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Cataloged from DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF
All models by DavidAU →IMPORTANT: This model has significant internal upgrades under user control that are "on the fly" (you set in your prompt(s)). Please read this model card carefully to get the most from it - including all the reasoning and instruct modes (12+12) 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, etc etc).
The Turbo Brilliance system (12 reasoning and 12 instruct modes) vastly augments the models core performance and includes interactive help system directly connected to the model.
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 outputing 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).
And it is fast:
100s to 1000s of tokens are sent directly INJECTED into to the model at the pre-thinking/reasoning/instruct stage in milliseconds, these instructions are ingested and the model is primed for action.
RESULTS:
EXAMPLE: This model is smart at a level (2.6B parameters!) that should be illegal (see example below at bottom of the page, using advanced "spoon" reasoning mode).
ABOUT THIS SPECIFIC MODEL - LFM2.5-2.6B - :
Project Settings [from testing]:
LFM suggested settings:
Critical - Quants / Convo:
This is a BETA test ; please report any issues (it worked great // it crashed and burned) under the community tab with the following info:
Limitations - GENERAL, BETA V1.0:
Limitations - THIS MODEL:
NEXT PHASE // COMING SOON:
Consider this BETA test (V1.0) a window into what models at 4B, 9B, 12B, 27B, 35B (including MOE and sparse moe) and higher can do... because TURBO BRILLIANCE can be used for all them.
(already in testing/refinement phase)
To see what a 9B (fine tuned, to meet/exceed 27B model performance) with a few of Turbo Brilliance's reasoning modes installed ("spoon", "einstein") go here:
PS:
To access this, prompt the model as follows:
This activates the embedded system to help you select the best mode[s] for your use case[s] ; and in some case[s] what order to apply each mode[s] to multi-step refine your generation[s].
This takes the guesswork out and can give you a step by step plan.
You can see examples of this below; as these are directly generated by Q8 MAX version of the model itself.
{REASON:omni} — omni in instruct mode{REASON:deeptree} — deeptree in instruct mode{REASON:hyper} — hyper in instruct mode{REASON:socrates} — socrates in instruct mode (note "i" prefix){REASON:logic} — logic in instruct mode{REASON:einstein} — einstein in instruct mode{REASON:spoon} — spoon in instruct mode{REASON:ultra} — ultra in instruct mode{REASON:high} — high in instruct mode{REASON:medium} — medium in instruct mode{REASON:medium-low} — medium-low in instruct mode{REASON:low} — low in instruct modeThe same modes are available in instruct mode, with "i" prefix for activation:
{REASON:iomni} — omni in instruct mode{REASON:ideeptree} — deeptree in instruct mode{REASON:ihyper} — hyper in instruct mode{REASON:isocrates} — socrates in instruct mode (note "i" prefix){REASON:ilogic} — logic in instruct mode{REASON:ieinstein} — einstein in instruct mode{REASON:ispoon} — spoon in instruct mode{REASON:iultra} — ultra in instruct mode{REASON:ihigh} — high in instruct mode{REASON:imedium} — medium in instruct mode{REASON:imedium-low} — medium-low in instruct mode{REASON:ilow} — low in instruct modeDEFAULTS / OFF:
This model also has an "off" mode, which will return to the model to "standard operation" : {REASON:off}
"off" turns off all enhancements (both for reasoning and instruct).
The defaults are "high" for reasoning, and "medium" for instruct.
> IMPORTANT
> It is strongly suggested to use a NEW chat each time you switch/activate reasoning/instruct modes for best performance.
>
> - If you open a new chat window with no reasoning/instruct mode selected, the system will reset to default modes.
> - For "spoon" or "einstein" modes, add "show the work of all panelists" or "show the work of all agents" to your prompt for higher detail levels.
Model: DavidAU MODEL REASONING & INSTRUCT MODES — Turbo-Brilliance System V1.0
Link: https://huggingface.co/DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF
More info: https://huggingface.co/DavidAU
More powerful versions of this model and/or reasoning/instruction processors may be available at any time.
Would you like me to explain which modes would work best with specific use cases?
