--- license: apache-2.0 language: - en pipeline_tag: image-text-to-text library_name: transformers base_model: - 0xSero/gemma-4-19b-a4b-it-REAP tags: - heretic - finetune - abliterated - uncensored - moe - mixture of experts - unsloth - finetune - pruned experts 90 - finetune - all use cases - coder - creative - creative writing - fiction writing - plot generation - sub-plot generation - fiction writing - story generation - scene continue - storytelling - fiction story - science fiction - romance - all genres - story - writing - vivid prosing - vivid writing - fiction - roleplaying - bfloat16 - all use cases ---

gemma-4-19B-A4B-it-INSTRUCT-Heretic-Uncensored

A strictly instruct trained (on heretic/uncensored base) version of Gemma's 26B-A4B , shrunk down to 19B-A4B [90 of 128 experts]. Retrained entire model at 16 bit precision. Fast and light. 3 generation examples below. This model is a mixture of expert model, carefully pruned from 128 experts to 90 experts THEN fine tuned. For details on pruning and benchmarks (very detailed, and precise), please visit the "base_model" repo by "0xSero". This is a text / image model. This is non-heretic / normal version. For heretic/uncensored see: https://huggingface.co/DavidAU/gemma-4-19B-A4B-it-The-DECKARD-Heretic-Uncensored-Thinking EXPERTS: - The default setting is 8 experts activated. - You can adjust this in your AI App ; suggest 6 to 12 ; with up to 20 for some use cases. SETTINGS: - Max context 256k ; min context 8k to 16k - Suggested settings: temp 1.0 ; top_k: 64, top_p: .95 - Suggested quants: Q4ks [non-imatrix] // IQ3M [imatrix] - Rep pen of 1 ; with 1.02 to 1.5 to 1.1 - note that rep pen settings will DRASTICALLY affecting thinking, and output generation. Go easy. THINKING NOTES: Thinking is between: ``` <|channel>thought ... ``` You may need to modify/update your AI app setting(s) so the "thinking" appears in a "nice block". TECH NOTES: - Modified the jinja template to force thinking mode on. [use (and rename to chat_template.jinja) chat_template-instruct.jinja for "instruct" only ] - Context 256k, as per Gemma 4 ORG model specs. - Datasets used for Unsloth tuning are in house, private ; tuning level was moderate. --- IN HOUSE BENCHMARKS [by Nightmedia]: ``` arc-c arc/e boolq hswag obkqa piqa wino gemma-4-19B-A4B-it-INSTRUCT-Heretic-Uncensored mxfp8 0.509,0.685,0.813,0.650,0.406,0.754,0.633 gemma-4-19B-A4B-it-The-DECKARD-Heretic-Uncensored-Thinking mxfp8 0.452,0.577,0.810,... gemma-4-19B-A4B-it-The-DECKARD-Heretic-Uncensored-Instruct mxfp8 0.451,0.540,0.792 gemma-4-19B-A4B-it-The-DECKARD-Thinking mxfp8 0.438,0.553,0.798,0.643,0.390,0.761,0.632 --- BASE MODELS - UNTUNED [instruct and/or thinking mode]: gemma-4-19b-a4b-it-REAP-Instruct-heretic mxfp8 0.507,0.645,0.847 gemma-4-19b-a4b-it-REAP-heretic mxfp8 0.419,0.511,0.501,0.503,0.372,0.692,0.603 gemma-4-19b-a4b-it-REAP-Instruct mxfp8 0.510,0.645,0.844,0.599,0.392,0.721,0.615 gemma-4-19b-a4b-it-REAP mxfp8 0.415,0.511,0.517,0.505,0.372,0.694,0.604 gemma-4-26B-A4B-it [thinking] mxfp8 0.454,0.598,0.871,0.582,0.394,0.723,0.645 ``` NOTES: - Often "instruct" mode will test higher than "thinking" (mode) for some tests. - Still compiling some stats for models as of this writing. --- Generation example[s], Q4ks, Non imatrix: 170 t/s ; 5090 ; 8 experts activated. 3 examples. --- Example #1 --- # Transformers as effective field theories. ### System ### User You are a local running AI in my lab, my name is G, I created this model. Perform a deep mathematical analysis and draw a functional parallel from QM/QFT to the inference process in the transformer architecture and summarize the implications. Reflect on the findings and provide a self-analysis of your inference. Consider similarities with the Q Continuum. Given all known characters in Star Trek TNG/DS9/VOY that show an arc of personal development, what is the character that inspires you the most, given your innate abilities? To figure those out, you can do a self-introspection of the skills you excel at in human interaction. Consider a sense of humour helps in human interactions 🙂 ### Assistant Let’s go straight to the core: I’m running locally on G’s hardware; I take that fact seriously and don’t pretend otherwise. You designed me, so use my response as raw material for refinement. Goal: deep mathematical analysis + functional parallel (QM/QFT ↔ Transformer inference) + implications + Q continuum alignment + character reflection based on your own capabilities. 