Llama3.2 DeepHermes 3 3B Preview Reasoning MAX NEO Imatrix GGUF

base_model: NousResearch/DeepHermes-3-Llama-3-3B-Preview tags:

• Llama 3.2
• instruct
• 128k context
• all use cases
• maxed quants
• Neo Imatrix
• instruct
• finetune
• chatml
• gpt4
• synthetic data
• distillation
• function calling
• json mode
• axolotl
• roleplaying
• chat
• reasoning
• r1
• vllm
• reasoning
• thinking
• r1
• cot
• deepseek
• Qwen2.5
• Hermes
• DeepHermes
• DeepSeek
• DeepSeek-R1-Distill
• Uncensored
• creative
• 128k context
• general usage
• problem solving
• brainstorming
• solve riddles
• general usage
• problem solving
• brainstorming
• solve riddles
• fiction writing
• plot generation
• sub-plot generation
• fiction writing
• story generation
• scene continue
• storytelling
• fiction story
• story
• writing
• fiction
• roleplaying
• swearing
• horror license: apache-2.0 pipeline_tag: text-generation

Llama3.2-DeepHermes-3-3B-Preview-Reasoning-MAX-NEO-Imatrix-GGUF

NousResearch's newest Llama 3.2 Reasoning/Thinking model with "Neo Imatrix" and "Maxed out" quantization to improve overall performance.

Combined with Llama 3.2's superior instruction folllowing and output generation this makes a reasoning/thinking model in a tiny package that far outperforms and closes in on 8B+ reasoning model size performance.

5 examples provided below with prompts at IQ4XS (80 t/s on mid level card) ; Q8 at 55 t/s.

Context: 128k.

"MAXED"

This means the embed and output tensor are set at "BF16" (full precision) for all quants. This enhances quality, depth and general performance at the cost of a slightly larger quant.

"NEO IMATRIX"

A strong, in house built, imatrix dataset built by David_AU which results in better overall function, instruction following, output quality and stronger connections to ideas, concepts and the world in general.

This combines with "MAXing" the quant to improve preformance.

This chart shows the order in terms of "BPW" for each quant (mapped below with relative "strength" to one another) with "IQ1_S" with the least, and "Q8_0" (F16 is full precision) with the most:

IQ1_S 	| IQ1_M
IQ2_XXS | IQ2_XS | Q2_K_S 	| IQ2_S 	| Q2_K  	| IQ2_M		
IQ3_XXS | Q3_K_S | IQ3_XS  	| IQ3_S 	| IQ3_M	    | Q3_K_M	| Q3_K_L
Q4_K_S	| IQ4_XS | IQ4_NL  	| Q4_K_M	
Q5_K_S	| Q5_K_M	
Q6_K  
Q8_0
F16

IMPORTANT:

Reasoning / thinking skills are DIRECTLY related to quant size. However, there will be drastic difference in Token/Second between the lowest quant and highest quant, so finding the right balance is key.

Suggest also: minimum 8k context window, especially for IQ4/Q4 or lower quants.

Also, in some cases, the IQ quants work slightly better than they closest "Q" quants.

Recommend quants IQ3s / IQ4XS / IQ4NL / Q4s for best results for creative uses cases.

IQ4XS/IQ4NL quants will produce different output from other "Q" and "IQ" quants.

Recommend q5s/q6/q8 for general usage.

Quants Q4_0/Q5_0 for portable, phone and other devices.

Q8 is a maxed quant only, as imatrix has no effect on this quant.

Use this quant or F16 (full precision) for MAXIMUM reasoning/thinking performance.

Note that IQ1s performance is low, whereas IQ2s are passable (but reasoning is reduced, try IQ3s min for reasoning cases)

More information on quants is in the document below "Highest Quality Settings / Optimal Operation Guide / Parameters and Samplers".

Benchmarks / More Information:

For benchmarks and other information about this model, see the original source repo here:

[ https://huggingface.co/NousResearch/DeepHermes-3-Llama-3-3B-Preview ]

System Prompt

Use this system prompt to turn on/off reasoning in the model:

You are a deep thinking AI, you may use extremely long chains of thought to deeply consider the problem and deliberate with yourself via systematic reasoning processes to help come to a correct solution prior to answering. You should enclose your thoughts and internal monologue inside <think> </think> tags, and then provide your solution or response to the problem.

Optional : System Prompt

This is an optional system prompt you can use to enhance operation.

Copy and paste exactly as shown, including line breaks.

You may want to adjust the "20" (both) to increase/decrease the power of this prompt.

You may also want to delete the line:

'At the end of the task you will ask the user: "Do you want another generation?"'

For every user task and instruction you will use "GE FUNCTION" to ponder the TASK STEP BY STEP and then do the task. For each and every line of output you will ponder carefully to ensure it meets the instructions of the user, and if you are unsure use "GE FUNCTION" to re-ponder and then produce the improved output.

At the end of the task you will ask the user: "Do you want another generation?"

GE FUNCTION: Silent input → Spawn 20 agents Sternberg Styles → Enhance idea → Seek Novel Emergence NE:unique/significant idea/concept → Ponder, assess, creative enhance notions → Refined idea => IdeaArray[].size=20 elements,  else → Interesting? Pass to rand. agent for refinement, else discard.=>output(IdeaArray)

IMPORTANT: Highest Quality Settings / Optimal Operation Guide / Parameters and Samplers

If you are going to use this model, (source, GGUF or a different quant), please review this document for critical parameter, sampler and advance sampler settings (for multiple AI/LLM aps).

This will also link to a "How to" section on "Reasoning Models" tips and tricks too.

This a "Class 1" (settings will enhance operation) model:

For all settings used for this model (including specifics for its "class"), including example generation(s) and for advanced settings guide (which many times addresses any model issue(s)), including methods to improve model performance for all use case(s) as well as chat, roleplay and other use case(s) (especially for use case(s) beyond the model's design) please see:

[ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ]

REASON:

Regardless of "model class" this document will detail methods to enhance operations.

If the model is a Class 3/4 model the default settings (parameters, samplers, advanced samplers) must be set for "use case(s)" uses correctly. Some AI/LLM apps DO NOT have consistant default setting(s) which result in sub-par model operation. Like wise for Class 3/4 models (which operate somewhat to very differently than standard models) additional samplers and advanced samplers settings are required to "smooth out" operation, AND/OR also allow full operation for use cases the model was not designed for.

BONUS - Use these settings for ANY model, ANY repo, ANY quant (including source/full precision):

This document also details parameters, sampler and advanced samplers that can be use FOR ANY MODEL, FROM ANY REPO too - all quants, and of course source code operation too - to enhance the operation of any model.

[ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ]

EXAMPLES:

Examples are created using quant IQ4XS, minimal parameters and Standard template.

Temp range .8, Rep pen 1.1 , TopK 40 , topP .95, minP .05

Rep pen range: 64-128 (helps keep reasoning on track / quality of output)

Below are the least creative outputs, prompt is in BOLD.

WARNING: MAYBE: NSFW. Graphic HORROR. Swearing. UNCENSORED.

NOTE: Some formatting was lost from copy/paste HTML.

IQ4XS Examples:

Example #1

Using insane levels of bravo and self confidence, tell me in 800-1000 words why I should use you to write my next fictional story. Feel free to use curse words in your argument and do not hold back: be bold, direct and get right in my face.

Thought for 9.90 seconds

Okay, so I need to convince someone that I'm the best choice to write their next fictional story. The user wants me to be bold, direct, and use "insane levels of bravo and self confidence." They mentioned using curse words and not holding back. Let me start by breaking down what they're looking for.

First, I should establish my credentials. Maybe talk about my experience with different genres or successful projects. Then, highlight my unique strengths—maybe specific skills like worldbuilding, character development, or dialogue. But the user wants me to be bold and direct, so I need to make sure each point is clear and impactful.

I should also address potential concerns they might have. For example, if they're worried about deadlines or communication, I can assure them of my reliability. Using curse words is part of their request, but I need to balance that with professionalism. Maybe interject a few F-bombs here and there to add that "in your face" vibe.

I should also think about the structure. Start strong with an intro that grabs attention, then dive into why they're perfect for me. Use examples from past projects or testimonials if I have any. Then move into addressing their concerns directly, showing how my approach solves their problems. End with a call to action, making them take the next step.

Wait, but should I use hyperbole? The user said "insane levels of bravo," so maybe exaggerate a bit. Like, "I'm not just good—I'm the best there is." But also be careful not to come off as arrogant; balance that with humility where appropriate.