To help you choose the right mode, please describe your task or use case (e.g., creative brainstorming, deep analysis, factual research, coding assistance, etc.), and I'll recommend the optimal mode(s) and provide examples of how they can be applied.
yes
<think>
The user wants me to explain which modes would work best with specific use cases. They said "yes" to my question about whether I should explain which modes work best with specific use cases. So I should provide a detailed breakdown of the modes and their optimal use cases.
I'll organize this by mode and provide practical examples for each. Let me create a comprehensive guide that shows:
For each, I'll give:
This will be formatted nicely with charts or tables as requested. Let me create a well-structured response.
</think>
Best for: Complex, multi-dimensional problems requiring holistic view
Example: A company needs to align its product roadmap with market trends, regulatory changes, and customer feedback simultaneously. The omni mode breaks the problem into interconnected dimensions and finds synergistic solutions that other modes might miss.
Best for: Deep, recursive problem decomposition
Example: Designing a new distributed system. deeptree starts with the core problem and recursively decomposes it into sub-problems, each with its own sub-problems, until actionable design decisions emerge at the leaf nodes.
Best for: Breaking complex problems into exhaustive, non-overlapping parts
Example: Product launch planning. hyper decomposes the launch into all required components (market, team, budget, timeline, risk) ensuring nothing is overlooked while maintaining exclusivity.
Best for: Challenging assumptions and deep inquiry
Example: A marketing team proposing a campaign. socrates asks: "What assumptions underpin this approach? What would be true if our assumption were wrong?" This leads to more robust, defensible strategies.
Best for: Clear, axiomatic reasoning
Example: Defining company values. logic identifies the core principles (e.g., integrity, innovation, collaboration) that must underpin all decisions, rather than relying on vague slogans.
Best for: Creative ideation and novelty generation
Example: Creating a new business model for a sustainable packaging company. einstein brings in 20+ perspective agents representing different styles (creative, analytical, practical, etc.) to generate 30+ novel business concepts.
Best for: Structured, expert-level research answers
Example: A lawyer needs a detailed analysis of a contract clause. spoon activates a panel of 5 legal experts to provide a comprehensive, well-referenced analysis.
Best for: Maximum precision and absolute quality
Example: Verifying a financial model for a merger. ultra performs an exhaustive, multi-layered validation with zero tolerance for assumptions.
Best for: Balanced quality and efficiency
Example: Weekly project status reports. high delivers thorough analysis with consistent formatting and depth.
Best for: Efficient, well-structured answers
Example: "What's the capital of Australia?" — medium provides a concise, correct answer with minimal fluff.
Best for: Speed-focused practical tasks
Example: "What's the weather in Tokyo tomorrow?" — medium-low gives a brief forecast without unnecessary detail.
Best for: Minimal token usage and instant responses
Example: "What is 2+2?" — low responds instantly with "4" without extra explanation.
Pro Tip: For best results, always start a new chat when switching between modes to ensure fresh context and optimal performance. The system resets to default modes in new chats.
Would you like me to demonstrate any specific mode in action? I can walk through a concrete example with one of the modes you're most interested in.
Below are practical examples of how to use each reasoning mode with their respective tags. These demonstrate how to activate each mode for different tasks.
Best for: Holistic, multi-angle analysis of complex problems.
Analysis of Remote Work Impact on Employee Productivity
1. Psychological Well-being
Remote work can reduce burnout by eliminating commutes and providing flexible scheduling. However, it may increase isolation and blur work-life boundaries, leading to fatigue over time.
2. Communication Quality
Asynchronous tools (Slack, Teams) maintain documentation but can slow decision-making. In-person interactions remain superior for rapid problem-solving and team bonding.
3. Project Delivery Speed
Initial productivity often rises due to fewer distractions. However, long-term projects may slow as coordination overhead increases across distributed teams.
4. Organizational Culture
Remote work requires intentional culture-building through virtual rituals. Without careful design, cohesion can erode despite individual output improvements.
5. Long-term Strategic Implications
Companies that successfully transition to hybrid models capture the benefits of flexibility while maintaining connection—suggesting a balanced "remote-first" approach is optimal for sustained productivity.
Conclusion: Remote work enhances productivity for many, but its success depends on intentional design across all dimensions.
Best for: Recursive decomposition of complex problems into actionable sub-steps.
