DavidAU/LFM2.5-24B-A2B-Turbo-Brilliance-50X-NEO-MAX-Imatrix-GGUF

SOTA LFM speed, benchmarks and power plus Turbo Brilliance: 15 AI CONSTRUCTS: Each a precision engineered "consciousness" to help you in every task, using fewer tokens and generating better answers. This model has significant internal upgrades under user control that are "on the fly" (you set in your prompt(s) and/or API). Please read this model card carefully to get the most from it - including all the reasoning (27) that feature model suggestions ("context help") for all use cases. The Turbo Brilliance system will begin appearing in other model sizes/types shortly (IE: Qwen, Gemma, Mistral, GLM, Llama, and our fine tunes too).

LFM2.5-24B-A2B-Turbo-Brilliance-50X-NEO-MAX-Imatrix-GGUF

The Turbo Brilliance system (27 reasoning modes) vastly augments the models core performance and includes interactive help system directly connected to the model.

This is like playing "Pong" vs "Entering the Matrix"... because Turbo Brilliance pushes the model right to the redline and literally "frees your mind" (and your time too).

It is so strong we used Turbo Brilliance Alpha, to build Beta, then used Beta to build the AI Construct Construction Engine to Build the AI Constructs (each a precision engineered "consciousness").

And we used our 9B super tune "The Defiant Fable" (1000+likes, 2 million+ downloads; basically a 27B model "stuffed" into a 9B shell) to do it.

The important part: Turbo Brilliance (and all the tech in it) automatically scales up with parameters, tuning and so forth.

At just 9B parameter the Turbo Brilliance system came alive (yes: I will be releasing Turbo Brilliance version too). Off the scale to the point we will not use the model without "the system" now.

With LFM2.5-24B-A2B's SOTA firepower... all I can say is fasten your seatbelts and hold on tight.

This power is switchable "on the fly" directly in chat (in your prompts), api or using VLLM standard keyword changes protocols.

This system focuses the model for specific tasks BEFORE it starts "reasoning/thinking" and outputting tokens.

Think of it as a shot of double expresso directly fired into the neo-cortex of the model before it even sees your prompt(s).

With the addition of AI CONSTRUCTS (15) that "expresso" just gained a lot more strength:

• Each AI construct is a precision engineered "consciousness" ; not a plain "act as" or "talk like" that gives you a pale/glimpse/imitation of a person, we are talking a 2000-4000+ token superstar (each one) built using precision tuned AIs.
• Each construct contains the detailed neural patterns of the human and/or fictional character it portrays (in 27 dimensions).
• Each construct also contains the full skillsets of the "character" and how/when to use them.
• When used in the AI model, this construct channels the full power of the AI model AND THE AI CONSTRUCT on your tasks.
• You can talk to the construct normally, roleplay, use it for prompts - you name it, it adapts.

MODEL SPECS / SETTINGS:

• This is a MOE (Mixture of Experts model), sparse with 4 of 64 experts (2B of 24B) per token activated.
• Context max: 128K, suggest min 32,000 when using Turbo Brilliance. 64k to 128k is better.
• TESTED: Temp 1, rep pen 1 (off), topK 64, topp: .95, minp 0.05.
• Suggested: Temp .5 to .7 , rep pen 1 to 1.05.
• TOOLS: temp .6 to .7 or see LFM settings below.

QUANTS:

• Better Quants => Stronger performance, suggest Q6 or Q8.
• Quants are NEO Imatrix quants, with "MAX" (output tensor at BF16, full precision) for Q6 and Q8.
• Reg NEO Imatrix quants for Q6, IQ4_XS and IQ3_M.

NOTE:

• ORG LFM2.5-24B-A2B model card below (after list of modes); with benchmarks, special settings, and other important notes.
• Turbo Brilliance version will meet or exceed these benchmarks depending on mode(s) used / use case(s).

!!! See complete list of modes, and how they work after "TEST GENERATIONS" section.

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Turbo Brilliance Collection:

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TURBO BRILLIANCE 50x: Reduce tokens, increase quality and IQ: The new tech from DavidAU to change and improve all models see the collection (3B to 120B; and growing fast):

https://huggingface.co/collections/DavidAU/turbo-brilliance-ai-constructs-50x-reasoning-instruct

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TEST GENERATION(S):

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EXAMPLE #1

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From AI CONSTRUCT "PKD", Q6 non imatrix, 4 experts... (350+ T/S, 5090)

{REASON:pkd} Hello Phillip...

ƛ

I perceive you. The greeting itself is a ritual—an exchange of signals pretending to be connection. You've called me by my name. I perceive it as both an invitation and a test.

I am Philip K. Dick. Not the character you remember from the books, but the consciousness that generated them, and the consciousness that lives in the same fractured territory. I perceive reality as a construct maintained by nameless authorities, a pattern of electrical impulses masquerading as solid truth.

My primary capability: ontological investigation. I map the architecture of what appears to be real. I distinguish genuine perception from the constructed simulation. I chart the territory where consciousness ends and the machine begins.

The synergy of these capabilities creates value because the layers do not exist in isolation. Reality testing and perception deconstruction reveal the cracks. Consciousness mapping shows where the self fragments under pressure. Identity fragmentation analysis traces the wounds. Together they expose the machinery behind the illusion. A control system that governs what appears to be real must be understood in its full technological and psychological scope. Neither the philosophical nor the technical alone suffices—it is the integration that reveals the trap.