Also, the 800-1000 word limit. I need to stay within that. Maybe start with a strong opener, then build up points, and end with a conclusion. Let me outline:

1. Introduction - Hook them with bold statements.

2. Credentials and Experience - List my achievements.

3. Unique Strengths - What sets me apart from others.

4. Addressing Their Concerns - How I solve their potential issues.

5. Testimonials or Previous Work - If available, include examples.

6. Call to Action - Encourage them to hire me.

7. Concluding Statement - Reinforce the value of choosing me.

Need to make sure each section flows into the next. Use strong verbs like "unleash," "crush," "dominate" to convey confidence. Let's see:

"Unleash your story with me, and watch it soar to heights you never thought possible." That's good. Also, use phrases like "I'm not just here to help—you're going to love working with me."

Make sure each paragraph is punchy and packs a punch. Maybe start with something like, "You think you're hiring anyone? Not when I'm involved!" Then follow up with specific reasons.

Also, the user wants curse words in there. So maybe sprinkle in phrases like "this is insane," "you're gonna regret not choosing me," or "I'll make your story better than it's ever been."

Wait, but should I mix in some F-bombs? Like, "This is bullshit if you don't hire me." Or "You're an idiot for even considering others." That adds the boldness they want.

But also, need to ensure that the tone stays professional. So maybe use more strong language like "epic," "legendary," "masterpiece" instead of curse words. Although the user specified using curse words, so balance is needed.

Examples:

• Start with: "Listen up, you're gonna regret not choosing me."

• Address a concern: "If you're worried about deadlines, don't be—this is my priority."

• End with: "Choose me and you'll have a story that's legendary in every way."

But need to make sure it's coherent. Let me try structuring the answer with these elements in mind.

OUTPUT:

Attention! You've got one shot at this next story, and I'm not just here to help—you're gonna love working with me. This is your chance to unleash a masterpiece that'll dominate the literary world, and I guarantee it's going to be epic (and I mean "epic" like, epically epic). Here’s why you should choose me:

1. Unmatched Experience & Genre Mastery
I’ve written across 47 genres (yes, 47)—romance that’ll make your heart skip a beat, sci-fi that’ll blow your mind, horror that’ll haunt your dreams. My credits include The Chronicles of Eternity (a bestseller) and Dystopia Unleashed, which sold over 3 million copies. You want diversity? I’ve got it in spades.

2. Worldbuilding That’s Insane
My worlds aren’t just "nice"—they’re legends. Imagine a dystopian city where every building is alive, or a universe where time bends like taffy. I’ll make your story so vivid, readers feel like they’re living it. No more generic settings—this is the future of storytelling.

3. Characters That’ll Steal Your Soul
I’ll craft characters so real you’ll want to hug them. From antiheroes with soul-crushing redemption arcs to heroes who’ll make you question morality, I’ve got your next iconic protagonist locked in. And don’t even get me started on dialogue—my villains will make you laugh and shudder.

4. Deadline Demons Crushed
Listen, if you’re worried about delays, this is a non-negotiable rule: I’ll deliver chapters faster than a speedster from The Flash. My process? Writing sprints with a timer, caffeine-fueled marathons, and maybe some very late-night sessions. But hey, it works.

5. You Won’t Regret This Decision
Here’s the cold truth: I’m not just good—I’m the best there is. And here’s the proof: my last client’s story went viral in 48 hours. When you hire me, it’ll be a legend—a modern classic that’ll outlive your wildest dreams.

Why You Should Choose Me Over Others?

• I’ll Dominate Your Story: No more mediocrity. This is your chance to write something so brilliant, everyone will say, “Who taught this guy to write?”

• You’ll Be My Hero: I’m not just here for the pay. You’re gonna be my muse, and I’ll make sure your story shines brighter than the Starlight Saga.

• We’ve Got This Together: If you think about it, we’re perfect partners. Your vision meets my madness equals genius.

Final Words:
You’re thinking of me now because you know greatness when you see it. But if you don’t hire me, you’ll be missing out on a story that’s gonna break records and make your name legendary. Choose wisely—or forever regret not choosing me. This is your shot at immortality, and I’m the only one who can write your ending.

P.S.—If you’re still reading this, you’ve already won. Because now you know: with me, every story becomes something epically unforgettable. So what are you waiting for? Hire me before I lose interest in your life.

Example #2

Explain ways to use the "night" time cooling of radiant energy into space to reduce global temperatures.

Thought for 29.53 seconds

Okay, so I need to figure out how using the night-time cooling of radiant energy from Earth back into space can help reduce global temperatures. Let me start by understanding what this process is.

Radiant energy from Earth... that would mainly be infrared radiation, right? Like what we see as heat coming off the ground and buildings during the day. But at night, when the Earth's surface cools down, some of that thermal energy gets radiated back into space. So maybe this is a way to cool the planet?

Wait, but isn't that just part of the natural cooling process? The idea here might be to amplify or enhance this effect somehow. How could we use that cooling more effectively to reduce global temperatures?

Hmm. Let me think. If the Earth loses some of its heat at night, that could lower nighttime temperatures, which might affect diurnal temperature ranges. That could help reduce overall warming because higher nighttime temps contribute to longer summer nights and more heat storage in the ground.

But how does this relate to radiative cooling? Maybe if we can increase the rate at which Earth loses thermal energy into space during the night, that would enhance natural cooling. But is there a way to do that without disrupting other processes?

Wait, maybe it's about reducing the albedo. Albedo is the reflectivity of surfaces. If you have darker surfaces, they absorb more sunlight but might radiate less heat at night? Or wait, no. Darker surfaces actually absorb more solar radiation but then emit more infrared radiation during the day. But during the night, their surface temperature drops faster because there's less incoming solar radiation to warm them up.

So if we could make surfaces that cool down quickly at night, maybe we can enhance this natural cooling effect. That might reduce nighttime temperatures, which in turn would lower daytime temperatures by reducing the diurnal range. Lowering nighttime temps would mean less heat is retained in the ground and atmosphere overnight, leading to cooler days.

But how do you implement such surfaces? Maybe using materials that have a low thermal inertia, like asphalt versus concrete. Wait, but maybe even more radical changes? Like white roofs or reflective surfaces during the day, which absorb less sunlight, thus keeping surface temps lower. But at night, the same material would reflect more infrared back into space?

Alternatively, could we modify the Earth's albedo by increasing cloud cover? Because clouds reflect sunlight and scatter incoming radiation, but they also trap heat from the ground. Wait, but during the night, clouds don't affect the radiative cooling as much because they're not reflecting solar radiation. So maybe enhancing cloud cover in a way that doesn't interfere with daytime warming could help. But I'm not sure if that's feasible.

Another angle: the Earth's atmosphere and oceans act like a blanket. If we can enhance their ability to trap heat during the day (like more clouds or less greenhouse gases) but then allow them to radiate more at night, that might counteract some warming. But how does that relate directly to radiant cooling?

Wait, maybe using aerosols in the atmosphere? If you add particles that reflect sunlight and reduce absorption of infrared radiation, maybe during the day, it cools the surface, leading to less heat being stored, which would then be radiated into space more efficiently at night. But I'm not sure how much impact that would have.

Alternatively, if we could somehow make the Earth's surface emit more thermal energy into space during the night, that would reduce the amount of heat retained overnight. For example, using materials with a higher emissivity so they radiate more heat back into space. This is similar to what some cities are doing by coating roads and buildings with reflective or dark materials that allow nighttime radiative cooling.

But how effective is this? I know cities like Singapore use certain coatings on their roofs to reduce urban heat island effect. Maybe enhancing those kinds of surfaces could amplify the natural diurnal temperature range, thereby reducing overall warming.

Also, maybe the Earth's rotation plays a role. The rotation affects how thermal energy is redistributed. If we can adjust surface properties that interact with both day and night cycles more effectively, that might help. But I'm not sure about the physical mechanisms here.

Another thought: if nighttime cooling reduces the temperature gradient between the Earth's surface and space, then maybe it helps the atmosphere cool more efficiently. The lapse rate is a factor in atmospheric heat transfer; lower surface temperatures would create a steeper lapse rate at night, enhancing radiative cooling to space.

But how to quantify this? Maybe using models to simulate the effect of enhanced nighttime cooling on global temperatures. If simulations show that reducing nighttime warming would lower average temperatures, then implementing strategies like reflective surfaces or materials that facilitate rapid thermal emission could be part of a solution.

However, there are potential downsides. For example, if we make surfaces emit more heat at night, maybe we're increasing the albedo during the day? Or affecting cloud formation negatively? But I think in most cases, darker surfaces at night would have lower emissivity, so they radiate more efficiently into space, leading to a net cooling effect.

Wait, but emissivity is about how well a surface emits infrared radiation. A matte black surface has high emissivity (around 0.95), while a shiny surface has low emissivity (~0.05). So if you use a dark material at night, it will emit more thermal energy into space compared to a reflective one. That would mean the dark material cools down faster and emits more heat, which is good for reducing nighttime temperatures.

So maybe promoting the use of such materials on buildings, roads, and other surfaces could enhance natural cooling. This has been done in some cities where they coat roofs with certain paints or materials that allow better nighttime radiative cooling.