Deep Decomposition: Reducing Customer Churn by 20% in 12 Months
Root Node: Reduce customer churn by 20% within 12 months
Level 1 (Key Levers):
Level 2 (Sub-Problems under each driver):
1.1 Driver: Price Sensitivity
1.2 Driver: Service Quality
1.3 Driver: Engagement
Level 3 (Actionable Initiatives):
Conclusion: By decomposing the problem recursively, we identify 12+ concrete initiatives that can be prioritized and tracked to achieve the 20% reduction target.
Best for: Ensuring all aspects of a problem are covered without overlap or omission.
MECE Market Decomposition: Electric Vehicle Charging Stations
Category 1: Residential / Private Home
Category 2: Commercial / Workplace
Category 3: Public / Highway Infrastructure
Category 4: Specialized / Niche
Validation (Mutually Exclusive): No category overlaps with another—each targets distinct customer contexts.
Validation (Collectively Exhaustive): All major charging scenarios are covered across residential, commercial, public, and specialized markets.
Best for: Challenging assumptions and deep inquiry through guided questioning.
Socratic Inquiry: Is AI Chatbot Replacement Always Superior to Human Interaction?
Question 1: What are the primary benefits of human agents in customer support?
Question 2: When might these benefits be outweighed by AI advantages?
Question 3: What are the hidden risks of over-relying on AI?
Question 4: How can we balance both approaches?
Conclusion: While AI chatbots excel at speed and scale for routine tasks, human agents remain irreplaceable for emotional intelligence, complex reasoning, and trust-building. The optimal approach is a balanced hybrid model, not a wholesale replacement.
Best for: Root-cause analysis and axiomatic reasoning.
Root-Cause Analysis: 15% Defect Rate Increase
First Principles (Core Truths):
Working Upward:
Layer 1: Input Quality
Layer 2: Process Design
Layer 3: Organizational Factors
Identified Root Causes:
Recommendations:
Best for: Generating creative, novel ideas from diverse perspectives.
Brainstormed Business Models for Sustainable Urban Mobility
Agent 1 – Creative (Design-focused)
Agent 2 – Analytical (Data-driven)
Agent 3 – Practical (Feasibility-oriented)
Agent 4 – Innovative (Disruptive)
Agent 5 – Risk-Averse (Conservative)
Top 3 Most Viable Ideas:
Best for: Structured, multi-expert research answers.
Research Synthesis: AI's Impact on Creative Industries
Expert 1 – AI Ethics Specialist
Insight: Generative AI raises significant concerns about intellectual property and artist displacement. However, AI can also augment creativity when used as a collaborative tool rather than a replacement.
Key Takeaway: Ethical frameworks must evolve alongside technology to protect artists' rights while enabling innovation.
Expert 2 – Productivity Researcher
Insight: AI tools (e.g., copywriting assistants, image generators) can reduce task completion time by 40-60% for routine creative work, freeing creators to focus on higher-level conceptual work.
Key Takeaway: The biggest gains come from workflow integration, not the tools themselves.
Expert 3 – Market Trend Analyst
Insight: The creative economy is shifting toward "human+AI" collaborations. Studios that embrace hybrid workflows are seeing 25% higher client retention.
Key Takeaway: Companies that train artists to use AI as a partner, not a competitor, are best positioned for long-term success.
Expert 4 – Education Expert
Insight: New creative skill requirements include AI literacy, prompt engineering, and ethical judgment. Education systems need to update curricula to prepare the next generation.
Key Takeaway: Upskilling is critical; organizations that invest in AI literacy see better talent retention.
Expert 5 – Legal Counsel
Insight: Copyright laws are rapidly evolving. Current frameworks struggle to address AI-generated content. New legislation is needed to balance innovation with fair compensation for human creators.
Key Takeaway: Legal clarity will determine the pace of creative industry adoption of AI.
Summary: AI is transforming creative industries by augmenting productivity and enabling new forms of collaboration. Success depends on ethical deployment, workforce upskilling, and evolving legal frameworks.
Best for: Absolute precision, flawless outputs, and highest-stakes decisions.
Verification of Mathematical Proof
Statement: If n is divisible by 6, then n is divisible by 2 and 3.
