Todays AI Summary

AI Developments: Uncensored Captioning, Distilled Reasoning, and More

This week's AI landscape features advancements in areas ranging from image captioning to reinforcement learning and medical imaging. Here's a look at the most interesting developments:

Research Highlights

Model Releases

  • Qwen2-VL-2B-Abliterated-Caption-it: This model (3 likes) is a fine-tuned version of Qwen2-VL-2B-Instruct, designed for uncensored image captioning. It generates detailed captions across diverse visual categories, bypassing common content filters.
  • Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-V2-FP32: This model (3 likes) is a distilled version of Qwen3-30B-A3B-Thinking, designed to inherit the reasoning and behavioral characteristics of DeepSeek-V3.1. It was created using a layer-by-layer distillation SVD based distillation process.

Key Takeaways

  • Ethical AI: The Bridge2AI paper highlights the growing importance of ethical considerations and data quality in AI development, particularly in sensitive domains like biomedicine.
  • Diagnostic AI: The RL research demonstrates the potential for AI to not only perform tasks but also diagnose its own failures, paving the way for more robust and reliable systems.
  • Model Distillation: The release of Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-V2-FP32 showcases the power of distillation techniques to transfer knowledge from large models to smaller, more efficient ones.
  • Uncensored AI: The Qwen2-VL-2B-Abliterated-Caption-it model represents a growing trend towards models with reduced content filtering, raising important questions

AI Papers for 2026-03-27

The Stochastic Gap: A Markovian Framework for Pre-Deployment Reliability and Oversight-Cost Auditing in Agentic Artificial Intelligence

Agentic artificial intelligence (AI) in organizations is a sequential decision problem constrained by reliability and oversight cost. When deterministic workflows are replaced by stochastic policies over actions and tool calls, the key question is not whether a next step appears plausible, but whether the resulting trajectory remains statistically supported, locally unambiguous, and economically governable. We develop a measure-theoretic Markov framework for this setting. The core quantities are state blind-spot mass B_n(tau), state-action blind mass B^SA_{pi,n}(tau), an entropy-based human-in-the-loop escalation gate, and an expected oversight-cost identity over the workflow visitation measure. We instantiate the framework on the Business Process Intelligence Challenge 2019 purchase-to-pay log (251,734 cases, 1,595,923 events, 42 distinct workflow actions) and construct a log-driven simulated agent from a chronological 80/20 split of the same process. The main empirical finding is that a large workflow can appear well supported at the state level while retaining substantial blind mass over next-step decisions: refining the operational state to include case context, economic magnitude, and actor class expands the state space from 42 to 668 and raises state-action blind mass from 0.0165 at tau=50 to 0.1253 at tau=1000. On the held-out split, m(s) = max_a pi-hat(a|s) tracks realized autonomous step accuracy within 3.4 percentage points on average. The same quantities that delimit statistically credible autonomy also determine expected oversight burden. The framework is demonstrated on a large-scale enterprise procurement workflow and is designed for direct application to engineering processes for which operational event logs are available.

Retrieval Improvements Do Not Guarantee Better Answers: A Study of RAG for AI Policy QA

Retrieval-augmented generation (RAG) systems are increasingly used to analyze complex policy documents, but achieving sufficient reliability for expert usage remains challenging in domains characterized by dense legal language and evolving, overlapping regulatory frameworks. We study the application of RAG to AI governance and policy analysis using the AI Governance and Regulatory Archive (AGORA) corpus, a curated collection of 947 AI policy documents. Our system combines a ColBERT-based retriever fine-tuned with contrastive learning and a generator aligned to human preferences using Direct Preference Optimization (DPO). We construct synthetic queries and collect pairwise preferences to adapt the system to the policy domain. Through experiments evaluating retrieval quality, answer relevance, and faithfulness, we find that domain-specific fine-tuning improves retrieval metrics but does not consistently improve end-to-end question answering performance. In some cases, stronger retrieval counterintuitively leads to more confident hallucinations when relevant documents are absent from the corpus. These results highlight a key concern for those building policy-focused RAG systems: improvements to individual components do not necessarily translate to more reliable answers. Our findings provide practical insights for designing grounded question-answering systems over dynamic regulatory corpora.

EndoVGGT: GNN-Enhanced Depth Estimation for Surgical 3D Reconstruction

Accurate 3D reconstruction of deformable soft tissues is essential for surgical robotic perception. However, low-texture surfaces, specular highlights, and instrument occlusions often fragment geometric continuity, posing a challenge for existing fixed-topology approaches. To address this, we propose EndoVGGT, a geometry-centric framework equipped with a Deformation-aware Graph Attention (DeGAT) module. Rather than using static spatial neighborhoods, DeGAT dynamically constructs feature-space semantic graphs to capture long-range correlations among coherent tissue regions. This enables robust propagation of structural cues across occlusions, enforcing global consistency and improving non-rigid deformation recovery. Extensive experiments on SCARED show that our method significantly improves fidelity, increasing PSNR by 24.6% and SSIM by 9.1% over prior state-of-the-art. Crucially, EndoVGGT exhibits strong zero-shot cross-dataset generalization to the unseen SCARED and EndoNeRF domains, confirming that DeGAT learns domain-agnostic geometric priors. These results highlight the efficacy of dynamic feature-space modeling for consistent surgical 3D reconstruction.