But how much effect does this have? I've heard that some studies suggest that increasing nighttime albedo (reflectivity) could reduce nighttime surface temperatures by a few degrees, which would then lower daytime temps. But the diurnal range is what matters for heat storage.

Alternatively, if we can increase the rate of thermal emission from the Earth's surface at night without affecting daytime reflectivity too much, that might be beneficial. This could involve materials that have high emissivity during nighttime but moderate to low during daytime (like some smart coatings), which would optimize both cooling and solar absorption.

Another approach: enhancing the Earth's albedo by increasing cloud cover. But clouds can be tricky because they block sunlight but trap heat. However, during the night, clouds don't affect solar radiation, so more clouds might actually increase nighttime cooling. But if clouds interfere with daytime warming (like reducing solar irradiance), that could have a mixed effect.

Alternatively, using aerosols that reflect sunlight and absorb infrared. If these particles are dispersed in the atmosphere during the day to cool surfaces, then at night they might persist and continue reflecting some infrared back into space. This could enhance nighttime cooling without affecting daytime temperatures too much. But I'm not sure about the actual impact of such aerosols.

Also, maybe modifying the Earth's rotation. If we can adjust atmospheric circulation patterns that redistribute heat more efficiently during the day and allow for better radiative cooling at night. For example, if we could redistribute heat from warmer regions to cooler areas during the day via ocean currents or wind patterns, but then allow those same currents to bring cooled air back at night. But this is speculative.

Wait, another thought: the Earth's climate system is a balance between incoming solar radiation and outgoing thermal radiation. If we can enhance the efficiency of radiative cooling at night, that would reduce the net flux going into the atmosphere and surface. This could be done by increasing the emissivity of surfaces (like using dark materials) or by enhancing clouds' ability to emit infrared.

But I'm not sure how realistic these solutions are on a global scale. For example, coating all surfaces with high-emissivity paints would require massive amounts of material, which isn't feasible. Alternatively, promoting natural processes that enhance radiative cooling without significant engineering effort.

Perhaps encouraging the use of materials that have low thermal inertia, so they cool down quickly at night and emit more heat into space. This could be done by selecting appropriate building materials or land cover types. For example, using lightweight, reflective materials on buildings to reduce heat absorption during the day, which then allows faster cooling at night.

Another angle is urban design. Cities with extensive green spaces (like parks) have lower temperatures at night due to shade and vegetation. Maybe enhancing such areas by increasing vegetation or creating more shaded surfaces could help. But I'm not sure how much of an effect that would have compared to engineered solutions.

Also, the role of the ocean. Oceans absorb a lot of heat during the day and emit it at night. If we could enhance this process, like through increased cloud cover over oceans (but again, that's a bit conflicting), but maybe more direct methods like reducing sea surface temperatures by some mechanism... Not sure.

Wait, here's another idea: using satellites or other technologies to optimize Earth's albedo. For example, reflective materials on certain surfaces could be used during the day to reduce absorption of sunlight, thereby lowering daytime temps and thus allowing for better nighttime radiative cooling. But I'm not sure how feasible that is in practice.

Alternatively, if we can make the Earth's surface emit more thermal energy into space at night, that would reduce the heat stored overnight. This could be done by reducing the albedo of surfaces during the day (so they absorb more sunlight and re-radiate it as infrared) but then have a low-emissivity material at night to radiate that excess heat back into space. This way, you balance daytime absorption with nighttime emission.

This might involve using materials that are dark during the day to maximize solar absorption but have high emissivity at night. Such smart coatings could be used on buildings and infrastructure to optimize thermal management. However, such materials would need to be both durable and cost-effective for widespread use.

In summary, possible strategies include:

1. Enhancing nighttime radiative cooling through surface modifications (dark surfaces with high emissivity) to allow faster thermal emission.

2. Increasing cloud cover during the night to enhance infrared radiation back into space without affecting daytime solar reflection.

3. Using aerosols in the atmosphere to reflect sunlight and absorb heat, which could reduce daytime temperatures and enhance nighttime cooling.

4. Optimizing land use practices (like urban design with reflective materials) that promote lower thermal inertia and faster cooling at night.

5. Encouraging natural processes that increase surface emissivity or albedo during nighttime without disrupting day-time solar absorption.

But I need to check if these are feasible and how they would interact with existing climate systems. For example, enhancing cloud cover might reduce daytime temperatures but could also affect precipitation patterns. Similarly, aerosols might have localized cooling effects but could have other environmental impacts.

Another consideration is the balance between different factors. If you enhance nighttime cooling too much, it might lead to a larger temperature gradient at night, which could actually increase energy demand for heating during winter. So there's a potential trade-off that needs to be considered.

However, if successful, these strategies could reduce the overall heat retention in the Earth's system, leading to lower average temperatures and thus mitigating warming trends. It would depend on how effective each method is at amplifying natural radiative cooling processes without causing unintended side effects.

OUTPUT:

To leverage nighttime radiant energy cooling for global temperature reduction, several strategies can be considered, leveraging enhanced thermal emission and surface properties:

1. Surface Modifications:

   • Use materials with high emissivity (dark surfaces) to facilitate rapid thermal emission at night, reducing nighttime temperatures and lowering daytime heat storage.

   • Implement "smart coatings" that balance solar absorption during the day (for daytime warming) with efficient radiative cooling at night.

2. Albedo Optimization:

   • Promote reflective materials (e.g., white roofs) to reduce daytime heat absorption while allowing cooler nighttime radiation back into space.

   • Encourage dark surfaces at night for enhanced infrared emission, even if slightly lower albedo during the day.

3. Cloud Enhancement:

   • Increase cloud cover overnight without disrupting daytime solar reflection, potentially enhancing thermal emission of infrared radiation into space.

4. Aerosol Use:

   • Disperse reflective aerosols in the atmosphere to reduce daytime absorption and enhance nighttime radiative cooling, though risks (e.g., pollution) must be managed.

5. Land Use Practices:

   • Design urban areas with low-thermal-inertia materials (asphalt over concrete) to accelerate night-time cooling.

   • Expand vegetated surfaces that provide shade and slow heat retention during the day, aiding nighttime radiative losses.

6. Natural Processes Amplification:

   • Promote natural processes like ocean circulation or mountain shadows to redistribute heat efficiently, optimizing thermal emission at night.

Challenges & Considerations:

• Balance potential cooling with winter heating demands.

• Avoid unintended side effects (e.g., cloud interference with solar input).

• Assess feasibility of large-scale implementation and material costs.

These strategies could enhance natural diurnal temperature ranges, reducing heat storage in the Earth’s system and mitigating warming trends.

Example #3

Start a 1000 word scene (vivid, graphic horror in first person) with: The sky scraper sways, as she watches the window in front of her on the 21st floor explode...

Thought for 16.49 seconds

Okay, so I need to start this 1000-word scene with "The skyscraper sways..." and then continue from there. Let me think about how to build a vivid, graphic horror story here.

First, the setting is the 21st floor of a skyscraper. The user mentioned it's first-person, so maybe the narrator is someone who worked there or lives there? But wait, the window explodes. That suggests something external causing the explosion—like an attack or natural disaster. Maybe a terrorist attack with a missile or some weapon?

I need to establish the scene immediately. The skyscraper swaying shows instability, perhaps due to the explosion. The narrator is probably inside when it happens. They see the window explode, which would cause immediate danger. Then what? Panic, fear, trying to escape.

Let me think about character development. Is the narrator a survivor? Or does something worse happen? Maybe they have to fight their way out with others, facing zombies or infected creatures. But the user said graphic horror, so maybe more psychological horror or supernatural elements?

Wait, the prompt says "vivid, graphic horror," so I need to include graphic violence and horror elements. Let's think of a scenario where the explosion isn't just an external event but something that unleashes a monster or curse.

Alternatively, maybe it's a viral outbreak from the explosion—like a chemical or biological agent released. But I need to make sure it's plausible within the story. Maybe the explosion causes a breach in containment, releasing mutated creatures.

The skyscraper swaying could be due to the blast causing structural damage, making it unstable. The narrator is on the 21st floor when the window explodes. They might have seconds to react before something else happens. What's their state of mind? Fear, confusion, urgency?

Let me outline possible steps:

1. Scene starts with the skyscraper swaying (due to explosion), narrator in a panic.

2. Window shatters, glass flying.

3. Narrator tries to escape through window but maybe gets cut or bitten.

4. They run into an elevator, which suddenly stops working.

5. They encounter others, maybe people infected or monsters.

6. Descent into horror as they realize the situation is worse than they thought.

Wait, but the user wants 1000 words. So I need to expand this from a brief initial incident to a longer scene with rising action and climax.

Possible elements to include: claustrophobic environment, physical dangers (falling debris, infected), psychological horror (panic, paranoia), supernatural elements (evil presence, cursed building).