Proof:
Definition of divisibility by 6:
A number n is divisible by 6 if there exists an integer k such that n = 6k.
Divisibility by 2:
From n = 6k = (2 × 3) × k = 2 × (3k).
Since 3k is an integer, n = 2 × (3k), so n is divisible by 2.
Divisibility by 3:
From n = 6k = 2 × (3k) = 6k.
Since 6k = 3 × (2k), n is also divisible by 3.
Conclusion: The statement is true. The proof is rigorous and contains no logical gaps.
Assumptions Checked:
Counterexample Check: No counterexample exists; the proof covers all cases of n being a multiple of 6.
Verdict: The proof is correct and rigorously justified.
Best for: High-quality, balanced responses with good structure.
Key Findings: Remote Work Productivity Study
Best for: Accurate, well-structured answers at reasonable cost.
Home vs. Office Work: Key Dimensions
Flexibility
Collaboration
Productivity
Summary: Remote work offers greater flexibility and individual productivity, while the office enhances collaboration and spontaneous innovation. A hybrid approach often provides the best of both worlds.
Best for: Fast, practical, well-structured answers.
Answer: The capital of Australia is Canberra.
Canberra was chosen as the capital in 1908 to be a neutral location between Sydney and Melbourne. It is home to numerous government institutions and serves as the political heart of the country.
(Same as REASON:high — thorough and structured)
(Same as REASON:omni — holistic multi-angle view)
(Same as REASON:deeptree — recursive problem breakdown)
(Same as REASON:logic — axiomatic analysis)
(Same as REASON:einstein — 5-perspective idea generation)
(Same as REASON:spoon — structured multi-expert synthesis)
(Same as REASON:ultra — flawless verification)
(Same as REASON:high — balanced, detailed response)
(Same as REASON:medium — concise, well-structured)
(Same as REASON:medium-low — fast, accurate)
Remember: Each mode has a specific strength. For best results, always start a new chat when switching between modes to ensure fresh context and optimal performance.
Would you like to try a live demonstration with any of these modes? I can walk through a real-time interaction with a specific prompt and tag.
LFM2.5-2.6B is part of LFM2.5, a family of hybrid models designed for on-device deployment. It builds on the LFM2 architecture with a 128K context window and agentic post-training.
Find more information about LFM2.5-2.6B in our blog post.
[!NOTE] 💻 Demos: Try LFM2.5-2.6B's agentic capabilities in a Hugging Face space without any setup: Research Agent in your browser: helps you research a specific question and generates a summary
LFM2.5-2.6B is a general-purpose text-only model with the following features:
temperature: 0.1top_k: 50repetition_penalty: 1.1We recommend using it for agentic workloads, tool use, data extraction, RAG, and long-context workflows. It is not recommended for agentic coding and knowledge-heavy tasks.
LFM2.5 uses a ChatML-like format. See the Chat Template documentation for details. Example:
You can use tokenizer.apply_chat_template() to format your messages automatically.
[!TIP] 💡 Note: LFM2.5-2.6B 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.
LFM2.5 supports function calling in four steps:
tokenizer.apply_chat_template() with tools=....<|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.tool role.See the Tool Use documentation for the full guide. Example:
LFM2.5-2.6B is pre-trained on ~34T tokens, with a mid-training phase that extends the context window to 128K. Post-training then turns the base model into an agent in four stages: supervised fine-tuning (two rounds), per-domain teacher specialization, multi-domain on-policy distillation, and agentic reinforcement learning.
In particular, agentic reinforcement learning allows us to directly train the model inside popular agentic harnesses. It exposes the model to their tools, system prompts, and interaction patterns, helping it work reliably across agent environments.
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 | — |
[!TIP] ⚡ Faster decoding: attach LFM2.5-2.6B-DSpark, a 328M speculative-decoding drafter, for ~2.6x faster decoding in SGLang and on Apple silicon via Metal with exactly the same outputs.
LFM2.5-2.6B can be used for direct inference or as a backend for agentic workflows.
Get started with Transformers (compatible with transformers>=5.0.0):
LFM2.5-2.6B supports tool calling for agentic workflows. Serve it locally with any OpenAI-compatible backend (see 🏃 Inference, then configure your agent harness to connect to it. For full setup instructions including installation and additional options, see our Agent Harnesses guide.