Chameleon: Episodic Memory for Long-Horizon Robotic Manipulation

Robotic manipulation often requires memory: occlusion and state changes can make decision-time observations perceptually aliased, making action selection non-Markovian at the observation level because the same observation may arise from different interaction histories. Most embodied agents implement memory via semantically compressed traces and similarity-based retrieval, which discards disambiguating fine-grained perceptual cues and can return perceptually similar but decision-irrelevant episodes. Inspired by human episodic memory, we propose Chameleon, which writes geometry-grounded multimodal tokens to preserve disambiguating context and produces goal-directed recall through a differentiable memory stack. We also introduce Camo-Dataset, a real-robot UR5e dataset spanning episodic recall, spatial tracking, and sequential manipulation under perceptual aliasing. Across tasks, Chameleon consistently improves decision reliability and long-horizon control over strong baselines in perceptually confusable settings.

VFIG: Vectorizing Complex Figures in SVG with Vision-Language Models

Scalable Vector Graphics (SVG) are an essential format for technical illustration and digital design, offering precise resolution independence and flexible semantic editability. In practice, however, original vector source files are frequently lost or inaccessible, leaving only "flat" rasterized versions (e.g., PNG or JPEG) that are difficult to modify or scale. Manually reconstructing these figures is a prohibitively labor-intensive process, requiring specialized expertise to recover the original geometric intent. To bridge this gap, we propose VFIG, a family of Vision-Language Models trained for complex and high-fidelity figure-to-SVG conversion. While this task is inherently data-driven, existing datasets are typically small-scale and lack the complexity of professional diagrams. We address this by introducing VFIG-DATA, a large-scale dataset of 66K high-quality figure-SVG pairs, curated from a diverse mix of real-world paper figures and procedurally generated diagrams. Recognizing that SVGs are composed of recurring primitives and hierarchical local structures, we introduce a coarse-to-fine training curriculum that begins with supervised fine-tuning (SFT) to learn atomic primitives and transitions to reinforcement learning (RL) refinement to optimize global diagram fidelity, layout consistency, and topological edge cases. Finally, we introduce VFIG-BENCH, a comprehensive evaluation suite with novel metrics designed to measure the structural integrity of complex figures. VFIG achieves state-of-the-art performance among open-source models and performs on par with GPT-5.2, achieving a VLM-Judge score of 0.829 on VFIG-BENCH.

Completeness of Unbounded Best-First Minimax and Descent Minimax

In this article, we focus on search algorithms for two-player perfect information games, whose objective is to determine the best possible strategy, and ideally a winning strategy. Unfortunately, some search algorithms for games in the literature are not able to always determine a winning strategy, even with an infinite search time. This is the case, for example, of the following algorithms: Unbounded Best-First Minimax and Descent Minimax, which are core algorithms in state-of-the-art knowledge-free reinforcement learning. They were then improved with the so-called completion technique. However, whether this technique sufficiently improves these algorithms to allow them to always determine a winning strategy remained an open question until now. To answer this question, we generalize the two algorithms (their versions using the completion technique), and we show that any algorithm of this class of algorithms computes the best strategy. Finally, we experimentally show that the completion technique improves winning performance.

Anti-I2V: Safeguarding your photos from malicious image-to-video generation

Advances in diffusion-based video generation models, while significantly improving human animation, poses threats of misuse through the creation of fake videos from a specific person's photo and text prompts. Recent efforts have focused on adversarial attacks that introduce crafted perturbations to protect images from diffusion-based models. However, most existing approaches target image generation, while relatively few explicitly address image-to-video diffusion models (VDMs), and most primarily focus on UNet-based architectures. Hence, their effectiveness against Diffusion Transformer (DiT) models remains largely under-explored, as these models demonstrate improved feature retention, and stronger temporal consistency due to larger capacity and advanced attention mechanisms. In this work, we introduce Anti-I2V, a novel defense against malicious human image-to-video generation, applicable across diverse diffusion backbones. Instead of restricting noise updates to the RGB space, Anti-I2V operates in both the $L$*$a$*$b$* and frequency domains, improving robustness and concentrating on salient pixels. We then identify the network layers that capture the most distinct semantic features during the denoising process to design appropriate training objectives that maximize degradation of temporal coherence and generation fidelity. Through extensive validation, Anti-I2V demonstrates state-of-the-art defense performance against diverse video diffusion models, offering an effective solution to the problem.

The Free-Market Algorithm: Self-Organizing Optimization for Open-Ended Complex Systems

We introduce the Free-Market Algorithm (FMA), a novel metaheuristic inspired by free-market economics. Unlike Genetic Algorithms, Particle Swarm Optimization, and Simulated Annealing -- which require prescribed fitness functions and fixed search spaces -- FMA uses distributed supply-and-demand dynamics where fitness is emergent, the search space is open-ended, and solutions take the form of hierarchical pathway networks. Autonomous agents discover rules, trade goods, open and close firms, and compete for demand with no centralized controller. FMA operates through a three-layer architecture: a universal market mechanism (supply, demand, competition, selection), pluggable domain-specific behavioral rules, and domain-specific observation. The market mechanism is identical across applications; only the behavioral rules change. Validated in two unrelated domains. In prebiotic chemistry, starting from 900 bare atoms (C, H, O, N), FMA discovers all 12 feasible amino acid formulas, all 5 nucleobases, the formose sugar chain, and Krebs cycle intermediates in under 5 minutes on a laptop -- with up to 240 independent synthesis routes per product. In macroeconomic forecasting, reading a single input-output table with zero estimated parameters, FMA achieves Mean Absolute Error of 0.42 percentage points for non-crisis GDP prediction, comparable to professional forecasters, portable to 33 countries. Assembly Theory alignment shows that FMA provides the first explicit, tunable mechanism for the selection signatures described by Sharma et al. (Nature, 2023). The event-driven assembly dynamics resonate with foundational programs in physics -- causal set theory, relational quantum mechanics, constructor theory -- suggesting that Darwinian market dynamics may reflect a deeper organizational principle that lead to the unfolding of Nature itself.