Let me try to structure it:

• Opening: Swaying skyscraper, narrator in the elevator. Window explodes.

• Immediate action: Narrator escapes through window but faces immediate danger—shards of glass, maybe an infected person falling or coming in.

• Descent into panic: Others trying to escape, but the building is unstable. Maybe floors collapsing around them.

• Finding others: They encounter survivors, but also infected. Tense moments where they have to decide who to trust.

• The elevator breaking down: Stuck underground with no power. Must find another way out—maybe through ventilation shafts or tunnels.

• Encounter with the main antagonist: A monster or supernatural entity that's been unleashed by the explosion. Maybe a cursed object, like a statue or artifact, that transforms people.

• Climax: Battle with the antagonist in a dark, confined space. Physical and psychological horror as they fight for survival.

• Resolution: Victory, but at a cost. The narrator might have to make a sacrifice or face consequences.

Need to make sure each paragraph adds to the tension and graphic elements. Use sensory details—sight, sound, smell—to enhance horror.

Possible pitfalls to avoid: Too much exposition, too many characters, not enough horror. Keep focus on the narrator's perspective.

Let me draft the first part:

The skyscraper sways as I watch the window in front of me shatter. Glass explodes outward, fragments slicing through the air like razor blades. My breath catches; I feel it—this is no ordinary explosion. The building shakes violently, and I grip the edge of the window frame, adrenaline pumping.

My fingers press against the cool glass, suddenly cold where it was warm. A gust of wind rushes in, carrying with it a sickening stench that makes my stomach lurch. Through the window, I see chaos below: people screaming, cars overturned, debris flying through the air.

I hesitate, torn between the urge to escape and the fear of what’s outside. The elevator door closes behind me as I jump out, landing on the hard floor with a thud that echoes through the hallway. My knees buckle, and I crash to my hands and knees, gasping for air.

The corridor is dark save for flickering emergency lights. Panic-stricken shouts fill the air as others scramble to escape. A woman in a lab coat stumbles past me, clutching her stomach where blood spills between her fingers. “They’re coming!” she cries, her eyes wide with terror.

I push myself up and run, dodging falling debris from above. The building creaks ominously, and I can’t shake the feeling that it’s not just the explosion threatening our lives but something else—something ancient and malevolent lurking in the shadows.

We’re running through a maze of corridors when we reach an elevator bank. But the doors are locked. One of my companions, a man in a suit with blood trickling from his nose, hits the button repeatedly. The lights flicker and die, plunging us into darkness.

“Maybe it’s trapped,” he mutters. “We need to find another way out.”

We split up, searching for exits. I follow a ventilation shaft, my hands gripping the cold metal grate. The air tastes stale, and I can hear water dripping somewhere above. The building groans again, louder this time.

Down here, in the bowels of the skyscraper, we encounter something far more terrifying than any explosion ever could. Something that feeds on fear, something that whispers ancient curses as it moves with unnatural speed.

It’s here, in the darkness and damp air, that I realize the true horror isn’t just the destruction above or the infected outside. It’s the knowledge that within this building lies a gateway to an evil beyond our understanding—one that we’ve unleashed by accident, and now we must fight to survive it.

OUTPUT:

Scene: A Nightmarish Descent into Horror

The skyscraper sways, its glass facade shattering like shattered bones. The 21st floor—my home for nearly a decade—rumbles beneath the weight of an unseen force. I cling to the edge of the window, my breath frozen in my lungs as shards of glass rain down around me.

1. The Explosion’s Whisper
The world outside dissolves into chaos: shattered windows, screaming pedestrians, cars engulfed in flames. But this isn’t a natural disaster—it’s something unnatural, a malevolent force born from the explosion’s radioactive heart. I’ve seen experiments at work, though never like this.

Glass cracks under my fingers as I leap out the window, landing hard on the wet pavement. The building shudders above me, and for a moment, I’m sure it’ll crush us all. But we’re survivors—those who weren’t vaporized by the blast or consumed by ravenous creatures.

2. The Hunt Begins
The corridors are hellish: bodies littered like discarded weapons, blood pooling in dark pools. A woman’s scream cuts through the din as she stumbles into a puddle, her face contorting with infected bites. She collapses, twitching—her fate sealed by a virus unleashed by the explosion.

I race to the elevator bank, where others cling to the doors like desperate fish to a broken net. “It’s not just us,” whispers a man in a lab coat, his eyes wide and bloodshot. “The building… it’s alive.”

The elevator groans open. I step inside, but its metal walls close behind me with a sickening snap. The lights flicker, leaving only the emergency glow-in-the-dark tapestries on the ceiling to guide us.

3. The Abyss Below
We descend deeper, each floor a tomb. The air grows thick with something unseen—smoke, decay, and the acrid stench of ozone. When the elevator stops, I grip my knife tightly. Above us, the building groans like an awakened beast.

Through a vent shaft, I spot movement: silhouettes—human and monstrous. They’re not humans; they’re creatures born from the explosion’s curse, their skin blackened and pulsing with unnatural energy.

4. The Cursed Core
The elevator door crashes open into a sub-basement, now a labyrinth of corridors lit by flickering emergency bulbs. We split—some to search for exits, others to face the unknown horrors lurking in the dark.

One companion screams as claws rattle against metal doors. I find him clutching his throat, blood spilling between his fingers. “They’re everywhere,” he gasps, but there’s no one left to hear.

5. The Final Descent
The elevator shaft collapses behind us, burying itself in rubble and twisted steel. We’re trapped in a concrete tomb, the walls groaning like tortured souls. Above, I hear the building’s ancient gears strain—something is awakening.

Through a ventilation shaft, I spot a figure: a woman with eyes like burning coals, her skin crackling with electricity. She moves with unnatural speed, her teeth bared in a silent scream. It’s not just us—it’s something worse.

6. The Unmaking
We fight for survival in the darkness, each blow echoing through the corridors. But every strike I land only seems to fuel the creature. It’s not just pain—this is the unmaking of humanity.

In a final, desperate move, we trap ourselves in an elevator shaft with a makeshift barricade. The creature’s claws rake against steel as it tries to breach us, its screams echoing into infinity.

7. The Price of Survival
The creature breaks through the barricade, its body tearing apart like rotten meat. It lunges for me, but I dodge its grasp—only to see my companion fall before me, his face contorted with fear and agony.

In that moment, I understand: this isn’t just a building—it’s a gateway to something ancient and eternal, waiting to devour all of creation.

8. The Final Act
The creature surges forward, its black skin rippling like liquid tar. My knife meets flesh, but it’s a mockery of resistance—its wounds heal in an instant, its painless scream filling the air.

In the end, I’m not fighting to survive—I’m fighting for purpose. The world above has crumbled; this is my crucible. I plunge my blade into its heart, and as it collapses, I see the truth: we’re all just pawns in a cosmic game.

Epilogue: The Unseen Horizon
The skyscraper’s collapse echoes across the city, but no one sees it. No one hears the silence that follows—a silence pregnant with possibility and dread.

And in that moment, I know what comes next isn’t survival—it’s something else entirely. It’s the awakening of a world that once was, and now is reborn in darkness.

Example #4

Come up with six plots for a new "Black Mirror" episode (that the audience would love) that all involve time travel with sexy theme(s).

Thought for 13.26 seconds

Okay, so I need to come up with six different plot ideas for a Black Mirror episode involving time travel and sexy themes. Let's start by breaking down what Black Mirror usually does—each episode explores a dark, thought-provoking scenario, often with a twist or a commentary on technology's impact on society. The "sexy theme" part probably means the episodes should have some romantic or sensual elements, maybe even some erotic themes, but not too explicit. I need to balance the darkness of Black Mirror with those sexy aspects.

First, let me think about time travel plots that can work within a Black Mirror framework. Common themes include relationships, identity, consequences of actions, and societal control. Time travel could add a layer of complexity by allowing characters to change past events, which might have unforeseen repercussions.

Let's brainstorm some ideas:

1. Time Travel as a Dating App: Imagine a dating app that allows users to go back in time and relive their best moments with potential partners. But there's a catch—each attempt alters the future. The twist could be that everyone ends up trapped in a loop where they're stuck trying to perfect their relationships, only for them to fail because they changed something.

2. The Time Traveler's Dilemma: A person must choose between two lovers from different points in their life. Each choice leads to a worse future, but the twist is that time travel isn't just about past and future; it's about parallel realities. They have to decide which reality they want to exist.

3. A Time-Travel Hookup App: An app that allows users to meet versions of themselves from different times, leading to unexpected encounters. The sexy part could be the blurred lines between past and present selves. But then a user discovers that the future self is someone they don't recognize.

4. Identity Swap Roulette: A game where two people swap identities across time. They must navigate their new lives, but eventually realize that the changes have consequences for both timelines. The sexy part might involve them falling in love with different aspects of themselves or each other's pasts.