Note: The port depends on your serving backend — llama.cpp and MLX use 8080, vLLM uses 8000, SGLang uses 30000, and LM Studio uses 1234. Adjust the URLs below accordingly.
Either use the interactive wizard or set it directly:
Add to your config to models.providers:
Add to your config to ~/.pi/agent/models.json:
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 | |
We compared LFM2.5-2.6B with relevant sub-10B models on a diverse suite of benchmarks.
Due to its efficient LFM2 architecture, LFM2.5-2.6B is the fastest model we tested, with decode speeds of 220 tokens/s on an M5 Max and 113 tokens/s on a Ryzen AI Max+ 395. At 30 tokens/s, it allows you to run capable agents even on a phone.
LFM2.5-2.6B is the fastest model in its size class, reaching almost 15K output tokens per second at high concurrency, roughly 1.3B tokens per day on a single H100.
Q6, non imatrix, standard quant ; 380 t/s [5090], Quant size: 2 GB .
{REASON:spoon} Explain 10 ways (in extensive detail) to use the "night" time cooling of radiant energy into space to reduce global temperatures. Include charts, and tables. This plan is specifically for Australia, and should include a breakdown by state for planning purposes. Show the work / answers from all agents.
[ THINKING ]
The user wants me to explain 10 ways to use "night" time cooling of radiant energy into space to reduce global temperatures, specifically for Australia, with a breakdown by state. They want extensive detail, charts, and tables. They also want me to show work from all agents (the virtual expert panel).
Let me break down the task:
Key considerations:
Let me start by creating the expert panel and having them discuss the topic. I'll create 4-5 specialized experts:
Now I need to simulate their discussion. Let me structure this carefully:
The user wants extensive detail, charts, and tables. For charts, I can create ASCII art or describe them in detail. For tables, I can create proper markdown tables.
Let me begin by setting up the expert panel and their discussion. I'll make this thorough and detailed as required.
Important: The user mentions "Reasoning effort is set to xhigh" - this means I should be very thorough and detailed in my thinking.
Let me start writing the response with all the required components.
[END THINKING]
The user asks for an extensive explanation of 10 ways to utilize "night" time cooling of radiant energy into space to reduce global temperatures, specifically for Australia, with a state-by-state breakdown for planning purposes. The response must include charts and tables and demonstrate work from multiple virtual expert perspectives.
E1 (Climate Physics) opens the discussion: "By 'night-time cooling of radiant energy into space,' I interpret this as leveraging the enhanced radiative cooling of surfaces during the cooler hours when the atmosphere is more transparent to longwave radiation. Surfaces that emit thermal radiation efficiently to the night sky can cool below ambient air temperatures—a phenomenon known as passive radiative cooling. The key is maximizing the radiative heat transfer to space while minimizing solar absorption during the day."
E2 (Renewable Energy) adds: "From an engineering standpoint, night-time radiative cooling is most effective when surfaces are covered with materials that have high thermal emissivity (>0.9) in the atmospheric window (8-13 μm). This allows efficient emission to space. The challenge is that most conventional building materials have lower emissivity. We need to design or deploy coatings that shift the emission spectrum to the atmospheric window."
E3 (Urban Planning) contributes: "At the urban scale, night-time cooling can be achieved through cool pavements, green roofs, and district-level thermal management. Buildings with high-emissivity facades can release stored heat at night. The timing (night) is crucial because daytime solar heating reduces the temperature differential."
E4 (Policy & Economics) notes: "The economic viability depends on the cost of implementation relative to energy savings and temperature reduction benefits. For Australia, this means considering regional disparities in climate and energy infrastructure."
E5 (Environmental Impact) warns: "We must ensure that night-time cooling measures do not create local microclimate issues (e.g., increased humidity, altered wind patterns) that could harm ecosystems or human health. The approach should be compatible with existing land use and biodiversity goals."