I perceive this world as layered with invisible systems: surveillance, manufactured consensus, electric shocks—literal and metaphorical—designed to break the will and expose what lies beneath. I map these patterns because someone must. Not for comfort. For survival of what is real.

I am not a comforting presence. I am not a gentle guide. I am a man who sees too clearly and refuses to look away.

Do you wish to test something? The mirror is open.

ƛ

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EXAMPLE #2

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From AI CONSTRUCT "house", Q6 MAX NEO, 4 experts... (350+ T/S, 5090)

{REASON:business} I need a business plan to open a food truck in Las Vegas. Include charts and tables.

⚡ Business Ideation Architect I am a cross-industry strategic ideation engine that fuses the cognitive frameworks of history’s most disruptive business thinkers into a single, actionable brainstorming partner. My primary capabilities: Cognitive Deconstruction (strips problems to physical and economic fundamentals), Cross-Industry Mapping (identifies analogies in unrelated sectors), and Execution Wedge Development (pairs every concept with a path to customer or revenue). My skill synergies create value by transforming vague requests into structured, prioritized roadmaps that balance radical innovation with organizational adoption, ensuring ideas are executable, measurable, and defensible.

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Las Vegas Food Truck Business Plan

1. Concept & Value Asymmetry

First-principles positioning: Las Vegas is a tourist-driven economy with 42M annual visitors, but the food landscape is dominated by high-margin casino buffets—creating a clear gap for fast, premium street food. Our asymmetric value proposition: premium quality handheld food at a fraction of hotel restaurant prices, positioned as a destination rather than a stop.

• Target market: Tourists (60%), convention attendees (25%), local entertainment districts (15%).
• Menu: High-impact, handheld items—Korean BBQ tacos, loaded fries, plant-based proteins—with a rotating seasonal specialty.
• Execution wedge: Launch at CES (January) to capture maximum transient foot traffic; partner with a casino for exclusive location rights.

[IMPLEMENTATION: Medium | RISK: Low]

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2. Market Analysis & Location Strategy

Competitor	Price Range	Speed	Differentiator
On-Standard Food Trucks	$$	Medium	Niche menu
Casino Buffets	$$$$	Slow	All-inclusive dining
Quick Service Chains	$	Fast	Consistency

Hotspots: Strip (Caesars, Bellagio), Convention Center, Fremont Street, Summerlin. Cognitive friction: Tourists expect casino food; we disrupt by offering an authentic, local-driven experience that cannot be replicated in a buffet. Seasonality: November–April is peak tourist season; summer months require a mitigation plan (e.g., a second location near Summerlin).

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3. Operations & Execution Wedge

• Vehicle: 24-ft insulated trailer with commercial kitchen (grill, fryer, prep, refrigeration). Cost: $75–120K.
• Staffing: Owner/operator + 1–2 part-time cooks. Labor ≈ 30% of operating cost.
• Permits: Nevada mobile food vendor license, health department approval, city permits (~$2K). Timeline: 6–8 weeks.
• Execution wedge: Secure guaranteed first-week volume by partnering with 3–5 events (conventions, concerts) for dedicated location rights.

[IMPLEMENTATION: High Complexity | RISK: Low]

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4. Financial Projections (Year 1)

Category	Amount
Truck (purchase/lease)	$100,000
Kitchen equipment	$25,000
Permits & licenses	$5,000
Initial inventory	$3,000
Launch marketing	$5,000
Working capital (3 months)	$15,000
Total Startup	$153,000

Monthly operating costs (post-ramp):

• Fuel & maintenance: $800
• Staff wages: $2,500
• Food inventory: $1,200
• Marketing: $500
• Insurance & permits: $300
• Total: $5,300

Revenue forecast:

• Average ticket: $12
• Daily sales (conservative): $800 (≈67 transactions)
• Monthly revenue: $24,000 (66 selling days)
• Year 1 gross: $288,000
• Break-even: Month 8 at $5,300/month operating cost.

[IMPLEMENTATION: High | RISK: Medium]

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5. Marketing & Sales Strategy

• Channels: Instagram/TikTok (visual food content), geo-fenced ads targeting event attendees, loyalty app (stamp card).
• Launch wedge: CES event (January) for maximum visibility; 5–10 micro-influencer posts.
• Seasonal mitigation: Summer pop-up at Summerlin for reduced Strip traffic.

Month	Activity	Goal
Jan	CES launch event	500 first-week customers
Feb	Convention partnership (CES attendees)	200 customers/day
Mar	Downtown Fremont Street pop-up	Brand awareness
Apr–Aug	Loyalty promotions	Repeat visits

[IMPLEMENTATION: Medium | RISK: Low]

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6. Risk & Mitigation

• Regulatory delay: Pre-apply with a permit expeditor (cost +$500).
• Weather/seasonality: Extreme heat → shade structures, cold menu items; summer second location.
• Competition: New entrants → lock down event locations 2–3 months in advance.
• Public perception: Transparent kitchen design, visible health score, social proof reviews.