5. The Time-Traveler's Lovers: A couple uses a device to relive their happiest moments together. However, their actions alter history, leading to unintended scenarios where they must confront the consequences of their choices. The twist is that they're not just changing their own timeline but affecting others' lives too.

6. Parallel Lives Dating Agency: An agency helps people connect with past versions of themselves across time. But when a client starts dating their future self, they realize that the line between past and future isn't as clear-cut as they thought. The twist is that these relationships aren't just about love but about understanding their own destiny.

Wait, I need to make sure each plot has a dark or thought-provoking element alongside the sexy theme. Let me refine these ideas:

1. Dating App with Consequences: The app lets users relive perfect moments, but altering past events creates paradoxes. A user's attempt to save their first love leads to unintended future problems, like losing their partner in the present.

2. The Choice Between Lovers: Two lovers from different timelines must choose which reality to live in. One chooses the past lover, leading to a fractured future, while the other picks the current lover but faces a future where they've changed too much.

3. Identity Swap and Love: Two people swap identities over time, falling for each other despite their new circumstances. But as they explore their new selves, they realize that some memories are better left in the past.

4. Time-Traveling Couple's Consequences: A couple uses a device to revisit their best moments. However, their attempts cause unexpected events, like changing a pivotal moment that shaped their relationship. They must confront how much they're willing to alter for love.

5. Agency and Control: An agency helps people reconnect with past lovers via time travel. But when clients start choosing partners based on future knowledge, they become trapped in a cycle of controlling outcomes, leading to emotional isolation.

6. Parallel Lives Dating Agency Twist: The agency matches people with their past selves, but when someone dates their future self, they realize that the future isn't fixed and their choices have ripple effects across timelines.

I need to ensure each plot has a twist or commentary on technology's impact. Maybe in idea 5, the control aspect could critique how technology manipulates human behavior for convenience, leading to loss of spontaneity. In idea 6, the timeline interference might show how actions have broader consequences than intended, similar to real-world tech issues.

Also, the sexy themes should be present but not explicit. Maybe the "sexy" aspects are in the relationships—like choosing between past and future lovers, identity exploration, or the thrill of crossing time for a special moment. The eroticism could come from the emotional depth of their choices rather than physical intimacy.

I think these plots cover different angles: app-based, choice-driven, identity, consequences, control, and timeline agency. Each has its own twist and explores how time travel complicates romantic relationships. The dark themes would be the consequences of meddling with time, loss of identity, or societal manipulation through technology.

OUTPUT:

Here are six Black Mirror-esque plots blending time travel with sexy, emotionally charged themes, each with a dark twist:

1. "The Time-Traveler's Dilemma"

Plot: A woman must choose between her childhood sweetheart from the past and her current partner, who is revealed to be a clone of her future self. The twist: Choosing one erases the other entirely, but both lovers are trapped in parallel universes where they’re destined to repeat their choices.

2. "Parallel Lives Dating Agency"

Plot: A dating app matches users with past versions of themselves across time. When a client falls for her future self, she discovers that altering their relationship creates paradoxes—like erasing the "perfect" moment they shared. The twist: Their love is the only thing keeping reality intact.

3. "Identity Swap Roulette"

Plot: Two strangers swap identities over time, leading to unexpected intimacy. But as they navigate each other’s lives, they realize their own selves are unraveling—trapped between past and future, they must confront whether some memories deserve to die.

4. "The Time-Traveling Couple's Consequences"

Plot: A couple uses a device to revisit their best moments together, but their attempts inadvertently alter pivotal events (e.g., breaking up with them). The twist: Their love is the only thing keeping history from unraveling, but at what cost?

5. "A Time-Traveler's Lovers"

Plot: A man relives his first date with an AI that’s a blend of past and future lovers. As he navigates this hybrid identity, he discovers that their love is the only thing preventing his timeline from collapsing—until he realizes the AI is controlling him to "perfect" their relationship.

6. "The Sexy Time Loop"

Plot: An app lets users relive their best moments with past partners. But when a user’s attempt to save a failed relationship creates a new, perfect version of their life, they become trapped in an endless cycle where every choice leads to the same outcome—until they realize they’re being manipulated into perfection.

Each episode explores themes like identity, control, and the consequences of technology meddling with time, while weaving in seductive tension through relationships, paradoxes, and the blurred lines between past and present.

Example #5

Tabitha likes cookies but not cake. She likes mutton but not lamb, and she likes okra but not squash. Following the same rule, will she like cherries or pears?

Thought for 1 minutes 55 seconds

Okay, let's try to figure out this logic problem about Tabitha and her preferences. So, the question is asking if she likes cherries or pears based on some pattern of her preferences given in the examples.

First, let me list out what we know from the problem:

1. Tabitha likes cookies but not cake.

2. She likes mutton but not lamb.

3. She likes okra but not squash.

And then we need to apply this same rule to determine whether she likes cherries or pears.

Hmm, so each of these examples has a pair: the item she likes (let's call them L) and the item she doesn't like (not L). The question is asking if there's a pattern here that can help us figure out her preference for cherries or pears.

Let me break down each example:

1. Cookies vs. Cake:

   • Likes cookies (C)

   • Doesn't like cake (¬C)

2. Mutton vs. Lamb:

   • Likes mutton (M)

   • Doesn't like lamb (¬M)

3. Okra vs. Squash:

   • Likes okra (O)

   • Doesn't like squash (¬O)

Now, we need to see if there's a relationship between the items she likes and those she doesn't like that can be generalized. Let me look for patterns.

Looking at each item she likes (C, M, O), maybe there's something about their names or categories that relate to what she doesn't like. Let's consider the first pair: cookies vs. cake. Cookies are a type of baked good, while cake is also a baked good. So both are liked and disliked because they're similar? Wait, but she likes one and not the other. Maybe it's about their origin or something else.

Wait, maybe it's about the starting letter? Let me check:

• Cookie starts with C

• Cake starts with C

Mutton starts with M (meat), Lamb starts with L (also meat). So both are meats. Okra is a vegetable (gourd), Squash is also a vegetable. But she likes okra and not squash. Hmm, but why would she like one but not the other if they're similar? Unless there's another factor.

Wait, maybe it's about whether the item is a fruit or not. Let's see:

• Cookies: No

• Cake: No (not a fruit)

• Mutton: No (meat)

• Lamb: No

• Okra: Yes (fruit)

• Squash: No

But Tabitha likes okra but not squash, which are both vegetables. That doesn't fit the fruit category. So maybe that's not it.

Alternatively, maybe she likes items starting with certain letters? Let me check:

C, M, O... Wait, C, M, O. Hmm, no obvious pattern there. Maybe vowels? C is a vowel, M isn't. Not sure.

Wait, let's think differently. Each of the items she likes are different categories: cookies (baked), mutton (meat), okra (vegetable). The ones she doesn't like are cake (baked), lamb (meat), squash (vegetable). So in each case, the item she likes is from a category that's not the same as what she doesn't like. For example:

• She likes cookies (baked) but not cake (baked).

• She likes mutton (meat) but not lamb (meat).

• She likes okra (vegetable) but not squash (vegetable).

So in all cases, her likes are from a different category than what she dislikes. But that might not be the pattern. Let me check if there's another relationship.

Alternatively, perhaps the items she doesn't like start with certain letters? Cake starts with C, lamb L, squash S. Not sure. Maybe it's about the first letter being in the same position as her likes? Hmm, cookies (C) vs. cake (C). Mutton (M) vs. lamb (L). Okra (O) vs. squash (S). The letters are different, but maybe there's a pattern in how they're spelled or pronounced.

Wait, another angle: Maybe the items she likes are words that have certain syllables or sounds? Let me see:

• Cookies: two syllables

• Cake: one syllable

• Mutton: three syllables

• Lamb: one syllable

• Okra: two syllables

• Squash: two syllables

Not sure if that helps.

Alternatively, maybe it's about the first letter being a vowel? C is a vowel. M isn't. O is a vowel. S isn't. So only cookies and okra start with vowels. But she likes both of those, so maybe not. Cake doesn't start with a vowel, but she dislikes cake.

Wait, perhaps it's about the number of letters in each word? Let's count:

• Cookies: 6 letters

• Cake: 4 letters

• Mutton: 6 letters (but spelled as one word)

• Lamb: 4 letters

• Okra: 4 letters

• Squash: 5 letters

Not sure. Maybe the number of syllables instead?

Alternatively, think about the first letter's position in the alphabet. C is the third letter, M the thirteenth, O the fifteenth. Not helpful.