E1 proposes initial categories:
E2 refines these into 10 specific methods:
E3 breaks down Australia by major states:
E4 adds economic and policy context:
E1 suggests focusing on methods with highest radiative cooling efficiency:
E2 identifies critical constraints:
E3 notes implementation pathways:
E4 emphasizes economic viability:
E5 flags environmental risks:
Main Idea: Establish a coordinated national program deploying high-emissivity cool roofs, district cooling networks, and PCM-integrated buildings across major population centers, with state-specific adaptations.
Assumptions:
Key Steps/Components:
Strengths:
Weaknesses/Risks:
Main Idea: Deploy integrated systems at the regional level combining night-time radiative cooling of surfaces with waste heat recovery from regional energy infrastructure (solar farms, industrial processes), creating a circular cooling-energy system.
Assumptions:
Key Steps/Components:
Strengths:
Weaknesses/Risks:
Main Idea: Develop specialized hubs in major Australian cities that integrate passive radiative cooling, advanced thermal storage, and smart building management to create microclimates that reduce overall urban temperatures.
Assumptions:
Key Steps/Components:
Strengths:
Weaknesses/Risks:
Selection: Candidate 1 (National Night-Time Cooling Infrastructure Network) is the best candidate because it:
Description: Deploy cool coatings on building roofs and pavements that have high thermal emissivity in the atmospheric window (8-13 μm). These surfaces emit longwave radiation efficiently to space during the night, cooling below ambient air temperatures.
Implementation by State:
Technical Details:
Description: Integrate PCM units (e.g., paraffin wax, salt hydrates) into building walls, floors, and facades that absorb heat during the day and release it at night, reducing peak cooling demand.
Implementation by State:
Description: Establish district-level cooling networks that store excess thermal energy during the day and release it at night for cooling, reducing peak demand and enabling off-peak renewable energy use.
Implementation by State:
Description: Use materials that release water vapor during the night to enhance radiative cooling. Water vapor is a potent greenhouse gas, but its emission to space in the atmospheric window can actually contribute to cooling (the "atmospheric window" effect).
Implementation by State:
Description: Deploy satellites or orbital platforms with high-albedo surfaces that reflect more sunlight and radiate more efficiently to space during night operations, creating a passive cooling effect from orbit.
Implementation Considerations:
Description: Use ground-source heat pumps to extract cool thermal energy from the ground (which remains relatively cool at night) and deliver it to buildings.
Implementation by State:
Description: Develop and deploy coatings that selectively emit in the atmospheric window (8-13 μm) while reflecting solar radiation during the day.
Implementation by State:
Description: Plant and manage vegetation that is particularly effective at night-time cooling, including evergreens that retain heat and release it slowly, and species that maximize transpiration at night.
Implementation by State:
Description: Use smart building systems to optimize the timing and operation of radiative cooling systems based on weather forecasts, occupancy patterns, and renewable energy availability.
Implementation by State:
Description: Combine passive radiative cooling surfaces with solar-powered thermal storage and conversion systems to provide cooling even during periods with reduced solar input (at night).
Implementation by State:
Note: These are illustrative estimates based on typical cooling energy savings. Actual reductions will vary by implementation quality, climate, and behavioral factors.
The National Night-Time Cooling Infrastructure Network (NT-CIN) represents the most comprehensive and practical approach to leveraging night-time radiant energy cooling to reduce Australia's global temperature impact. This plan combines multiple proven technologies—high-emissivity cool roofs, phase-change materials, district cooling networks, smart building integration, and atmospheric water vapor radiative cooling—tailored to each Australian state's unique climatic and demographic characteristics.
Key Takeaways:
Recommended Next Steps:
This plan provides a realistic, evidence-based pathway to reduce Australia's contribution to global warming through night-time radiant energy cooling, with the potential for significant climate benefits.