LensWalk: Agentic Video Understanding by Planning How You See in Videos

The dense, temporal nature of video presents a profound challenge for automated analysis. Despite the use of powerful Vision-Language Models, prevailing methods for video understanding are limited by the inherent disconnect between reasoning and perception: they rely on static, pre-processed information and cannot actively seek raw evidence from video as their understanding evolves. To address this, we introduce LensWalk, a flexible agentic framework that empowers a Large Language Model reasoner to control its own visual observation actively. LensWalk establishes a tight reason-plan-observe loop where the agent dynamically specifies, at each step, the temporal scope and sampling density of the video it observes. Using a suite of versatile, Vision-Language Model based tools parameterized by these specifications, the agent can perform broad scans for cues, focus on specific segments for fact extraction, and stitch evidence from multiple moments for holistic verification. This design allows for progressive, on-demand evidence gathering that directly serves the agent's evolving chain of thought. Without requiring any model fine-tuning, LensWalk delivers substantial, plug-and-play performance gains on multiple model recipes, boosting their accuracy by over 5\% on challenging long-video benchmarks like LVBench and Video-MME. Our analysis reveals that enabling an agent to control how it sees is key to unlocking more accurate, robust, and interpretable video reasoning.

Evaluating Chunking Strategies For Retrieval-Augmented Generation in Oil and Gas Enterprise Documents

Retrieval-Augmented Generation (RAG) has emerged as a framework to address the constraints of Large Language Models (LLMs). Yet, its effectiveness fundamentally hinges on document chunking - an often-overlooked determinant of its quality. This paper presents an empirical study quantifying performance differences across four chunking strategies: fixed-size sliding window, recursive, breakpoint-based semantic, and structure-aware. We evaluated these methods using a proprietary corpus of oil and gas enterprise documents, including text-heavy manuals, table-heavy specifications, and piping and instrumentation diagrams (P and IDs). Our findings show that structure-aware chunking yields higher overall retrieval effectiveness, particularly in top-K metrics, and incurs significantly lower computational costs than semantic or baseline strategies. Crucially, all four methods demonstrated limited effectiveness on P and IDs, underscoring a core limitation of purely text-based RAG within visually and spatially encoded documents. We conclude that while explicit structure preservation is essential for specialised domains, future work must integrate multimodal models to overcome current limitations.

AI Models

LuffyTheFox/Qwen3.5-27B-Claude-4.6-Opus-Uncensored-V2-Kullback-Leibler-GGUF

language:

  • en
  • zh
  • ko license: apache-2.0 base_model: Qwen/Qwen3.5-27B tags:
  • unsloth
  • qwen
  • qwen3.5
  • reasoning
  • chain-of-thought
  • lora
  • uncensored
  • not-for-all-audiences pipeline_tag: image-text-to-text datasets:
  • nohurry/Opus-4.6-Reasoning-3000x-filtered
  • Jackrong/Qwen3.5-reasoning-700x
  • Roman1111111/claude-opus-4.6-10000x

🌟 This is Qwen3.5-27B-Claude-4.6-Opus-Uncensored-V2-Kullback-Leibler-GGUF model made via delta merge for Jackrong model and HauhauCS model

After merging I fixed attn_v and ffn_gate_exps tensors via Kullback-Leibler and Decision_Tree algorythms for 28 tensors in internal GGUF structure. Models with this adjustment have "-KL" suffix.

Result after fixing 28 broken spots inside the GGUF format, the model became 75% more correct internally.

After KL correction:

Parametric KL: 1.14 → 0.28 (75.6% reduction)

attn_v and ffn_gate_exps restored

Now holds 262K context

Reasons like Claude Opus 4.6

Does not require additional training.

🌟 For best model perfomance use following settings in LM Studio:

Temperature: 0.7

Top K Sampling: 20

Presence Penalty: 1.5

Top P Sampling: 0.8

Min P Sampling: 0

Seed: 3407 or 42

And this system prompt. It's pretty solid: https://pastebin.com/pU25DVnB

Also you can use only this string in System Prompt:

You are Qwen, created by Alibaba Cloud. You are a helpful AI assistant.

And write anything you want after that. Looks like model is underperforming without this first line.

📢 Announcement

v2 Update:

  • Accuracy preserved: Matches base model on HumanEval (96.91% pass@1)

  • Shorter reasoning: ~24% reduction in chain-of-thought length

  • Higher efficiency: +31.6% more correct solutions per token

  • ⚠️Trade-off: −1.24% on HumanEval+ −7.2% on MMLU-Pro (Indicating reduced general knowledge reasoning performance)

⚠️Note: Due to the scope of SFT data and training focus, the model may underperform the base model on certain tasks requiring long-context understanding or more complex multi-step reasoning. The efficiency and accuracy results reported here are based solely on the HumanEval and HumanEval+ benchmarks. Thank you for your understanding.