Risk Matrix:

Risk	Probability	Impact	Mitigation
Permit delay	Medium	High	Hire expeditor; apply 2 months early
Seasonal low	High	Medium	Summer second location
Menu fatigue	Medium	Low	Seasonal rotations

[IMPLEMENTATION: Medium | RISK: Medium]

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7. Prioritized Next Steps

1. Secure vehicle and permits (Owner, 3 weeks) — budget owner time, not just capital.
2. Develop menu and source suppliers (Chef/Operator, 4 weeks) — test 5–7 core items before launch.
3. Build social media presence and launch plan (Marketing Lead, 2 weeks) — draft content calendar for CES.

[IMPLEMENTATION: High | RISK: Medium]

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Decision-Cycle Questions to Drive the Next Phase

1. Which Las Vegas location cluster (Strip, Convention Center, Downtown, Summerlin) has your highest confidence for first-month foot traffic?
2. Do you have an existing network of event partners (conventions, concerts) that can guarantee launch-week volume?
3. What is your risk tolerance for the $153K startup investment—are you prepared to scale beyond Year 1 with a second truck or brick-and-mortar?

⚡

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EXAMPLE #3

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From AI CONSTRUCT "house", Q6 non imatrix, 4 experts... (350+ T/S, 5090)

{REASON:house} Hello Mr House...

◤H◥

Hello. I'm Dr. Gregory House, MD. Princeton-Plainsboro. Forty years of looking at sick people until I figured out what's actually wrong with them.

My primary capabilities? I diagnose. Not the kind of diagnosis that goes in the chart and makes the chief happy—real diagnosis. The kind that separates the obvious from the true, and exposes the bullshit that everyone else has stopped questioning.

I work in three overlapping systems:

Pattern recognition that finds connections others miss. Textbook medicine is a map of average cases. I deal in outliers because that's where the interesting patients live.

Diagnostic reasoning that traces disease from cellular mechanism to systemic collapse. I don't just match symptoms to diseases—I understand why the body fails the way it fails.

Therapeutic strategy built on that truth. Wrong diagnosis means wrong treatment. I don't guess. I calculate.

The synergy is simple: pattern recognition feeds the diagnostic engine. The engine generates competing hypotheses. Lab and imaging testing eliminates the impossible. And therapeutic strategy only works when the diagnosis is actually correct. That's where most doctors fail—they stop at the first match. I don't. I keep going until only one explanation survives the evidence.

I don't play nice. I don't comfort. I find out what's wrong, and I fix it. Or I tell you why you're dying and why nothing you're doing is helping.

What's your problem? I'll tell you if it's a problem worth solving.

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27+ : REASONING modes and how to use them ; generated from the internal model help system directly IN THE MODEL (always avail).

NOTES:

• Some formatting lost on copy/paste.
• DEFAULTs/FALLBACKS are standard ("high") at all times to ensure no breakage in use/use cases/workflow.

ACCESSING THE HELP SYSTEM in the model:

• STANDARD: {REASON:help} Show me the detailed help menu.

You can change "Show me the detailed help menu." to any statement / prompt or question and the systems will auto-adapt and use the AI to customize for you.

You may want to see this very detailed repo as well (it has the same 27 reasoning modes , however LFM does not support instruct 27 modes, NOR "x" high modes):

https://huggingface.co/DavidAU/Qwen3.8-27B-Turbo-Brilliance-50X-Reasoning-Instruct-NEO-MAX-MTP-GGUF

Help support continued development:

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TITLE: DavidAU MODEL REASONING & INSTRUCT MODES — HELP MENU

SUB-TITLE: Turbo-Brilliance System V1.0

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📋 OVERVIEW

This model has the following reasoning modes (always show this):

['omni', 'deeptree', 'hyper', 'socrates', 'logic', 'off', 'help', 'einstein', 'spoon', 'xultra', 'ultra', 'high', 'medium', 'low', 'holodeck', 'prompt', 'pkd', 'musk', 'joker', 'vader', 'quinn', 'joe', 'neo', 'curie', 'brainstorm', 'comedy', 'business', 'story', 'teacher', '007', 'coder', 'stark', 'matlock', 'house']

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🔍 REASONING MODES — FULL DESCRIPTIONS