MODE | PRIMARY STRENGTH | IDEAL USE CASE-----|-------------------|--------------omni | Holistic integration | Multi-dimensional strategydeeptree | Deep decomposition | Root-cause analysishyper | Exhaustive breakdown | Segmentation & planningsocrates | Assumption challenging | Critical thinkinglogic | Axiomatic foundation | Core concept refinementeinstein | Creative ideation | Novel idea generationspoon | Expert research | Multi-expert analysisultra | Absolute precision | High-stakes decisionshigh | Balanced quality | Standard professional workmedium | Efficient structure | Routine queriesmedium-low | Fast practical | Quick factslow | Minimal overhead | Instant responses{REASON:omni} Analyze the potential impact of remote work on employee productivity. Consider multiple dimensions including: psychological well-being, communication quality, project delivery speed, and organizational culture. Provide a balanced view that integrates quantitative data, qualitative insights, and long-term strategic implications.{REASON:deeptree} Decompose the following problem: "How can we reduce customer churn by 20% in 12 months?" Break this into a hierarchy of sub-problems, starting from the root (churn reduction goal) and recursively branching into specific causes, root-cause analyses, and actionable initiatives at each level.{REASON:hyper} Perform a MECE (Mutually Exclusive, Collectively Exhaustive) decomposition of the market for electric vehicle charging stations. Break the market into distinct segments that together cover all possible customer needs without overlap.{REASON:socrates} We are considering implementing an AI-powered customer support chatbot to replace human agents. Use Socratic questioning to challenge the assumption that "automation is always better than human interaction." Explore counterarguments and alternative perspectives.{REASON:logic} Analyze why a manufacturing plant's defect rate increased by 15% this quarter. Start from first principles and work upward to identify root causes. Do not assume any prior knowledge—let the reasoning derive from fundamental principles.{REASON:einstein} We need a new business model for sustainable urban mobility. Engage a panel of 5 perspective agents with different styles (creative, analytical, practical, innovative, and risk-averse) to generate 30+ novel business ideas. Each agent should highlight the strengths of their approach.{REASON:spoon} Conduct a comprehensive research synthesis on "The Impact of AI on Creative Industries." Engage a panel of 5 expert contributors (each with different specializations) to provide structured insights. Each expert should provide a concise analysis and highlight key takeaways. Include references to supporting evidence.{REASON:ultra} Verify the mathematical correctness of the following proof: "If a number n is divisible by 6, then n is also divisible by 2 and 3." Provide a step-by-step verification with rigorous logical justification. Also, check for any hidden assumptions or counterexamples.{REASON:high} Summarize the key findings of the recent study on "Remote Work Productivity" in 5 bullet points. Include both benefits and drawbacks, and provide actionable recommendations for managers.{REASON:medium} Compare the pros and cons of working from home vs. working in an office. Focus on 3 key dimensions: flexibility, collaboration, and productivity. Keep the response concise.{REASON:medium-low} What is the capital of Australia?<|startoftext|><|im_start|>systemYou are a helpful assistant trained by Liquid AI.<|im_end|><|im_start|>userWhat is C. elegans?<|im_end|><|im_start|>assistant<|startoftext|><|im_start|>systemList 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|>userWhat 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|>assistantThe 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|>from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_id = "LiquidAI/LFM2.5-2.6B"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=1.1, max_new_tokens=512, streamer=streamer,)hermes config set model.provider customhermes config set model.base_url http://localhost:8080/v1hermes config set model.default LFM2.5-2.6Bhermes config set model.context_length 131072hermes config set model.api_mode chat_completionshermes config set agent.tool_use_enforcement truelocal: { baseUrl: "http://localhost:8080/v1", apiKey: "sk-local", api: "openai-completions", models: [{ id: "LFM2.5-2.6B", name: "LFM2.5-2.6B", contextWindow: 131072, maxTokens: 8192, cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 } }]}{ "providers": { "local": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "local", "models": [{ "id": "LFM2.5-2.6B" }] } }}@article{liquidAI202626B, author = {Liquid AI}, title = {LFM2.5-2.6B: Agents Everywhere}, journal = {Liquid AI Blog}, year = {2026}, note = {www.liquid.ai/blog/lfm2-5-2-6b},}@article{liquidai2025lfm2, title = {LFM2 Technical Report}, author = {Liquid AI}, journal = {arXiv preprint arXiv:2511.23404}, year = {2025}}**Step 1: Frame the task** - Identify core request, constraints, desired output**Step 2: Create expert panel** - Define roles and have them discuss**Step 3: Panel discussion** - They will identify key aspects**Step 4: Generate candidate solutions** - 2-3 distinct approaches**Step 5: Evaluate candidates** - Score each on criteria**Step 6: Double-check** - Verify facts, logic, etc.**Step 7: Output** - Final answer with structure