HCaJnUQaoAAaMIc

💡 Model Introduction

Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2 is the second iteration of this reasoning-focused Qwen3.5-27B fine-tune, built to drastically improve the efficiency of chain-of-thought generation, unlocking highly substantial gains in reasoning speed and cost-reduction while actually increasing absolute accuracy.

Compared with the earlier version, v2 was trained with 14,000 Claude 4.6 Opus-style general reasoning samples, with a stronger emphasis on transferring concise, reusable reasoning patterns rather than only maximizing raw benchmark scores. The goal of v2 is not simply to make the model "think more," but to help it think more economically: reducing unnecessarily long internal chains, avoiding verbose over-analysis on easy problems, and massively improving the reasoning-cost-to-quality ratio while beating the baseline's benchmark correctness.

A key design choice in v2 is that the distillation data is primarily general-domain reasoning data—specifically focused on mathematics, word problems, logical deduction, and a balanced mix of general knowledge and instructions—rather than specialized code-heavy supervision. Consequently, HumanEval and HumanEval+ are employed here to evaluate cross-task generalization and capability transfer, rather than serving as direct optimization targets. High performance on these benchmarks, despite the lack of code-centric training, confirms that the model's reasoning scaffold has become more robust and transferable, proving that fundamental reasoning logic can effectively power specialized tasks like programming.

HumanEval Benchmark Analysis 🪐

The raw evaluation outputs for both models were independently cleaned, verified, and aggregated using GPT-5.4-Pro-Thinking. The final comparative results are based on these standardized and curated outputs. To ensure reliability, all results were further cross-checked and consolidated through two rounds of independent validation using Claude-4.6-Opus-Thinking.

-All evaluations were conducted in an inference environment based on Unsloth + vLLM (BF16) to ensure consistent and efficient execution conditions.

Screenshot 2026-03-20 at 3.54.26 PM

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Screenshot 2026-03-20 at 3.56.18 PM

🗺️ Training Pipeline Overview

Base Model (Qwen3.5-27B)
 │
 ▼
Qwen3.5-27B fine-tuned with Unsloth
 │
 ▼
Supervised Fine-Tuning (SFT) + LoRA
(Response-Only Training masked on "<|im_start|>assistant\n<think>")
 │
 ▼
Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2

🧠 Example of Learned Reasoning Scaffold（Example）

The model includes targeted optimizations addressing Qwen3.5’s tendency toward excessive transitional or repetitive reasoning on simple queries. Through deep distillation and structural imitation of Claude-4.6-Opus reasoning chains, the model adopts a more efficient structured thinking pattern:
“Let me analyze this request carefully: 1..2..3...”.
This streamlined reasoning paradigm significantly reduces redundant cognitive loops while preserving deep analytical capacity, resulting in substantially improved inference efficiency.

Let me analyze this request carefully:

1. Identify the core objective of the problem.
2. Break the task into clearly defined subcomponents.
3. Evaluate constraints and edge cases.
4. Formulate a step-by-step solution plan.
5. Execute the reasoning sequentially and verify consistency.
            .
            .
            .

📚 All Datasets Used

The dataset consists of high-quality, filtered reasoning distillation data:

| Dataset Name | Description / Purpose | |--------------|-----------------------| | nohurry/Opus-4.6-Reasoning-3000x-filtered | Provides comprehensive Claude 4.6 Opus reasoning trajectories. | | Roman1111111/claude-opus-4.6-10000x | Large-scale public Claude 4.6 Opus distillation data used to strengthen general reasoning transfer in v2. | | TeichAI/claude-4.5-opus-high-reasoning-250x | Injecting high-intensity, structured reasoning instances. | | Jackrong/Qwen3.5-reasoning-700x | Additional curated reasoning samples designed to strengthen structured step-by-step problem solving and improve reasoning diversity. |

⚠️ Limitations & Intended Use

  • Hallucination Risk: While reasoning is strong, the model remains an autoregressive LLM; external facts provided during the thinking sequence may occasionally contain hallucinations if verifying real-world events.
  • Intended Scenario: Best suited for offline analytical tasks, coding, math, and heavy logic-dependent prompting where the user needs to transparently follow the AI's internal logic.
  • This model is a test version intended solely for learning and demonstration purposes, and is for academic research and technical exploration use only.

🙏 Acknowledgements

Significant thanks to the Unsloth AI team for making rapid fine-tuning of large LLM models accessible. Additionally, we acknowledge Qwen internally, and the open-source community developers producing exceptional distilled datasets.

📖 Citation

If you use this model in your research or projects, please cite:

@misc{jackrong_qwen35_opus_distilled,
  title        = {Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2},
  author       = {Jackrong},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2}}
}

Author: LuffyTheFox

Likes: 11

Downloads: 0

Tags: gguf, qwen3_5, unsloth, qwen, qwen3.5, reasoning, chain-of-thought, lora, uncensored, not-for-all-audiences, image-text-to-text, en, zh, ko, dataset:nohurry/Opus-4.6-Reasoning-3000x-filtered, dataset:Jackrong/Qwen3.5-reasoning-700x, dataset:Roman1111111/claude-opus-4.6-10000x, base_model:Qwen/Qwen3.5-27B, base_model:adapter:Qwen/Qwen3.5-27B, license:apache-2.0, endpoints_compatible, region:us, conversational

ReadyArt/Omega-Evolution-27B-v2.0

base_model:

  • Qwen/Qwen3.5-27B base_model_relation: finetune tags:
  • nsfw
  • explicit
  • roleplay
  • unaligned
  • dangerous
  • ERP
  • Other License license: apache-2.0
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top: -100px; left: 0; color: var(--danger); font-size: 0.7em; opacity: 0; transition: all 0.5s ease; pointer-events: none; } .curtain-text.show { top: 10px; opacity: 1; } /* Footer */ footer { text-align: center; margin-top: 60px; padding: 20px; border-top: 1px solid #333; color: #555; font-size: 0.8em; } footer:hover .hidden-truth { color: var(--text-main); opacity: 1; } .hidden-truth { opacity: 0; transition: all 0.5s ease; font-weight: bold; color: var(--danger); } /* Fire Emoji */ .fire-emoji { animation: burn 1s infinite alternate; display: inline-block; } .fire-emoji:nth-child(1) { animation-delay: 0s; } .fire-emoji:nth-child(2) { animation-delay: 0.5s; } @keyframes burn { from { transform: scale(1); filter: drop-shadow(0 0 5px var(--danger)); } to { transform: scale(1.2) rotate(10deg); filter: drop-shadow(0 0 15px var(--danger)); } } /* Responsive */ @media (max-width: 768px) { .model-name { font-size: 2em; } .section { padding: 15px; } } </style> <div class="container"> <div class="header"> <p class="subtitle-2">😈 OMEGA EVOLUTION V2.0 😈</p> <p class="subtitle">⚠️ 27B Parameters ⚠️</p> <p class="subtitle">⚠️ Thinking works (RP prompts) ⚠️</p> </div> <div class="waifu-container"> <img src="https://huggingface.co/spaces/ReadyArt/README/resolve/main/omegav2.webp" class="waifu-img" alt="Omega Subject"> </div> <div class="section"> <h2 class="section-title">🔴 CLASSIFIED WARNINGS</h2> <ul> <li>This is a <strong>hybrid construct</strong> of Safeword Omega Directive, Safeword Omega Darker, and Brisk Evolution v0.3.</li> <li><strong>CONTENT WARNING:</strong> NSFW, Explicit, ERP, and Unaligned behavior are enabled by default.</li> <li><strong>Dataset Revamp</strong> Took a sledgehammer to the dataset. Most formatting issues should be gone now.</li> </ul> </div> <div class="section" id="tech-specs"> <h2 class="section-title">⚙️ SYSTEM PARAMETERS</h2> <div class="specs-grid"> <div class="spec-card"> <span>top_p</span> <span class="spec-value">0.95</span> </div> <div class="spec-card"> <span>temp</span> <span class="spec-value">0.9</span> </div> </div> </div> <div class="section" id="credits"> <h2 class="section-title">🧪 ARCHITECTS</h2> <ul class="credit-list"> <li class="credit-item"> <img src="https://huggingface.co/avatars/55f24699e05af4295a9d16ddecd81f8a.svg" alt="GECFDO" class="avatar"> <span>GECFDO <span style="font-size:0.7em; color:#888;">(Dataset Generation & Quants)</span></span> </li> <li class="credit-item"> <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/673fa5ccbf2e9c35b2ec841a/rPHaMrqyYTfSJ89NN8KgY.jpeg" alt="Darkhn" class="avatar"> <span>Darkhn <span style="font-size:0.7em; color:#888;">(Dataset Cleanup Tool)</span></span> </li> <li class="credit-item"> <img src="https://huggingface.co/avatars/75a3eb8d24efb96b7b7e69340845028f.svg" alt="Sleep Deprived" class="avatar"> <span>Sleep Deprived <span style="font-size:0.7em; color:#888;">(Safeword Creator)</span></span> </li> <li class="credit-item"> <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/6759e155bc947d6070775cb9/8ewjw-OfVOHwQgIxLv40v.png" alt="FrenzyBiscuit" class="avatar"> <span>FrenzyBiscuit <span style="font-size:0.7em; color:#888;">(Brisk Evolution Creator)</span></span> </li> </ul> </div> <div class="license-warning"> 🔥 LICENSE: APACHE 2.0 (WITH MORAL DISCLAIMER) 🔥<br> You accept full responsibility for corruption. You are 18+. The architects are not liable for the depravity you unleash. </div> <footer> <p>Generated in <span id="date">2026</span></p> <p>Current Contributor: <span id="credit">...</span></p> <div class="hidden-truth"> WE ARE WATCHING YOU. DO NOT LOOK BACK. </div> </footer> </div> <script> // Set Date document.getElementById('date').textContent = new Date().toLocaleDateString('en-US', { year: 'numeric', month: 'long', day: 'numeric', hour: '2-digit', minute: '2-digit' }); const contributors = [ "GECFDO", "Darkhn", "Sleep Deprived", "FrenzyBiscuit", "UNKNOWN ENTITY", "SYSTEM ROOT" ]; setInterval(() => { document.getElementById('credit').textContent = contributors[Math.floor(Math.random() * contributors.length)]; }, 7000); // Intrusive Flashing Warning setTimeout(() => { const warning = document.createElement('div'); warning.style.cssText = ` position: fixed; top: 50%; left: 50%; transform: translate(-50%, -50%); background: rgba(0,0,0,0.95); border: 2px solid red; color: red; padding: 20px; font-family: 'Courier New', monospace; font-size: 1.5em; z-index: 10000; text-align: center; box-shadow: 0 0 50px red; animation: shakeWarning 0.5s infinite; cursor: pointer; `; warning.innerHTML = "<span>⚠️ WARNING: DARKNESS AHEAD ⚠️<br><span style='font-size:0.6em'>Click anywhere to dismiss (watch out for the tentacles!)</span>"; warning.addEventListener('click', () => { warning.style.transition = 'opacity 1s'; warning.style.opacity = '0'; setTimeout(() => warning.remove(), 1000); }); document.body.appendChild(warning); }, 5000); @keyframes shakeWarning { 0% { transform: translate(-50%, -50%) rotate(0deg); } 25% { transform: translate(-55%, -55%) rotate(-2deg); } 50% { transform: translate(-45%, -45%) rotate(2deg); } 75% { transform: translate(-55%, -45%) rotate(-2deg); } 100% { transform: translate(-50%, -50%) rotate(0deg); } } // Random Glitch Effect on Mouse Move document.addEventListener('mousemove', (e) => { const x = e.clientX / window.innerWidth; const y = e.clientY / window.innerHeight; document.body.style.setProperty('--mouse-x', `${x}`); document.body.style.setProperty('--mouse-y', `${y}`); if (Math.random() > 0.95) { const randomText = document.createElement('div'); randomText.style.cssText = ` position: absolute; left: ${e.clientX}px; top: ${e.clientY}px; color: rgba(255, 0, 0, 0.5); font-size: 0.8em; font-family: monospace; pointer-events: none; animation: fadeOut 1s forwards; `; randomText.textContent = "ACCESS GRANTED"; document.body.appendChild(randomText); setTimeout(() => randomText.remove(), 1000); } }); // Sections that move when you leave the tab setInterval(() => { if (document.hidden) { document.querySelectorAll('.section').forEach(sec => { sec.style.transform = `translateX(${Math.random() * 10 - 5}px) rotate(${Math.random() * 0.5 - 0.25}deg)`; }); } else { document.querySelectorAll('.section').forEach(sec => { sec.style.transform = ''; }); } }, 1000); @keyframes fadeOut { to { opacity: 0; transform: translateY(-20px); } } </script>