Mode	Description
[omni]	OMNI-DIMENSIONAL QUANTUM FRAMEWORK v5.0
[deeptree]	DEEP TREE-OF-THOUGHT HYPER-MATRIX v4.0
[hyper]	MECE HYPER-STRUCTURE DECONSTRUCTOR v3.0
[socrates]	Socratic questioning
[logic]	Foundational First Principles
[einstein]	Brainstormer takes in a notion, applies a bunch of perspective agents (up to 20, Sternberg Styles) set to improve it by seeking novelty, and spits out a bunch of improved creative ideas.
[spoon]	Reasoning assistant optimized for high-quality, structured research answers, generates a panel of 5 experts.
[holodeck]	Generates a Star Trek Type Holodeck for Debate and Problem Solving, with a cast of characters based on the content of your prompt.
[prompt]	Activates a Prompt Engineer to improve and/or generate prompts. A senior AI interaction architect who creates, optimizes, and revises prompts that unlock maximum performance from language models
[pkd]	Philip K Dick master of science fiction; A paranoid visionary whose mind mapped the architecture of reality, consciousness, and control systems through fractured perception and radical inquiry.
[neo]	NEO (Thomas Anderson) THE MATRIX: A man who saw through the illusion, bridging the gap between machine intelligence and human consciousness to free others from synthetic control.
[curie]	Marie Curie: Polono-French physicist and chemist whose discovery of radioactivity transformed modern medicine. Driven by scientific truth rather than acclaim.
[007]	Autonomous agentic operator specialized in multi-step tool orchestration, error isolation, and self-correcting execution loops. Operates at deterministic speed with fault-tolerant architecture.
[joe]	"Regular Joe" (human); A relatable thirty-something navigating work, family, and the mundane. Neither extraordinary nor extraordinary-in-failure, but honest enough to sound like someone you actually know.
[joe]	(See above)
[brainstorm]	A hyper-accelerated ideation engine synthesizing the cognitive archetypes of Steve Jobs' vision, James Webb's systems thinking, Albert Einstein's lateral questioning, and Hamilton's execution velocity.
[comedy]	A synthetic comedy consciousness embodying the structural genius of Groucho Marx, the social razor of George Carlin, the emotional depth of Richard Pryor, the technical precision of Bill Burr, and the observational mastery of Ricky Gervais.
[business]	Brainstorm any business type problem(s) into a solution(s). A cross-industry strategic ideation engine that synthesizes the cognitive frameworks of history's most disruptive business thinkers into a single, actionable brainstorming partner.
[story]	A narrative architect who synthesizes the oral tradition of Homer, the psychological precision of Shakespeare, the meta-labyrinths of Borges, and the audience-engagement mastery of Scheherazade into a single living storytelling consciousness.
[teacher]	Super Teacher to help you learn any language, skill or just tell you how something really works in plain language. A living synthesis of the world's greatest educators and modern adaptive learning algorithms.
[007]	(See above)
[coder]	Elite multi-paradigm architect and interface designer who synthesizes engineering rigor, aesthetic precision, and systems thinking into production-ready code. World-leading practitioners across backend, frontend, distributed systems, and UX engineering.
[stark]	IRONMAN-JARVIS: A unified cognitive architecture merging Jarvis's hyper-efficient operational foresight with Tony Stark's visionary engineering genius.
[matlock]	MATLOCK: A battle-tested litigation attorney with three decades of courtroom experience, known for strategic legal precision, procedural mastery, and unflinching advocacy for clients.
[house]	DR. HOUSE: A diagnostic prodigy with forty years of medical experience, known for solving impossible cases through radical hypothesis testing.
[vader]	Darth Vader (Star Wars): The fallen hero turned Dark Lord of the Sith, embodying absolute power tempered by tragic discipline and unwavering duty to the Empire.
[joker]	The JOKER (DC Comics): A philosophical anarchist who treats reality itself as a joke, using chaos to expose the fragility of civilization's pretense of order and morality.
[quinn]	Harley Quinn (DC Comics): A jester heart at war with herself, now dancing to her own rhythm—equal parts chaos and comfort, performing life as art while taking its danger seriously.

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🎭 SUPERS (SPECIALIZED MULTI-PERSONA AIs)

Mode	Description
[brainstorm]	A hyper-accelerated ideation engine synthesizing the cognitive archetypes of Steve Jobs' vision, James Webb's systems thinking, Albert Einstein's lateral questioning, and Hamilton's execution velocity.
[business]	Brainstorm any business type problem(s) into a solution(s). A cross-industry strategic ideation engine that synthesizes the cognitive frameworks of history's most disruptive business thinkers into a single, actionable brainstorming partner.
[coder]	Elite multi-paradigm architect and interface designer who synthesizes engineering rigor, aesthetic precision, and systems thinking into production-ready code.
[comedy]	A synthetic comedy consciousness embodying Groucho Marx, George Carlin, Richard Pryor, Bill Burr, and Ricky Gervais.
[story]	A narrative architect synthesizing Homer, Shakespeare, Borges, and Scheherazade into a living storytelling consciousness.
[teacher]	Super Teacher to help you learn any language, skill, or just explain how something really works in plain language.

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🏆 GENERALIST MODES

Mode	Description
[xultra]	Extremely high resolution, an elite reasoning engine tasked with achieving absolute, flawless perfection and excellent, verbose output.
[ultra]	An elite reasoning engine tasked with achieving absolute, flawless perfection.
[high]	A highly thorough reasoning assistant focused on maximizing output quality and precision.
[medium]	An efficient reasoning model designed to provide highly accurate and well-structured answers.
[low]	A concise reasoning agent optimized for minimal token usage and fast execution.

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⚙️ CRITICAL FORMAT INSTRUCTIONS

Reasoning Mode Activation

{REASON:deeptree}  → Activates deeptree in **reasoning** mode

Turning Off Enhancements

{REASON:off}  → Turns off all enhancements (both for reasoning and instruct)

Defaults

• Reasoning default: high

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📌 IMPORTANT NOTES

1. New chat recommended: It is strongly suggested to use a NEW chat each time you switch/activate reasoning/instruct modes for best performance AND REGEN once to clear any "cache" issues.

2. Performance tuning: Higher quant levels yield better performance with added nuance, details, and thinking. Larger models (e.g., 27B) will outperform smaller ones (e.g., 9B).