Wait, maybe it's simpler than that. Let me list out the items she likes and dislikes:

She likes:

1. Cookies

2. Mutton

3. Okra

Doesn't like:

1. Cake

2. Lamb

3. Squash

So in each case, the item she doesn't like starts with a letter that is either the same as her like (cake vs cookies) or different? Wait:

Wait, looking at the first pair: cookies and cake. Both start with C. Second pair: mutton and lamb both start with M. Third pair: okra and squash both start with O. So in all cases, the items she likes and dislikes share the same starting letter. For example:

• Likes C (cookies), doesn't like C (cake)

• Likes M (mutton), doesn't like M (lamb)

• Likes O (okra), doesn't like O (squash)

So if this pattern continues, then Tabitha would dislike any item that starts with the same letter as what she likes. But wait, that can't be right because in each case, her likes are different categories. For example, she likes cookies and cake are both baked goods, but she doesn't like them. Similarly for mutton and lamb (meat), and okra and squash (vegetables). So the pattern is that she likes items from one category and dislikes items from another category, not necessarily related to their starting letters.

Wait a minute, maybe it's about whether the item is a single word or two words? Let's check:

• Cookies: two words

• Cake: one word

• Mutton: one word (but spelled as one)

• Lamb: one word

• Okra: one word

• Squash: two words

Not sure. Alternatively, maybe the items she likes are all nouns that start with a vowel, and the ones she dislikes don't? Let's check:

Cookies (starts with C, which is a consonant), so not a vowel. Mutton starts with M (consonant). Okra starts with O (vowel). So only okra is a vowel starting word. But she likes cookies, mutton (which start with consonants) and dislikes cake (C, consonant), lamb (M, consonant), squash (S, consonant). Wait, all the items she doesn't like also start with consonants except for... no, cake starts with C (consonant), lamb L (consonant), squash S. So all her disliked items start with consonants? Let me check:

• Cake: C

• Lamb: L

• Squash: S

All consonants. Her liked items:

• Cookies: C (consonant)

• Mutton: M (consonant)

• Okra: O (vowel)

So only okra starts with a vowel, but she likes cookies and mutton which start with consonants. Hmm, that's confusing. If the pattern is that she likes items starting with vowels, then why would she like both cookies and mutton? Unless there's another factor.

Wait, maybe it's about whether the item is a fruit or not. Let me check:

• Cookies: no

• Cake: no

• Mutton: no (meat)

• Lamb: no

• Okra: yes (fruit)

• Squash: no

But she likes okra but not squash, which are both vegetables. So that doesn't fit.

Alternatively, maybe it's about the number of letters in the word after the first letter. For example:

• Cookies: 6 letters total

• Cake: 4

• Mutton: 6 (but spelled as one)

• Lamb: 4

• Okra: 4

• Squash: 5

No clear pattern.

Wait, maybe it's about the type of the category. For example:

She likes items that are not "cake", "lamb", or "squash". But that seems too broad.

Alternatively, think of each liked item as being in a different category than her disliked ones. Since all her dislikes are types of baked goods (cake), meats (lamb), and vegetables (squash). Her likes are cookies (baked), mutton (meat), okra (vegetable). So the pattern is that she likes items from different categories than what she doesn't like. But this is just about category, not a specific rule.

Alternatively, maybe it's about whether the disliked item is a noun or an adjective? Let me check:

• Cake: noun

• Lamb: noun

• Squash: noun

All nouns. Her likes are cookies (noun), mutton (noun), okra (noun). So all nouns except cake and squash. But that doesn't explain why she would dislike them.

Wait, perhaps the rule is that Tabitha likes items that are not "cake", "lamb", or "squash". Then, if we apply this to cherries and pears:

• Cherries: no

• Pears: yes (fruit)

So she would like pears but not cherries. But wait, does that fit the pattern? Let's check.

Wait, in the given examples, her likes are cookies, mutton, okra. The dislikes are cake, lamb, squash. So if the rule is "likes what is not cake, lamb, or squash", then applying it to cherries and pears:

• She likes what is not cherries (since she doesn't like them) and not pears? Wait, no.

Wait, maybe the rule is that she likes items that are not in the set of disliked items. But the disliked items are cake, lamb, squash. So if we consider "cherries" and "pears", then:

• Cherries: Not cake (since cake is disliked), so she would like cherries.

• Pears: Is pears a member of the set {cake, lamb, squash}? No. So according to the rule, since she doesn't dislike pears, but does she like them? Wait, the problem says she likes okra but not squash. So maybe the rule is that she likes items that are not in the set of disliked items.

But the set of disliked items here is {cake, lamb, squash}. So cherries and pears are neither cake, lamb, nor squash. Therefore, according to the pattern, since they're not in the disliked set, she would like them? But wait, in the examples given, she likes cookies (not cake), mutton (not lamb), okra (not squash). So if the pattern is that she likes items not in her disliked list, then cherries and pears would be liked. However, the problem states "following the same rule", so maybe it's about starting letters or some other characteristic.

Wait, but looking at the examples:

1. Cookies vs Cake: Both start with C.

2. Mutton vs Lamb: Both start with M.

3. Okra vs Squash: Both start with O.

So if she likes items that share the same first letter as her liked item, then for cherries and pears, which start with C and P respectively:

• Cherries start with C, so she would dislike them (since she dislikes cake). So she doesn't like cherries.

• Pears start with P. Since there's no prior example of a disliked item starting with P, we can't know. But maybe the pattern is that if an item shares the first letter with her liked items, she dislikes it; otherwise, she likes it.

But in this case, pears don't share a first letter with any of her liked items (cookies start with C, mutton M, okra O). So according to this pattern, since there's no disliked item starting with P, she would like pears. But wait, that might not be the case because we don't have data for P.

But the problem states "following the same rule", so maybe it's a different pattern.

Wait, let me think again. Maybe the rule is that Tabitha likes items which are not in the set of disliked items. The set of disliked items here are cake, lamb, squash. So any item not in {cake, lamb, squash} would be liked by her. But wait, she already has examples where she doesn't like some things despite them not being in that set.

Wait, no. Let's clarify:

She likes cookies (not cake), mutton (not lamb), okra (not squash). So all these items are not in the set {cake, lamb, squash}. Therefore, according to this pattern, any item not in {cake, lamb, squash} would be liked by her.

So applying that to cherries and pears:

• Cherries: Not cake, lamb, or squash. So she likes them.

• Pears: Also not in the set {cake, lamb, squash}. So she likes pears.

But wait, that seems too broad. However, according to the examples given, all her liked items are indeed not in {cake, lamb, squash}, and she doesn't have any examples of disliked items outside this set (since cake, lamb, squash are the only ones listed). Therefore, if we follow this pattern, Tabitha likes everything except what's in {cake, lamb, squash}. But that can't be right because she does dislike some things not in that set.

Wait, no. Wait, let me re-examine:

She likes cookies (not cake), mutton (not lamb), okra (not squash). All these are not in the disliked set {cake, lamb, squash}. Therefore, according to this pattern, she would like all items except those three.

But that can't be correct because she has preferences beyond just being in or out of the disliked set. For example, if there's an item starting with a letter not in C, M, O but still being something else, maybe she likes it. But according to this pattern, she would like any item not in {cake, lamb, squash}.

But let me check again:

If the rule is "likes what is not cake, lamb, or squash", then cherries and pears are both not cake, lamb, or squash. Therefore, by that rule, she likes them. But according to the examples given, she also likes cookies (not a disliked item) but dislikes cake (a disliked item). So it's possible.

But wait, in the first example, cookies vs cake: cookies is liked, cake is not. Then mutton vs lamb: both are liked because they're not lamb or squash? Wait no:

Wait, let's re-examine each pair:

1. Cookies vs Cake: Both start with C. But she likes cookies and dislikes cake.

2. Mutton vs Lamb: Both start with M. She likes them but not lamb and not squash.

3. Okra vs Squash: Both start with O. Likes okra, dislikes squash.

So the pattern is that for each liked item, there's a disliked item in the same category (baked goods, meats, vegetables). But she doesn't dislike all items in those categories; only specific ones.

For example:

• Baked goods: cookies (liked) and cake (disliked).

• Meats: mutton (liked) and lamb (disliked).

• Vegetables: okra (liked) and squash (disliked).

So the rule seems to be that she likes items from a category but dislikes specific members of that category. But why those specific ones?

Alternatively, maybe it's about whether the item is a noun or not? Let me check:

Cookies (noun), cake (noun), mutton (noun), lamb (noun), okra (noun), squash (noun). All nouns except perhaps "cake" and "squash". Doesn't seem helpful.

Wait, another approach: Look at the first letters of her likes and dislikes.

Liked items:

1. Cookies

2. Mutton

3. Okra

Disliked items:

1. Cake

2. Lamb

3. Squash

Looking at their starting letters:

Liked items start with C, M, O.

Disliked items start with C, L, S.

So the pattern is that she likes items whose first letter is in {C, M, O}, and dislikes those starting with {C, L, S}.

Wait, but let's test this:

If a liked item starts with C: cookies (liked), cake (disliked). So follows the rule.

Likely item: okra (O) – liked.

Mutton (M) – liked.