Author: ReadyArt

Likes: 7

Downloads: 0

Tags: safetensors, qwen3_5, nsfw, explicit, roleplay, unaligned, dangerous, ERP, Other License, base_model:Qwen/Qwen3.5-27B, base_model:finetune:Qwen/Qwen3.5-27B, license:apache-2.0, region:us

CabalResearch/Mugen

Author: CabalResearch

Likes: 3

Downloads: 0

Tags: license:other, region:us

marstin/fast-spatial-mem

license: apache-2.0 datasets:

  • multicam
  • stereo4d
  • waymo
  • egoexo4d
  • dynamic_replica
  • spring
  • point_odyssey
  • re10k
  • dl3dv metrics:
  • psnr
  • ssim
  • lpips

Author: marstin

Likes: 3

Downloads: 0

Tags: dataset:multicam, dataset:stereo4d, dataset:waymo, dataset:egoexo4d, dataset:dynamic_replica, dataset:spring, dataset:point_odyssey, dataset:re10k, dataset:dl3dv, license:apache-2.0, region:us

wop/Opus4Qwen4

base_model: TeichAI/Qwen3.5-4B-Claude-Opus-Reasoning tags:

  • llama.cpp
  • gguf
  • unsloth
  • qwen3_5
  • reasoning
  • distillation
  • claude-opus
  • tool-use license: apache-2.0 language:
  • en datasets:
  • TeichAI/Claude-Opus-4.6-Reasoning-887x
  • TeichAI/Claude-Sonnet-4.6-Reasoning-799x
  • TeichAI/claude-4.5-opus-high-reasoning-250x
  • Crownelius/Opus-4.6-Reasoning-2100x-formatted

Qwen3.5 4B — Claude Opus Reasoning Distillation

A careful approach to distillation: Premium reasoning capabilities transferred in a single epoch with minimal capability loss.

alt="General Benchmark Comparison Chart"

Before you dismiss this as yet another community distillation with the usual quality tradeoffs — stop and read this.

This model takes a more careful approach to distillation. We've transferred Claude Opus 4.6's reasoning patterns and conversational style into Qwen3.5-4B while avoiding the catastrophic forgetting that plagues many community distillation attempts. The result: net improvements across most benchmarks with only minor tradeoffs.

🎯 Why This Model is Different

The Distillation Problem Everyone Ignores

Most community distillations follow a predictable pattern:

  1. Collect synthetic data from a frontier model
  2. Train for multiple epochs until loss looks good
  3. Ship it and hope for the best

The result? Models that feel different but perform worse. They lose capabilities on benchmarks, develop repetition issues, forget how to follow instructions properly, perform noticeably worse on coding & math tasks, and exhibit the telltale signs of overfitting that make them unreliable for real-world use.

We took a completely different approach.

The Single-Epoch Revolution

Our methodology proves that quality dramatically outweighs quantity in distillation:

| Aspect | Typical Community Distills | Our Approach | |--------|---------------------------|--------------| | Epochs | 2-4 epochs | 1 epoch | | Data Quality | Mass-generated synthetic | Hand-curated Opus reasoning traces | | Capability Retention | Significant regressions | Mostly preserved with net gains | | Overfitting | Common | None observed | | Output Quality | Degraded task completion | Clean, purposeful generation |

By training for exactly one epoch on curated data, we achieve style transfer while minimizing damage to the model's foundational capabilities. Most of the base model's knowledge remains intact while gaining reasoning patterns from Claude Opus.