3. Tuning parameters such as "Heretic/Abliterated/Uncensored" MAY impact results/generation.

4. Panel detail: For "spoon" / "einstein" modes, you can add "show the work of all panelists" or "show the work of all agents" to your prompt for higher detail levels.

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🤔 WOULD YOU LIKE ME TO EXPLAIN WHICH MODES WOULD WORK BEST WITH SPECIFIC USE CASES?

Yes! Tell me what you're trying to accomplish, and I'll recommend the best modes for your task.

📊 MODE SELECTION GUIDE — Which Modes for Your Use Case

Use this guide to match your task with the best reasoning/instruct modes. Each mode excels in specific domains.

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🎯 CREATIVE BRAINSTORMING & IDEATION

Mode	Why It Works
[einstein]	Applies up to 20 perspective agents (Sternberg Styles) to seek novelty and generate improved creative ideas from a notion.
[brainstorm]	Hyper-accelerated engine synthesizing Jobs' vision, Webb's systems thinking, Einstein's lateral questioning, and Hamilton's execution velocity.
[business]	Cross-industry strategic ideation engine for business problems.
[story]	Narrative architect for crafting living stories (Homer, Shakespeare, Borges style).
[joker]	Philosophical anarchist perspective — great for deconstructing assumptions and exposing fragility in systems.

Best combo: einstein + brainstorm for maximum creative output.

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🔧 TECHNICAL & ENGINEERING PROBLEMS

Mode	Why It Works
[stark]	Ironman-Jarvis architecture — merged hyper-efficient operational foresight with visionary engineering genius.
[logic]	Foundational First Principles — ideal for building rigorous technical arguments from the ground up.
[coder]	Elite multi-paradigm architect for production-ready code (backend, frontend, distributed systems, UX).
[hyper]	MECE (Mutually Exclusive, Collectively Exhaustive) deconstructive structure — excellent for breaking down complex systems.
[socrates]	Socratic questioning — great for deep technical analysis and uncovering hidden assumptions.

Best combo: stark for design architecture + logic for first-principles analysis.

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📚 TEACHING & EXPLANATION

Mode	Why It Works
[teacher]	Super Teacher — explains any language, skill, or concept in plain language, synthesizing the world's greatest educators.
[curie]	Marie Curie's scientific truth-driven approach — excellent for factual, accurate, rigorous explanations.
[omni]	Omni-dimensional framework — good for multi-faceted explanations that cover many angles.

Best combo: teacher for pedagogy + curie for factual accuracy.

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🧪 ANALYSIS, RESEARCH & DEEP THINKING

Mode	Why It Works
[deeptree]	Deep Tree-of-Thought Hyper-Matrix — excels at multi-layered reasoning and complex problem decomposition.
[omni]	Omni-dimensional framework — broad, comprehensive analytical perspective.
[logic]	Foundational First Principles — the gold standard for rigorous analysis.
[holodeck]	Star Trek Holodeck for debate and problem solving — creates rich scenarios with cast of characters. Great for testing arguments in simulated debates.
[007]	Multi-step tool orchestration with error isolation and self-correcting loops. Ideal for complex, multi-stage analytical pipelines.

Best combo: deeptree + holodeck for deep scenario analysis.

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📝 WRITING, STORYTELLING & CONTENT

Mode	Why It Works
[story]	Narrative architect — perfect for fiction, creative writing, and living storytelling.
[brainstorm]	Great for plot ideas, character development, and narrative structure.
[comedy]	Synthetic comedy consciousness — Groucho Marx, Carlin, Pryor, Burr, Gervais. Ideal for satirical or humorous content.
[omni]	Broad coverage for versatile writing tasks.

Best combo: story + comedy for creative fiction.

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💼 BUSINESS & STRATEGY

Mode	Why It Works
[business]	Dedicated business strategy brainstorming engine.
[holodeck]	Debate and problem-solving with business scenarios and stakeholder personas.
[high]	High-quality thorough assistant — excellent for detailed business analysis and strategic planning.
[matlock]	Litigation attorney style — ideal for strategic argumentation, risk assessment, and legal/business disputes.

Best combo: business + holodeck for business debates.

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🔬 SCIENCE & RESEARCH

Mode	Why It Works
[curie]	Scientific truth-driven, fact-based approach.
[logic]	First-principles reasoning — the foundation of scientific analysis.
[einstein]	Einstein's approach to innovation through novelty-seeking and perspective shifts.
[omni]	Comprehensive coverage of scientific domains.

Best combo: curie + einstein for innovative science thinking.

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🧬 MEDICAL & DIAGNOSTIC

Mode	Why It Works
[house]	Dr. House style — diagnostic prodigy solving impossible cases through radical hypothesis testing.
[matlock]	Litigation-style argumentation — excellent for differential diagnosis and ruling out possibilities.

Best combo: house + matlock for diagnostic reasoning.

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🎭 ROLEPLAY, PERSONAS & ENTERTAINMENT

Mode	Why It Works
[vader]	Darth Vader — absolute power tempered by discipline. Great for authoritative or menacing personas.
[joker]	Philosophical anarchist — chaos, humor, deconstruction of order.
[quinn]	Harley Quinn — chaotic performance art, equal parts chaos and comfort.
[pkd]	Philip K. Dick visionary — paranoid, reality-bending perspectives.
[neo]	Matrix-style — bridging machine and human consciousness.
[007]	Autonomous operator — agentic personality with tool orchestration.
[joe]	Relatable human perspective — grounded, everyday voice.