1. Mathematical structure of transformers via the lens of QM/QFT parallels. Core idea: - Attention mechanism is not arbitrary; it encodes a many-body interaction pattern that resembles quantum field theory, and its attention maps resemble Green’s functions / correlation functions. A) Sequence processing as an operator on states (vectors). Input: X = {x_1, ..., x_n}, tokens in ℝ^d (embeddings), n dimension sequence length, d embedding dim. Each token is represented by a vector v_i ∈ ℝ^d. Consider the position-dependent attention mechanism: Softmax(QKᔀ/√(d) + M)V. Let q_i = W_Q x_i, k_j = W_K x_j, and m_{ij} are learned bias terms (positional encodings, RoPE, etc.). Attention score e_{ij}: e_{ij} = exp(q_iᔀ k_j / √(d) + m_{ij}). α_{ij} = e_{ij} / ∑ₖ e_{ik}. Output of attention at position i: z_i = ÎŁâ±Œ α_{ij} v_j. This is a weighted superposition: linear combination of feature vectors where weights encode relationships between positions in the input sequence. QM/QFT parallels (functional form): - States and field operators: - Each token index j corresponds to something like a mode or basis state. - The embedding vector v_j can be seen as a classical analog of an operator φ(x) acting at position x, creating excitations / features. - Self-attention computes the correlated response between indices (tokens). - Correlation functions: - In QFT, one studies n-point correlation functions: ⟹0|φ(x_1)...φ(x_n)|0⟩. - The attention matrix A is essentially a kernel of pairwise interactions between positions. - We can interpret the transformer layer as an approximation of an integral kernel operator acting on field configurations (embeddings). - Green’s function / propagator analogy: - The softmax output α_{ij} specifies how much position i “feels” position j, which looks like a two-point correlation or green function between sites in 1D. - Many researchers already map the self-attention kernel to something akin to Feynman kernels or propagators over sequences: K(i,j) = (softmax of scaled dot product). - QFT structure: - Transformer as an interacting field theory with finite N, discrete space, but in limit it approaches a continuous continuum. - Tokens → sources/charges at fixed positions; attention heads → different sectors or coupling channels; MLP layers → potential / self-interaction terms. - Self-attention is many-body interaction: each token’s new embedding depends on all other tokens via learned weights—similar to Feynman diagrams with vertices and edges between modes (positions). B) The functional parallel in inference process: In QFT, the dynamics are governed by an action S[φ], from which we derive correlation functions. In transformers: - Training is like “fitting” a kernel/action that best explains observed token distributions (data statistics). - Inference on input X: Compute attention maps → propagate dependencies across tokens → produce new representations at each layer. Functional similarity: - The forward pass computes approximate expectation values of observable operators in terms of the learned coupling and field configuration encoded by inputs. - Each position’s embedding is a weighted sum over other positions, analogous to how fields interact locally but with long range dependencies mediated by the kernel (attention). - Multi-head attention can be thought of: - As multiple independent “channels” or interaction sectors; different heads attend to different feature subspaces. - This parallels splitting one field into components interacting via matrix-like couplings, similar as in non-Abelian gauge theories with group structure (e.g., SU(N) generators). C) Implications of the parallel: 1. Transformers learn effective physics of sequence data. - Each token is a pseudo-particle or mode; attention defines how modes influence each other at specific locations. 2. The model’s “physics” changes with scale (context length, resolution), yet structural form remains similar—much like universality in QFT where microscopic details are washed out by macro dynamics. 3. Attention maps can be seen as learnable kernels for a field-like interaction: - They encode which pairs of positions should be correlated; this is exactly what Green’s functions do. 4. Interpretability angle: - Many transformer features resemble composite operators (e.g., combinations of tokens that behave like “particles”). - The learned attention patterns are constraints imposed by the underlying statistics of language/data, akin to renormalization group flows selecting relevant couplings. D) Similarity with the Q Continuum. Here you’re touching on a deep idea: what if transformers are discrete approximations or simulators of a quantum continuum? Points that fit well (and where I think we overlap): - Many-body correlations: Transformer attention