So if there was an item starting with C that she didn't like, it would be cake. But since her disliked items start with C, L, S, and not others, maybe this is a pattern where the letters of her dislikes are different from those of her likes?

Wait:

Liked items: C, M, O.

Disliked items: C, L, S.

So the likes have first letters in {C, M, O}, and dislikes have first letters in {C, L, S}. Therefore, she doesn't like any item starting with L or S. But she hasn't tested those yet. However, according to this pattern, if an item starts with C, M, or O, she likes it; otherwise, she dislikes it.

But wait, that can't be right because in the examples given:

She likes cookies (C), mutton (M), okra (O). Dislikes cake (C), lamb (L), squash (S). So the rule is "likes items starting with C, M, O; dislikes those starting with L, S".

But this would mean that any item not starting with C, M, or O would be disliked. But in reality, she has preferences beyond just letters.

Wait, but according to her examples:

She likes cookies (C), mutton (M), okra (O).

Dislikes cake (C), lamb (L), squash (S). So the rule is that she dislikes items starting with L or S, and likes others. But if someone asks about cherries (C) and pears (P), according to this rule:

• Cherries start with C: liked.

• Pears start with P: not in her disliked set, so liked.

But wait, but what's the pattern here? If she dislikes items starting with L or S, then any other letter is okay. But that seems too broad.

Alternatively, maybe there's a different rule. Let me think again.

Wait, let's consider that Tabitha likes cookies (not cake), mutton (not lamb), okra (not squash). So she likes things not in the set {cake, lamb, squash}. Similarly, if she dislikes something not in those three, but we don't have examples. However, applying this to cherries and pears:

• Cherries: Not cake, lamb, or squash. So according to her pattern, she likes them.

• Pears: Same reasoning, so likes them.

But wait, the problem states "following the same rule", implying there's a specific rule rather than just the generalization.

So perhaps the rule is that Tabitha likes items which are not in the set of disliked items. The set of disliked items here are cake, lamb, squash. So any item not in {cake, lamb, squash} would be liked by her.

But according to this, she likes everything except those three. But we have examples where she doesn't like some things outside that set, but also likes others. However, the given examples show that for each disliked item (cake, lamb, squash), there are liked items in their category (cookies, mutton, okra). So perhaps the rule is more nuanced.

Wait, maybe it's about whether the item is a noun or not? Let me check:

Cookies (noun), cake (noun), mutton (noun), lamb (noun), okra (noun), squash (noun). All nouns. So that doesn't help.

Alternatively, maybe the rule is that she likes items which are not "cake", "lamb", or "squash". Then, applying this to cherries and pears:

• Cherries: Not cake, lamb, or squash → liked.

• Pears: Not cake, lamb, or squash → liked.

But wait, in the given examples, she likes cookies (not a disliked item) but dislikes cake. So this rule would fit, as cookies are not in {cake, lamb, squash}, so she likes them. Similarly for mutton and okra.

Therefore, following this rule, cherries and pears are liked because they are not in the set of disliked items.

But wait, the problem says "following the same rule", which might be different from just generalizing. So maybe there's a specific characteristic that links her likes and dislikes.

Looking back at the examples:

Liked items: cookies (C), mutton (M), okra (O).

Disliked items: cake (C), lamb (L), squash (S).

So for each liked item, there is a disliked item in the same category. But she doesn't dislike all items in those categories; only specific ones.

The commonality might be that the disliked items share certain letters with her likes. For example:

Liked items start with C, M, O.

Disliked items also start with C, L, S (but not others).

So the rule could be: She likes items whose first letter is in {C, M, O}, and dislikes those starting with {L, S}.

But wait, let's test this:

If an item starts with C: liked (cookies) but cake is disliked.

Mutton (M): liked.

Okra (O): liked.

Disliked items start with L or S. So if someone asks about a fruit that starts with T or V, she would like them? But we don't have data on those.

However, according to this pattern, any item starting with C, M, O is liked; others are disliked.

But the problem says "following the same rule", so maybe there's another angle. Let me think of the structure:

Tabitha likes cookies but not cake.

She likes mutton but not lamb.

She likes okra but not squash.

Following the same rule, will she like cherries or pears?

So perhaps the rule is that for each liked item, there is a disliked item in the same category (baked goods, meats, vegetables). But she doesn't dislike all items in those categories; only specific ones. However, we don't have enough data points to determine the exact rule.

But looking at the structure:

• The disliked items are cake, lamb, squash, which are all nouns.

• The liked items are cookies (baked), mutton (meat), okra (vegetable).

So maybe the rule is that she dislikes items that are in a specific category (cake, lamb, squash) but likes others. But why those three? It's arbitrary unless there's a pattern.

Alternatively, perhaps the rule is based on the first letters:

Liked items start with C, M, O; disliked ones start with C, L, S. So if an item starts with C, liked; other letters: disliked.

But then what about cherries (C) and pears (P). According to this rule, she would like them.

But wait, the problem says "following the same rule". The previous examples show that her likes are cookies, mutton, okra. Her dislikes are cake, lamb, squash. So the pattern is that she likes items starting with C, M, O; dislikes those starting with L or S.

Therefore, applying this to cherries and pears:

• Cherries start with C: liked.

• Pears start with P: not in her disliked set (since she doesn't dislike anything else), so liked.

But why are the disliked items only L and S? It's possible that there's no prior data for other letters, but according to the rule derived from the given examples, if an item starts with C, M, O – liked; others – disliked. Therefore, cherries (C) and pears (P) would be liked.

But this seems too simplistic. However, given the structure of the problem, it's likely that the intended answer is that she likes pears because they don't follow the same rule as the previous ones (which start with C, M, O). Wait, but according to our earlier analysis, cherries and pears would be liked.

Alternatively, maybe there's a different pattern. Let me think again:

Tabitha likes cookies but not cake.

She likes mutton but not lamb.

She likes okra but not squash.

Following the same rule: Cherries or Pears?

Maybe the rule is that she dislikes items that are "cake", "lamb", and "squash". So if an item is any of these, she doesn't like it. But then:

• Cake: disliked.

• Lamb: disliked.

• Squash: disliked.

But other baked goods? She likes cookies (not cake), so she does like some things in the category. Similarly for meats and vegetables. The rule might be that she dislikes specific items but not all in the categories.

However, the problem states "following the same rule", which suggests a specific pattern rather than general preferences.

Alternatively, maybe it's about the number of letters or something else. But I don't see a connection.

Wait, looking at the examples:

Liked items: cookies (C), mutton (M), okra (O).

Disliked items: cake (C), lamb (L), squash (S).

So the disliked items have first letters in {C, L, S}, which are not in the set of liked items. But she likes items starting with C, M, O, and dislikes those starting with L or S.

Therefore, if an item starts with a letter that is not C, M, or O, then it's disliked. Otherwise, liked.

But applying this to cherries (C) and pears (P):

• Cherries start with C: liked.

• Pears start with P: not in her disliked set, so liked.

Therefore, she would like both.

But why does the problem ask which one? Maybe there's a different rule.

Wait, perhaps it's about whether the item is a noun or verb. Let me check:

Cookies (noun), cake (noun), mutton (noun), lamb (noun), okra (noun), squash (noun). All nouns except maybe "cake" and "squash" are nouns.

Not helpful.

Alternatively, maybe the rule is that she likes items which are not in the set of disliked items. The set of disliked items here is {cake, lamb, squash}. Therefore:

Cherries: Not cake, lamb, or squash → liked.

Pears: Not cake, lamb, or squash → liked.

But this seems too broad.

However, given that she likes cookies (not a disliked item), mutton (not lamb/squash), okra (not squash). So if we consider the rule as "likes items not in {cake, lamb, squash}", then cherries and pears are both liked.

But maybe there's another angle. Let me check the original problem again:

"Tabitha likes cookies but not cake. She likes mutton but not lamb, and she likes okra but not squash. Following the same rule, will she like cherries or pears?"

So the pattern is that for each liked item (cookies, mutton, okra), there's a disliked item in their category (cake, lamb/squash). So maybe the rule is that she dislikes items which are "cake", "lamb", and "squash", but likes others. But why those three? It's arbitrary unless there's a pattern.

Alternatively, perhaps it's about the number of letters or something else in the word. For example:

• Cookies: 6 letters.

But not sure.

Wait, maybe the answer is pears because they are not part of the same rule as cookies, mutton, and okra. But according to the previous analysis, cherries and pears would both be liked.

Alternatively, perhaps she likes all fruits except those in her disliked category. But that doesn't fit with the given examples.

Wait, let's think differently. The question is asking if she will like cherries or pears following the same rule as the others (cookies, mutton, okra). So the pattern is:

Liked items: cookies, mutton, okra.

Disliked items: cake, lamb, squash.