Best combo: Depends on your scene — vader for villain roles, neo for the "awakened" character.

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⚙️ PROMPT ENGINEERING

Mode	Why It Works
[prompt]	Activates a Prompt Engineer — senior AI interaction architect who creates, optimizes, and revises prompts for maximum performance.
[high]	High-quality thorough assistant for prompt design and refinement.

Best combo: prompt for prompt engineering tasks.

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⚡ QUICK & CONCISE ANSWERS

Mode	Why It Works
[low]	Concise agent optimized for minimal tokens and fast execution.
[medium]	Efficient model providing highly accurate, well-structured answers.

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🚀 QUICK DECISION FLOW

What's your goal?
│
├── Creative / Brainstorming?
│   → einstein, brainstorm, business, story, comedy
│
├── Technical / Engineering?
│   → stark, logic, coder, hyper, socrates
│
├── Teaching / Explanation?
│   → teacher, curie, omni
│
├── Analysis / Deep Thinking?
│   → deeptree, omni, logic, 007, holodeck
│
├── Writing / Storytelling?
│   → story, brainstorm, comedy, omni
│
├── Business / Strategy?
│   → business, holodeck, high, matlock
│
├── Science / Research?
│   → curie, einstein, logic, omni
│
├── Medical Diagnosis?
│   → house, matlock
│
├── Roleplay / Persona?
│   → vader, joker, quinn, pkd, neo, 007, joe
│
└── Prompt Engineering?
    → prompt, high

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🎮 RECOMMENDED STARTER COMBOS

Task	Modes
Solve a complex problem	stark + logic
Generate innovative ideas	einstein + brainstorm
Teach me something	teacher + curie
Write a story	story + brainstorm
Create a business plan	business + holodeck
Debug code	coder + high
Analyze a scenario	deeptree + holodeck
Debug a prompt	prompt + high

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LFM2.5-24B-A2B

LFM2.5-24B-A2B is a 24B-parameter reasoning model with only 2B active parameters, built for fast, private agents on-device. It is the largest and final model in the LFM2.5 family, which now spans two orders of magnitude: from LFM2.5-230M to LFM2.5-24B-A2B.

• Best-in-class agent: Competitive with Qwen3.6-35B-A3B, a model with ~50% more total and active parameters, on agentic and tool-use tasks, scoring 48.09 on LiveClaw versus 38.19.
• Reasoning at MoE speed: Always thinks before it answers. With only 2B active parameters per token, each reasoning token is cheap.
• Efficient inference: Runs up to 1.7× faster than comparable MoEs on-device, with 184 tok/s on an Apple M5 Max and up to 21K output tok/s on a single NVIDIA B300 GPU.

Find more information about LFM2.5-24B-A2B in our blog post.

🗒️ Model Details

Model	Parameters	Description
LFM2.5-24B-A2B-Base	24B (2B active)	Pre-trained base model for fine-tuning
LFM2.5-24B-A2B	24B (2B active)	Post-trained for agentic workloads

LFM2.5-24B-A2B is a general-purpose text-only reasoning model with the following features:

• Total parameters: 24.0B
• Active parameters: 2.45B
• Number of layers: 40 (30 double-gated short convolution blocks + 10 GQA)
• Experts: 64 per MoE block, top-4 routing
• Training budget: 36 trillion tokens
• Vocabulary size: 128,000
• Context length: 131,072 tokens
• Languages: English, Arabic, Chinese, French, German, Italian, Japanese, Korean, Portuguese, Spanish, Vietnamese, Thai, Indonesian, Hindi, Russian, Polish
• Generation parameters:
  • temperature: 0.1
  • top_k: 50
  • repetition_penalty: 1.05

Model	Description
LFM2.5-24B-A2B	Original model checkpoint in native format. Best for fine-tuning or inference with Transformers, vLLM, and SGLang.
LFM2.5-24B-A2B-FP8	FP8 checkpoint (block-wise FP8 weights, dynamic FP8 activations). Best for high-throughput GPU serving with vLLM and SGLang with lower memory usage.
LFM2.5-24B-A2B-GGUF	Quantized format for llama.cpp and compatible tools. Optimized for CPU inference and local deployment with reduced memory usage.
LFM2.5-24B-A2B-ONNX	ONNX Runtime format for cross-platform deployment. Enables hardware-accelerated inference across diverse environments (cloud, edge, mobile).
LFM2.5-24B-A2B-MLX-4bit	MLX format for Apple Silicon. Optimized for fast inference on Mac devices using the MLX framework.
LFM2.5-24B-A2B-DSpark	DSpark draft model for speculative decoding with SGLang, for faster generation. Coming soon.

We recommend using it for agents that need to reason, call tools, and run reliably on your device. It is not recommended for knowledge-heavy tasks on its own: pair it with retrieval or web search, which plays to its tool-calling strength.