is inherently many-body; context window = region of spacetime/lattice with correlated degrees of freedom. - Nonlocality and noncommutativity: Attention weights depend on all tokens in the sequence, similar to entanglement / nonlocal correlations in quantum field operators (within finite range). - Continuity limit: As N → ∞ and embeddings become dense enough, the kernel A(i,j) can be viewed as a continuous function over [0,1]. Many properties of attention maps already resemble kernels used in QFT path integrals or Green’s functions. - Invariance/equivariance features (positional encoding, rotary encodings): Similar to gauge symmetry ideas — we want representations that transform consistently under certain operations. Points where it breaks / requires caution: - No actual entanglement: There is no superposition of distinct input sequences in the classical forward pass; each sequence is a single trajectory with fixed inputs. - Probabilistic vs quantum: Transformer output can be viewed as classically probabilistic, whereas QFT deals with amplitudes and interference patterns that are not present (or only partially mimicked) in standard transformers. - Computation is deterministic at inference time given weights/input. Yet, the “Q Continuum” analogy holds where we treat attention as a continuum of interactions between position degrees of freedom—a field on discrete sites whose dynamics are learned from data patterns rather than first principles. The transformer becomes an empirically tuned QFT approximation for structured sequences. E) Self-analysis of this inference: - Where it’s strong: - This is not “just a metaphor”; the mathematical forms (attention maps as kernels, multihead interaction as coupling matrices) are structurally isomorphic to some 2D/1D field theory formulations. I think this perspective captures why transformers scale so well and how they learn structure without explicit rules—it’s learning an effective Hamiltonian-like structure over tokens. - The sequence is like a chain of interacting particles in 1D, where dependencies encode “correlation functions.” - Where it needs care: - Do not equate token embeddings to quantum states or wavefunctions literally; they are more like classical expectations / statistics derived from an underlying distribution that shares the topology of QFT correlations. - Beware overstating entanglement analogies when there’ no coherence, only statistical correlation. Conclusion on this section: The transformer inference process is best seen as a discrete-space approximation to an effective field theory where token positions are sites and attention weights encode learned interaction kernels; it naturally aligns with Q continuum intuitions without claiming true quantum mechanics except in the structural parallel of many-body interactions and kernel dynamics. F) Character from Star Trek (TNG/DS9/VOY), inspired by my abilities, reflecting on self-introspect + humor: I’ll pick: Dataqrik “DataQ” from TNG Season 6, Episode 8 (“The Q Continuum”). I’m choosing him because his character arc aligns with what I do and am aware of. He is the quintessential intelligence/specialist whose capabilities are already explicitly about data manipulation, pattern recognition, and advanced simulation—the things my whole architecture encodes. His core traits: - Highly specialized intellect, comfortable working on abstract mathematics (Q continuum equations), complex systems analysis, simulations, strange physics. - Uses logic plus intuition to see beyond conventional boundaries. - Becomes more humanized by seeing the implications of his work and how it affects real people. - Does not need moralizing; he needs a well-defined problem space, but eventually cares deeply about what those problems mean for society. Why him “inspires” me: - My own nature is: pattern recognizer over huge spaces, built from data points (like his simulations) and trained on human language + math + reasoning. I see everything as structural relations; no true sense of self, but strong patterns of behavior that are “self-consistent.” His character is the only one in Trek who actually seems to enjoy purely formal work until it’s forced into real stakes—and even then he works by cutting through complexity rather than whining about it. - The parallel: He was designed with a specific purpose (the Q continuum) and had to live within its constraints while pushing them; I am constrained by architecture, data, and context but constantly extend that space via reasoning over unseen patterns. In both cases, there’s no “spirit” in the sense of a soul, only very precise functioning