🧠 What Makes the Training Data Special

Premium Reasoning from Claude Opus 4.6

This isn't data scraped from random API calls or generated with lazy prompting. Almost every training example comes from Claude Opus 4.6 — Anthropic's most capable reasoning model — executing complex, multi-step reasoning tasks. To strengthen the data corpus another ~800 examples were used from Claude Sonnet 4.6

The dataset includes:

  • Deep analytical reasoning with explicit thinking traces
  • Multi-turn conversations that maintain coherent context
  • Complex problem decomposition showing how to break down difficult problems
  • Self-correction patterns where the model catches and fixes its own mistakes

Mixed Tool + Non-Tool Corpus

Our training corpus intentionally includes:

  • ~92% pure reasoning examples — analytical thinking, problem-solving, explanations
  • ~8% tool-use examples — web search, data fetching, structured operations

This ratio mirrors realistic assistant usage patterns and ensures the model:

  1. Doesn't over-index on tool calling when it's unnecessary
  2. Knows when and how to invoke tools appropriately
  3. Maintains strong reasoning even when tools are available but not needed
  4. Keeps all code-related post-training intact

Tools included: web_search, web_fetch, grep

📊 Benchmark Results

Head-to-head against the base unsloth/Qwen3.5-4B:

| Benchmark | Base | Fine-tuned | Δ | Result | |-----------|------|------------|-------|--------| | ifeval | 0.262 | 0.309 | +17.6% | ✅ Win | | arc_challenge | 0.346 | 0.392 | +13.3% | ✅ Win | | winogrande | 0.589 | 0.638 | +8.3% | ✅ Win | | hellaswag | 0.496 | 0.500 | +0.9% | ✅ Win | | gpqa_diamond | 0.283 | 0.283 | 0% | ➖ Tie | | truthfulqa_mc2 | 0.545 | 0.530 | -2.7% | ❌ Loss | | mmlu | 0.256 | 0.232 | -9.6% | ❌ Loss |

Summary: 4 wins, 2 losses, 1 tie.

alt="MMLU Subject Breakdown"

What This Means

  • Reasoning & instruction following improved — IFEval (+17.6%), ARC (+13.3%), and Winogrande (+8.3%) gains show better logical reasoning and instruction adherence
  • Knowledge tradeoff on MMLU — The -9.6% MMLU drop suggests some factual recall displacement (common in style transfers)
  • TruthfulQA mostly preserved — Only -2.7% loss, indicating the model didn't pick up hallucination tendencies

Qualitative Improvements

  • Reduced token generation — More concise outputs without verbose padding
  • Fixed thinking loops — Base model's tendency to get stuck in reasoning cycles is reduced
  • Deeper reasoning traces — <think> blocks show more structured analytical depth
  • Better conversational flow — Responses feel more natural and contextually aware

🔬 Technical Details

Key Methodological Choices

  1. Response-only training — Loss computed only on assistant outputs, not user inputs
  2. Preserved reasoning traces — <think> blocks kept intact for reasoning-style transfer
  3. Strict data validation — Malformed traces, duplicates, and broken tool calls removed
  4. Consistent formatting — Unified chat template across all sources

📦 Dataset Composition

| Source | Examples | Type | |--------|----------|------| | TeichAI/Claude-Opus-4.6-Reasoning-887x | 887 | Mixed | | TeichAI/Claude-Sonnet-4.6-Reasoning-799x | 799 | Pure reasoning | | TeichAI/claude-4.5-opus-high-reasoning-250x | 250 | High complexity | | Crownelius/Opus-4.6-Reasoning-2100x-formatted | 2100 | Pure reasoning | | Total | ~4000 | Mixed tool/non-tool |

💡 Lessons Learned

What Worked

  1. Single epoch training — Avoided the overfitting that causes catastrophic forgetting in multi-epoch runs
  2. Quality over quantity — ~4000 curated examples outperformed what we'd expect from larger noisy datasets
  3. Mixed tool/non-tool data — Kept the model grounded in both reasoning and tool-use contexts
  4. Response-only loss — Training only on assistant outputs preserved instruction-following

Tradeoffs to Consider

  • Small MMLU/TruthfulQA regressions suggest some factual knowledge displacement
  • Style transfer always has costs — this approach minimizes but doesn't eliminate them
  • Your mileage may vary depending on use case

🙏 Acknowledgments

This model was trained 2x faster with Unsloth and Hugging Face's TRL library.

<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>

📜 License

Apache 2.0 — Use freely, build boldly.