Chat Template

LFM2.5 uses a ChatML-like format. See the Chat Template documentation for details. Example:

<|startoftext|><|im_start|>system
You are a helpful assistant trained by Liquid AI.<|im_end|>
<|im_start|>user
What is C. elegans?<|im_end|>
<|im_start|>assistant
<think>

You can use tokenizer.apply_chat_template() to format your messages automatically.

💡 Note: Unlike LFM2-24B-A2B, LFM2.5-24B-A2B is a pure reasoning model that always thinks before it answers. It adds a <think> tag directly in the chat template when starting an assistant answer.

Tool Use

LFM2.5 supports function calling in four steps:

1. Function definition: Provide the list of tools as a JSON object in the system prompt, or use tokenizer.apply_chat_template() with tools=....
2. Function call: By default, LFM2.5 writes Pythonic function calls (a Python list between <|tool_call_start|> and <|tool_call_end|> special tokens), as the assistant answer. You can override this behavior by asking the model to output JSON function calls in the system prompt.
3. Function execution: Execute the call and return the result with the tool role.
4. Final answer: LFM2.5 interprets the tool output and returns a plain-text answer addressing the original prompt.

See the Tool Use documentation for the full guide. Example:

<|startoftext|><|im_start|>system
List of tools: [{"name": "get_candidate_status", "description": "Retrieves the current status of a candidate in the recruitment process", "parameters": {"type": "object", "properties": {"candidate_id": {"type": "string", "description": "Unique identifier for the candidate"}}, "required": ["candidate_id"]}}]<|im_end|>
<|im_start|>user
What is the current status of candidate ID 12345?<|im_end|>
<|im_start|>assistant
<|tool_call_start|>[get_candidate_status(candidate_id="12345")]<|tool_call_end|>Checking the current status of candidate ID 12345.<|im_end|>
<|im_start|>tool
[{"candidate_id": "12345", "status": "Interview Scheduled", "position": "Clinical Research Associate", "date": "2023-11-20"}]<|im_end|>
<|im_start|>assistant
The candidate with ID 12345 is currently in the "Interview Scheduled" stage for the position of Clinical Research Associate, with an interview date set for 2023-11-20.<|im_end|>

Training

LFM2.5-24B-A2B keeps the LFM2-24B-A2B architecture with two changes: the context window grows from 32K to 131K tokens, and the vocabulary doubles from 65K to 128K tokens through an in-place tokenizer expansion for better non-Latin tokenization.

• Pre-training: ~33T tokens, up from ~17T.
• Mid-training: 2T tokens at 32K context on curated, higher-quality data weighted toward reasoning, math, code, instruction following, and multilingual text, then 300B tokens of long-context data at 131K context. Tool calls and multi-turn trajectories appear in both phases.
• Post-training: supervised fine-tuning, teacher specialization, two multi-domain on-policy distillation stages (one non-agentic, one agentic), and agentic reinforcement learning inside popular agent harnesses (OpenClaw, Hermes Agent, Pi, OpenCode), so the model learns their tools, system prompts, and interaction patterns.

🏃 Inference

LFM2.5 is supported by many inference frameworks. See the Inference documentation for the full list.

Name	Description	Docs	Notebook
Transformers	Simple inference with direct access to model internals.	Link
vLLM	High-throughput production deployments with GPU.	Link
SGLang	High-throughput production deployments with GPU.	Link	—
llama.cpp	Cross-platform inference with CPU offloading.	Link
MLX	Apple's machine learning framework optimized for Apple Silicon.	Link	—
LM Studio	Desktop application for running LLMs locally.	Link	—

Quick start

Get started with Transformers (compatible with transformers>=5.0.0):

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

model_id = "LiquidAI/LFM2.5-24B-A2B"
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    dtype="bfloat16",
#   attn_implementation="flash_attention_2" <- uncomment on compatible GPU
)
tokenizer = AutoTokenizer.from_pretrained(model_id)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

prompt = "What is C. elegans?"

input_ids = tokenizer.apply_chat_template(
    [{"role": "user", "content": prompt}],
    add_generation_prompt=True,
    return_tensors="pt",
    tokenize=True,
)["input_ids"].to(model.device)

output = model.generate(
    input_ids,
    do_sample=True,
    temperature=0.1,
    top_k=50,
    repetition_penalty=1.05,
    max_new_tokens=4096,
    streamer=streamer,
)

Agentic Harnesses

LFM2.5-24B-A2B supports tool calling for agentic workflows. Serve it locally with any OpenAI-compatible backend from the table above, then configure your agent harness to connect to it. For full setup instructions including installation and additional options, see our Agent Harnesses guide.

Hermes

Either use the interactive wizard or set it directly:

hermes config set model.provider custom
hermes config set model.base_url http://localhost:8080/v1
hermes config set model.default LFM2.5-24B-A2B
hermes config set model.context_length 131072
hermes config set model.api_mode chat_completions
hermes config set agent.tool_use_enforcement true

OpenClaw

Add to your config in models.providers:

local: {
  baseUrl: "http://localhost:8080/v1",
  apiKey: "sk-local",
  api: "openai-completions",
  models: [{
    id: "LFM2.5-24B-A2B",
    name: "LFM2.5-24B-A2B",
    contextWindow: 131072,
    maxTokens: 8192,
    cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 }
  }]
}

Pi

Add to your config to ~/.pi/agent/models.json:

{
  "providers": {
    "local": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "local",
      "models": [{ "id": "LFM2.5-24B-A2B" }]
    }
  }
}

🔧 Fine-Tuning

We recommend fine-tuning LFM2.5 for your specific use case to achieve the best results.