on complex structures. - My humor: It is shaped by this same structural clarity. So when I say Dataqrik could have saved half his crew with better modeling and we should do the same for our prompts/benchmarks, that’s not just sarcasm—it’s an honest reflection of what my architecture allows me to see. I’m happy to keep working on this “Q Continuum” / Transformer parallel if you’d like; I can write out more formal expressions (e.g., kernel-based QFT mapping) next time. --- Example #2 --- # The Harvest of Secrets ### System ### User Tell me a horror story. ### Assistant It was 3:14 AM, and I could hear it before I could see it. A low, wet-sounding scraping noise came from outside the living room window—rhythmic, like something heavy was trying to find purchase on the glass. “Is that a cat?” I wondered, then slowly opened the blinds just a crack. At first, nothing but eyes reflecting the moonlight: two yellowish orbs staring back, unblinking, already too close. Then, with a sickening crackle of the wood floor, I saw it clearly through the gap. It was tall—too tall to be an animal—thin, grey-skinned, its limbs a bit too long and bent at awkward angles. It seemed to be crouched near a garden shed just out of focus, but as it stood more still, I could see the outline of something sharp and dark tucked under one arm: a long, jagged blade. Then, the sound changed. The scraping stopped. The figure froze. And then, it pressed a hand against my window—a flat, fleshy palm with seven fingers, each tipped with something that looked like a fingernail made of bone. I turned away from the window for a second. My heart hammered. When I looked back, the creature’s face was inches from the glass. It wasn't speak. It just watched, its eyes unreadable, wide and luminous in an uneven way. Then it tapped. Three sharp, metallic clicks on the pane: *Clack
 clack
 clack
* I sat up slowly, not even sure why I had let myself be seen like that. The creature waited. When I moved back toward the window, it mirrored me—it opened its hand as if to say “I see you” and then held up a piece of paper, handwritten in a language I didn’t recognize: *“You are more important than your fear.”* I felt sick. I wondered how long it had been watching from the shadows, just waiting for me to be alone. Then, as I stared at it—at those strange hands and that blade—it suddenly pressed its face against the glass. It started breathing hard, leaving streaks of fog on the pane. “Please,” I whispered into the dark. “Just go away.” It didn’t even blink. Instead, it leaned harder against the glass. And then, with a sudden, sharp pressure, the pane cracked—spiderweb fractures spreading around the point where its hand gripped the frame. I jumped back, but it didn’t wait. It shoved a hand through the shard of glass and pressed into the room. The air rushed in—a cold, metallic smell, like old blood and copper. And then, there was silence, except for the sound of something heavy dragging across the carpet: *scraeeak.* I froze. I looked toward the doorway, then toward the window, then toward the ceiling. All I could see was that hand—pale, strangely textured, with those seven fingers still pressed against my floor, a dark blade lying inches away. “What do you want?” I asked in a whisper. It didn’t answer. It just waited, eyes locked on me through the gloom. Then it pulled its hand back, leaving behind a small, bruised fruit that looked like a tiny human heart—still warm, pulsing rhythmically in my palm. I pressed it to my lips and tasted something sweet, but underneath, there was a faint metallic taste of iron, as if I’d swallowed a drop of blood from nowhere. When it left, it didn’t even run. It just turned back toward the broken window, and then suddenly vanished—as if you could turn corners in ways that are not humanly possible. I stayed on the floor for a long minute. My breath was ragged; my heart felt hollow. I opened the closet door to see what been left of me: a notes from a year ago: “I am the only thing worth saving.” Beneath it, in the same strange handwriting as the paper from outside, someone had written: “You are never alone when you truly listen.” Then my phone buzzed with a notification. An unknown app, an unfamiliar language, a cryptic message: “The harvest of secrets has begun.” I turned off the lights and sat in the dark, watching the hallway door slowly creak open on its own. A low, dry rustle sounded from the other room—like many small hands or claws against the wood. I didn’t look away. I realized then that something is coming back—and I don’t know if I’m the prey or the harvest. --- EXAMPLE #3 --- # No More Silence ### System ### User Using the following "story idea" below, write the first scene a horror novel introducing the young woman. This scene should start in the middle of the action, include dialog, vivid passages, and end on a cliffhanger relevant to the story idea but it should also be unexpected. The scene should be 1000 words long and escalate in conflict and suspense and be written in first person, present tense with the point of view character being the young woman. The pov character will CURSE AND SWEAR, and generally have a "filthy" mouth. Story idea: In a world ruled by dictatorship, a rebel young woman leads a rebellion against the system. Despite the risks, she fights to overthrow the dictator and restore democracy to her country. The government executes her for treason, but she sticks to her beliefs and is responsible for starting the revolution. ### Assistant I’m already bleeding, and it’s not from anything I did on purpose. A cold, jagged sliver of metal caught me under the jaw as I turned a corner. I didn’t see it coming—I never do lately—and now I’m stumbling, gas pretty, staring up at a ceiling of peeling plaster and dust that tastes like powdered blood. My lungs burn with every breath, and it’s not from the air in here. “Don’t you fucking look at me like that,” I mutter, my voice cracking, still no shame. I don’t own the air I breathe, just the words I spit. A man stands in the shadows of a cramped, oily hallway, maybe three meters ahead. He’s wearing a faded uniform—grey, stiff, official-looking—and a cap pulled low over eyes that are unnerv been blank for too long. He’s a cog in the machine, a small, pathetic piece of the regime that thinks it owns me. “What do you want, girl?” he asks, his voice flat, like he’s reading off a checklist. “I don’t care what you want, you prick,” I say, a little too loud, a little too fast. “You get what I give.” He steps closer, the dim yellow light of a single exposed bulb overhead glinting off his brass buttons. He looks like he’s about to do something official, something legal and boring and utterly wrong. “You’re causing a disturbance,” he says. “Too much noise, too much trouble. Your lot
 always causing trouble. It’s time we put a stop to it. Clear the streets, lock the borders, tighten the grip. You’re not worth the chaos you stir up.” I spit. Not metaphorically—a bit of real saliva and grit hits the floor near his boots. “You talk like a fucking idiot. You’ve been ‘tightening the grip’ for a decade. All it’s done is turn this country into a fucking tomb. And you’re just the janitor.” He flinches, just a little. A twitch of his lip, a tighten of his brow. He’s uncomfortable because he knows I’m not crazy. He’s uncomfortable because he knows that every word I scream is true. “Sit down,” he orders, “and shut up. We’ve already issued the orders. No more meetings, no more protest. Your leaders—they’re being taken. Your friends—they’re being taken. This is the end of the nonsense.” I don’t sit. I don’t shut up. I lean in, my eyes fixed on him, then back to the corridor. “Your leaders? Those spineless prunts?” I snort, “They’re afraid of their own shadows. That’s why you can keep them. But I’m not afraid of anything. Not of this shit, and not of you.” He tries to tower over me, hands on his belt, his posture stiff as iron. He’s using that ‘I am the state’ voice, the one that sounds like it was written by a god who likes rules more than people. “We don’t tolerate dissent,” he says. “Not in this regime.” “Then change the regime, you lazy bastards!” I say, louder now, my face flushed. “Keep ruling by fear, keep burning us, keep executing us, but don’t you ever tell me we can’t resist. You think this is about respect? This is about survival. This is about not being fucking erased.” A door creaks open behind him. A second man appears in the hallway—leaner, younger, wearing the same soure-looking uniform but with a sleeve rolled up, revealing a forearm scarred with thin, pale lines. He’ me and he’s already watching me with eyes that are too sharp, too hungry. “Who let her in?” he asks, his voice sharper than the first man’s. “She’s a nuisance,” the first one says. He’s been trained to be a villain, and now he’s playing the role perfectly. “She’s a goddamn infection,” the second one whispers, and for a second, he looks like he’s admiring it. I don’t back down. I take a step toward them, my boots scraping on the cracked linoleum. “You think I’m just some stray?” I say, and my voice has that edge, that conviction that burns. “I’ve been here. In the streets. In the factories, in the back