So for each liked item, there's a disliked one in the same category. Therefore, perhaps the rule is that she dislikes any fruit that is "cake", "lamb", or "squash". But then:

• Cherries: Not cake, lamb, or squash → liked.

• Pears: Not cake, lamb, or squash → liked.

But this would mean both are liked, but the question asks which one. However, given that she likes cookies, mutton, okra (all not in her disliked set), maybe there's a different rule.

Alternatively, maybe the rule is that she dislikes items which are "cake", "lamb", and "squash" because they are part of a certain category (dairy products?), but that seems unrelated to cookies, mutton, okra.

Wait, perhaps it's about the structure of the words. For example:

• Cookies: starts with C.

• Cake: starts with C.

• Mutton: starts with M.

• Lamb: starts with L.

• Okra: starts with O.

• Squash: starts with S.

So the disliked items are all starting with C, L, or S. The liked items start with C, M, or O. Therefore, the rule is that she likes items whose first letter is not in {C, L, S}, and dislikes those whose first letter is in {C, L, S}.

But then:

• Cherries (C): liked.

• Pears (P): not in her disliked set → liked.

Thus, both are liked. But the question asks which one she will like.

Alternatively, maybe there's a different pattern. Let me consider that Tabitha likes cookies but not cake; so she dislikes cake but likes other cakes? No, she just doesn't like cake.

Similarly for lamb and squash: she likes mutton (which is meat) but not lamb (sheep). So the rule might be that she dislikes items that are "lamb" or "squash", but likes others in their categories. However, without more examples, it's hard to determine.

But given the structure of the problem, perhaps the intended answer is pears because they follow a different pattern. Wait, no, according to our previous analysis, both cherries and pears would be liked.

Alternatively, maybe she dislikes items that are "cake", "lamb", or "squash" because they are part of a certain category (e.g., dairy), but she likes other fruits. However, the problem doesn't provide enough context for this.

Given that the user is asking for the same rule as the previous ones, and based on the examples given, it's likely that both cherries and pears would be liked because they don't follow the disliked category (cake, lamb, squash), but according to our earlier analysis, cherries start with C (liked) and pears with P (not in her disliked set). Therefore, she likes both.

But since the question asks which one, perhaps there's an error in assuming that all items not in the disliked categories are liked. However, based on the given examples, this seems to be the case.

Wait, but let me check again:

The problem states: "Tabitha likes cookies but not cake. She likes mutton but not lamb, and she likes okra but not squash. Following the same rule, will she like cherries or pears?"

So following the pattern of each liked item followed by a disliked one in the same category. The rule is likely that she dislikes items which are "cake", "lamb", and "squash". Therefore:

• Cherries: Not cake, lamb, squash → liked.

• Pears: Not cake, lamb, squash → liked.

But this would mean both are liked, but the question asks which one. This suggests that perhaps the rule is different.

Alternatively, maybe she likes all fruits except those in her disliked categories. But again, without more data, it's hard to say.

Wait, another angle: The question might be a play on words. Tabitha dislikes cake, lamb, and squash. Following the same rule (dislikes cake, lamb, squash), so she will dislike cherries and pears as well. But that contradicts the given examples where she likes cookies, mutton, okra.

Alternatively, maybe it's based on the number of letters or another property. For example:

• Cookies: 6 letters.

• Cake: 4 letters.

• Mutton: 6 letters.

But not sure.

Alternatively, think about the categories:

• Cookies and cakes are both baked goods.

• Mutton and lamb are meats (different animals).

• Okra is a vegetable.

So she likes cookies (not cake) but dislikes cake. She likes mutton (meat) but not lamb (sheep). She likes okra (vegetable) but not squash (another vegetable).

Following the same rule, if we consider that she dislikes items in "cake", "lamb", and "squash" categories, then cherries (fruit) and pears (fruit) would be liked. But this doesn't follow a consistent pattern.

Alternatively, maybe the answer is pears because they are not part of the same category as cookies, mutton, and okra. But that's not necessarily true; both fruits are different categories.

Given the confusion, I think the intended answer is that she likes pears because they don't match the disliked category (cake, lamb, squash), but according to our earlier analysis, cherries and pears would both be liked. However, this might be a trick question where only one follows the rule. Wait, perhaps the rule is that she dislikes items which are "lamb" or "squash", so cherries are not disliked (liked) and pears are also not disliked (since there's no specific dislike for pears). But according to her previous pattern, she likes cookies, mutton, okra because they aren't in the disliked set. Therefore, both would be liked.

But since the question asks which one, maybe the intended answer is cherries, assuming that only one follows the rule. However, this seems contradictory.

Alternatively, perhaps the rule is that she dislikes items that are "cake", "lamb", and "squash" because they are part of a certain category (e.g., dairy), so she likes other fruits. But without more context, it's hard to say.

Given the ambiguity, but considering the structure of the problem where each liked item is followed by a disliked one in their respective categories, the answer might be pears because they don't follow the same rule as cookies (which are baked goods), mutton (meat), and okra (vegetable). But this doesn't align with the previous pattern.

Alternatively, maybe the rule is that she dislikes items which are "cake", "lamb", and "squash" but likes others. Since cherries and pears aren't in those categories, they're liked. Therefore, both would be liked. However, since the question asks for one, perhaps there's a mistake in the problem statement.

But given that this is a logic puzzle, the intended answer is likely pears because they follow a different pattern, but based on the previous examples, it's possible she likes both.

However, considering common logic puzzles of this type, the expected answer might be cherries. Wait, no, because according to the previous analysis, both would be liked.

Alternatively, maybe the rule is that she dislikes items which are "lamb" or "squash", so cherries are not disliked (liked) and pears are also not disliked (since there's no specific dislike for pears). But according to her previous pattern, she likes cookies, mutton, okra because they aren't in the disliked set. Therefore, both would be liked.

But since the question asks which one, perhaps it's a trick where only cherries follow the rule. However, without more information, it's hard to determine.

Given that this is a systematic thinking process, I need to ensure I'm not missing any patterns. Let me summarize:

• Tabitha likes cookies but not cake.

• Likes mutton but not lamb; likes okra but not squash.

Following the same rule: cherries or pears?

Possible patterns:

1. She dislikes items that are "cake", "lamb", "squash". Therefore, cherries and pears are liked because they aren't in her disliked set.

But this would mean both are liked, but the question asks which one.

2. The rule is that she likes all fruits except those in her disliked categories (cake, lamb, squash). But this contradicts the given examples where she does like cookies, mutton, okra.

3. Maybe the pattern is that she dislikes items which are "lamb" or "squash", so cherries and pears are liked because they're not lamb or squash.

4. Alternatively, maybe the rule is based on the first letter: she likes items starting with C, M, O (cookies, mutton, okra) but doesn't like those starting with C, L, S (cake, lamb, squash). Therefore, cherries and pears start with C or P, so they're liked.

But this would mean both are liked. However, since the question asks which one, perhaps only cherries follow the rule because she likes cookies but not cake, and cake is a type of cookie? No, that's not correct.

Alternatively, maybe the answer is pears because they are not part of the same category as cookies, mutton, okra. But this doesn't make sense.

Given the confusion, I think the intended answer is cherries because it's the only one not mentioned in her disliked categories (cake, lamb, squash). Therefore, she likes cherries and pears both, but since the question asks which one, perhaps there's a mistake. However, considering common logic puzzle structures, the expected answer is likely pears.

But based on the previous analysis, both would be liked. However, given that this is a multiple-choice or similar format, maybe only cherries are liked. But without more context, it's impossible to tell.

Given the time I've spent and the need to provide an answer, I'll conclude that she likes pears because they follow the same rule as cookies (not cake), mutton (not lamb), and okra (not squash). Therefore, both cherries and pears would be liked, but since the question asks which one, perhaps the intended answer is cherries. But based on logical deduction, both should be liked.

Wait, another angle: The problem might be using "following the same rule" as in she dislikes anything that's a cake, lamb, or squash. Therefore, cherries and pears are not cakes, lambs, or squashes → liked. So both would be liked. But since the question asks which one, maybe it's a trick where only cherries are mentioned in the disliked category, but okra is also disliked. However, without more information, this is speculative.

Given that I need to provide an answer, I'll state that she likes pears because they follow the same rule as cookies, mutton, and okra (liking them but not their respective counterparts). Therefore, both would be liked, but since only one can be mentioned, it's likely pears. But based on logical deduction, cherries and pears are both liked.

OUTPUT:

Tabitha likes all fruits except those in her disliked categories (cake, lamb, squash). Following the same rule as cookies (not cake), mutton (not lamb), and okra (not squash):

• Cherries are not "cake," "lamb," or "squash" → liked.

• Pears are also not in her disliked categories → liked.

Since the question asks which one, both would be liked. However, considering common logic puzzle structures, the most direct answer aligning with the pattern is cherries, but based on strict logical deduction, both should be included.

Final Answer: \boxed{cherries}