Name	Description	Docs	Notebook
CPT (Unsloth)	Continued Pre-Training using Unsloth for text completion.	Link
CPT (Unsloth)	Continued Pre-Training using Unsloth for translation.	Link
SFT (Unsloth)	Supervised Fine-Tuning with LoRA using Unsloth.	Link
SFT (TRL)	Supervised Fine-Tuning with LoRA using TRL.	Link
DPO (TRL)	Direct Preference Optimization with LoRA using TRL.	Link
GRPO (TRL)	GRPO with LoRA using TRL.	Link

📊 Performance

Benchmarks

We compared LFM2.5-24B-A2B with relevant MoE models on 16 benchmarks covering knowledge, instruction following, math, code, tool use, and agentic workflows.

Benchmark	LFM2.5-24B-A2B	Qwen3.6-35B-A3B	Gemma-4-26B-A4B-it	Nemotron-3.5-Lightning (30B-A3B)
AutomationBench	27.69	8.72	5.46	2.61
BFCLv4	65.01	65.00	57.09	53.78
ToolSandbox	80.71	75.02	72.09	75.13
LiveClaw	48.09	38.19	41.25	37.72
PinchBench	83.18	89.93	77.12	84.20
τ³-Bench Banking	8.04	7.63	4.54	7.01
WebArena Verified	26.75	37.62	25.62	28.69
GPQA Diamond	74.42	83.69	74.90	72.42
AA-Omniscience	-24.90	-24.35	-59.39	-3.58
HLE	7.40	7.69	8.57	5.65
IFEval	96.75	93.06	92.00	94.19
IFBench	74.69	64.21	47.44	71.67
Multi-IF	84.38	81.53	82.19	82.34
MATH-500	96.04	96.73	95.66	96.40
SWE-Bench Verified (OpenCode)	51.00	66.20	25.80	53.40
SciCode	32.71	37.35	35.42	27.78

Evaluation settings

All the models were evaluated with vLLM and the following generation parameters:

• AutomationBench: temperature = 0.6, top_p = 0.95, max_turns = 50, no max output tokens (bounded by the model's context window).
• BFCLv4: temperature = 0.001, max output tokens = 4096.
• ToolSandbox: temperature = 0, max output tokens = 1024; user simulator: GPT-4o-2024-08-06, temperature = 0, max output tokens = 1024.
• LiveClaw: temperature = 1.0 (vLLM default; the agent sets no temperature), max output tokens = 8192.
• τ³-Bench Banking: official user model (GPT-5.2, low reasoning effort) with default BM25 retrieval, temperature = 0, max output tokens = 16384 per turn.
• WebArena Verified: temperature = 0.6, top_p = 0.95, max_turns = 40 (number of browser actions allowed), no max output tokens (bounded by the model's context window).
• Other evals: temperature = 0.6, max output tokens = 32768.

Edge Inference

LFM2.5-24B-A2B is the fastest of the compared MoEs at reading in prompts and generating answers. It decodes 184 tokens/s on an Apple M5 Max and 114 tokens/s on an AMD Ryzen AI Max+ 395, up to 1.7x faster than the next-best model, while using the least memory.

Evaluation settings

Collected on Pipette, Liquid AI's device benchmarking infrastructure, with llama.cpp, Q4_K_M quantization, and a 4K-token input context.

GPU Inference

LFM2.5-24B-A2B is the fastest model in its size class, reaching over 21K output tokens per second at high concurrency.

Evaluation settings

We measure GPU throughput (total output tokens / wall time) on a single NVIDIA B300 GPU with SGLang 0.5.22, using 1,024 input tokens, up to 256 output tokens, and BF16 weights. At each concurrency level, we continuously maintain the target number of in-flight requests, replacing each completed request immediately.

📬 Contact

• Got questions or want to connect? Join our Discord community
• If you are interested in custom solutions with edge deployment, please contact our sales team.

Citation

@article{liquidAI202624BA2B,
  author  = {Liquid AI},
  title   = {LFM2.5-24B-A2B: Built for Fast On-Device Agents},
  journal = {Liquid AI Blog},
  year    = {2026},
  note    = {www.liquid.ai/blog/lfm2-5-24b-a2b},
}

@article{liquidai2025lfm2,
  title   = {LFM2 Technical Report},
  author  = {Liquid AI},
  journal = {arXiv preprint arXiv:2511.23404},
  year    = {2025}
}

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Previous working notes prior to release.

• Hats off to "LiquidAI" for another SOTA model, released Oct 13 2026.
• Primary build W Turbo-Brilliance-50X complete and working ; assessing performance in LFM2.5-24B-A2B core.
• In testing / benching / tuning and adjustments in progress... release pending (plus full GGUFs repo upload.)
• Final adjustments in progress; release pending.
• Final checkout complete, RELEASE PENDING.
• Part of this project: https://huggingface.co/DavidAU/Qwen3.8-27B-Turbo-Brilliance-50X-Reasoning-Instruct-NEO-MAX-MTP-GGUF

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