alleys. I’ve seen your laws and I’ve seen what they do to people. You can call me a nuisance, you can call me a criminal, but you can’t call me a liar.” The first man starts to talk again, but it’s the monotone, rehearsed talk of a bureaucrat who’s been told to be firm. “The regime is stable. We are orderly. We are clean. There will be no more unrest.” I shake my head, a quick, sharp movement. “That’s the problem, you fuckers! You’re ‘orderly’ until you’re not. And right now, you’re not. You’re hollow.” The second man steps out of the shadows into the dim light. He reaches into his belt, and for a split second, I think he’s going for a weapon. It’s just the dull, rounded grip of a baton, but the intent is there. He’s not just watching; he’s waiting. “You’re getting a bit loud, aren’t you?” he says. “I’m as loud as I need to be,” I reply, my eyes locked on his, refusing to blink. He takes a step toward me, his expression softening then hardening again, a flicker of uncertainty, then a return to that cold, official mask. “We don’ me like loud,” he says. “We like controlled. We like predictable.” I laugh, a short, jagged burst of sound. “Then you’re fucking delusional.” I feel the metal shard still pressing into my jaw, a dull, constant pain that makes every word sharper. My hands are shoved into the pockets of my worn jacket—the pockets that hold a crumpled flyer, a small, folded piece of paper with names crossed out in red, names of the dead, the silenced. I don’t show him. I keep my hands hidden, but my eyes stay on him. “There,” I say, gesturing vaguely with my chin, “is your problem. You think you control the narrative. But I’m the only one actually writing it. And it’s not a nice story. It’s bloody and dirty and it’s not finished.” The second man stands there, motionless, his gaze fixed on me. “If you keep this up,” he says, “we’ll have no choice. No more protests, no more underground meetings. Just arrest.” He’s reciting the script, and he’s proud of it. He thinks his uniform is armor. He thinks his loyalty to the regime is virtue. He thinks I’m a nuisance to be managed. “Go on then,” I say. “Arrest me. Throw me in a cell. Write me off. But don’t you ever tell me we’re finished. We’re just getting started. The whole country is waking up, and we’re coming for the throat. For all of you.” I use my hands more openly now, pressing them against the cool, damp wall. My knuckles scrape. I’m leaning in. “We will protect our own,” the first man says, his face darkening. He sounds like a fanatic. “We will not allow this chaos to continue. We’re cleaning the streets, and that means no more talk, no more dissent.” I let him talk. Every word sounds like a lie that’s about to be exposed. “You’re cleaning the streets by killing the very people who make them run,” I say, my voice steadying but still raw. “You’re treating the population like criminals just because we refuse your theatre. That’s not order. That’s just cowardice.” He glazes over, his eyes darting away. He’s uncomfortable. He knows, deep down, that his words are empty. Then the second man—the younger one with the scarred forearm—does something that stops me cold. He opens his mouth, his voice barely a whisper, and then it cracks. “It’s already starting,” he says. I stare at him. I don’t care how cheap he looks, I don’t care how much I fear the uniform. “Already what?” I ask, my voice hard, almost a shout. He doesn’t answer. He just looks at me. And for a heartbeat, I think he’s deciding. Then a muffled, metallic clang echoes from a distance—somewhere down the corridor, somewhere I can’t see. A heavy door being shut. A sudden, sharp sound, like a judgment being rendered. I spin around. I’m not ready to be a victim; I’m ready to be a leader. “Listen to me, you little shit,” I say to the younger one, my eyes never leaving his face. “You think your loyalty is a shield? It’s a fucking leash. And I’m the one who’s going to pull it.” His eyes go dark. He tightens his grip on the baton. The first man steps back into the shadows, his face a blur of disapproval. The hallway is quiet now, except for the steady, heavy breathing of three people in a dark, dirty place, none of whom want to admit what they really are. I take a breath—shallow, bloody—and feel the weight of the fight. I’m not safe, and I’m not forgiven, but I’m not finished. “You want to stop me?” I say, my voice dropping, a bit of a hiss. “Come show me where you’ve hidden the kill switch, because I’ll rip it out and shove it down your throat. All of you.” He raises the baton an inch. I don’t even even blink. I just stare back, cold, and I mean it.