgeneratorc2k / storygeneratorc2k)

File / Directory Description
model.safetensors Unified BF16 (bfloat16) weights containing the 58-layer 6-language Gemma-4-31B generator, the storygeneratorc2k 3-Level 5,376-D Latent Bank, the EmbeddingGemma-300M + userneeds2k 2,000-category MLPClassifier, and compressed runtime metadata.
google_embeddinggemma-300m/ Self-contained google/embeddinggemma-300m (Gemma3TextModel + 1_Pooling + 2_Dense + 3_Dense in bfloat16) subdirectory for standalone document embedding and userneeds2k categorical scoring.
catid_define.json Minified human-readable 6-language (en, cn, de, sp, fr, jp) category taxonomy definitions (1,999 non-adult categories).
config.json Standard Hugging Face PreTrainedConfig with auto_map and custom_pipelines registration for transformers.pipeline.
modeling_textgeneratorc2k.py Hugging Face PreTrainedModel and Pipeline hook enabling direct execution via from transformers import pipeline.
README.md This design document and model card.

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