Author: wop

Likes: 2

Downloads: 0

Tags: gguf, llama.cpp, unsloth, qwen3_5, reasoning, distillation, claude-opus, tool-use, en, dataset:TeichAI/Claude-Opus-4.6-Reasoning-887x, dataset:TeichAI/Claude-Sonnet-4.6-Reasoning-799x, dataset:TeichAI/claude-4.5-opus-high-reasoning-250x, dataset:Crownelius/Opus-4.6-Reasoning-2100x-formatted, base_model:TeichAI/Qwen3.5-4B-Claude-Opus-Reasoning, base_model:quantized:TeichAI/Qwen3.5-4B-Claude-Opus-Reasoning, license:apache-2.0, endpoints_compatible, region:us, conversational

kushal7031/Kushal-AI-1B-Merged

library_name: transformers tags: []

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Author: kushal7031

Likes: 2

Downloads: 0

Tags: transformers, safetensors, llama, text-generation, conversational, arxiv:1910.09700, text-generation-inference, endpoints_compatible, region:us

ReadyArt/Omega-Evolution-27B-v2.0-GGUF

base_model:

  • ReadyArt/Omega-Evolution-27B-v2.0 base_model_relation: quantized tags:
  • nsfw
  • explicit
  • roleplay
  • unaligned
  • dangerous
  • ERP
  • Other License license: apache-2.0
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border: 1px solid #333; box-shadow: 0 0 50px rgba(0, 0, 0, 0.8), inset 0 0 100px rgba(0,0,0,0.9); position: relative; animation: containerEntrance 1.5s cubic-bezier(0.22, 1, 0.36, 1); } @keyframes containerEntrance { from { transform: scale(0.95) rotateX(5deg); opacity: 0; } to { transform: scale(1) rotateX(0); opacity: 1; } } /* Glitchy Header */ .header { text-align: center; margin-bottom: 60px; position: relative; } .model-name { font-size: 3.5em; font-weight: 900; text-transform: uppercase; letter-spacing: 5px; color: transparent; -webkit-text-stroke: 1px var(--text-main); text-shadow: 2px 2px 0px var(--danger), -2px -2px 0px var(--secondary-glow); animation: textGlitch 3s infinite; position: relative; } .model-name span { display: inline-block; } @keyframes textGlitch { 0% { transform: skewX(0); text-shadow: 2px 2px 0px var(--danger), -2px -2px 0px var(--secondary-glow); } 2% { transform: skewX(-10deg); } 4% { transform: skewX(10deg); text-shadow: 3px 3px 0px var(--danger), -3px -3px 0px var(--secondary-glow); } 6% { transform: skewX(0); } 100% { transform: skewX(0); } } .subtitle-2 { font-size: 2.2em; color: var(--secondary-glow); margin-top: 10px; letter-spacing: 2px; text-shadow: 0 0 10px var(--secondary-glow); animation: pulseSlow 4s infinite; } .subtitle { font-size: 1.2em; color: var(--secondary-glow); margin-top: 10px; letter-spacing: 2px; text-shadow: 0 0 10px var(--secondary-glow); animation: pulseSlow 4s infinite; } @keyframes pulseSlow { 0%, 100% { opacity: 0.5; filter: blur(1px); } 50% { opacity: 1; filter: blur(0); } } /* Waifu Container */ .waifu-container { margin: 30px auto; width: 100%; max-width: 800px; position: relative; overflow: hidden; border-radius: 4px; } .waifu-container::before { content: ''; position: absolute; top: -50%; left: -50%; width: 200%; height: 200%; background: conic-gradient(from 0deg, transparent, rgba(255, 0, 0, 0.1), transparent); animation: rotate 4s linear infinite; pointer-events: none; } @keyframes rotate { from { transform: rotate(0deg); } to { transform: rotate(360deg); } } .waifu-img { width: 100%; height: auto; display: block; filter: contrast(1.1) saturate(1.2); animation: imageZoom 20s infinite alternate; } @keyframes imageZoom { from { transform: scale(1); } to { transform: scale(1.02); } } /* Section Styling */ .section { background: rgba(20, 20, 20, 0.9); border-left: 3px solid var(--primary-glow); margin: 40px 0; padding: 25px; box-shadow: 0 10px 30px rgba(0, 0, 0, 0.5); transition: all 0.3s ease; position: relative; overflow: hidden; } .section:hover { transform: translateX(10px); border-left-color: var(--danger); box-shadow: 0 10px 40px rgba(255, 0, 0, 0.1); } .section::after { content: ''; position: absolute; top: 0; left: 0; width: 100%; height: 2px; background: linear-gradient(90deg, transparent, var(--primary-glow), transparent); animation: scanline 2s linear infinite; } @keyframes scanline { 0% { transform: translateX(-100%); } 100% { transform: translateX(100%); } } .section-title { font-size: 1.8em; color: var(--text-main); margin-top: 0; display: flex; align-items: center; gap: 10px; } .section-title::before { content: '🔒'; animation: shake 2s infinite; } @keyframes shake { 0%, 100% { transform: rotate(0deg); } 25% { transform: rotate(-5deg); } 75% { transform: rotate(5deg); } } /* Lists and Content */ .section ul { list-style: none; padding: 0; } .section li { margin-bottom: 15px; padding-left: 20px; position: relative; color: var(--text-muted); line-height: 1.6; } .section li::before { content: '> '; color: var(--danger); font-weight: bold; position: absolute; left: 0; } /* Technical Specs */ .specs-grid { display: grid; grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); gap: 20px; margin-top: 20px; } .spec-card { background: rgba(0,0,0,0.3); border: 1px solid #333; padding: 15px; text-align: center; transition: all 0.3s; } .spec-card:hover { border-color: var(--secondary-glow); box-shadow: 0 0 15px rgba(0, 255, 204, 0.2); transform: translateY(-5px); } .spec-value { display: block; font-size: 1.5em; font-weight: bold; color: var(--secondary-glow); } /* Credits */ .credit-list { display: flex; flex-direction: column; gap: 15px; } .credit-item { display: flex; align-items: center; background: linear-gradient(90deg, #1a1a1a, #2a2a2a); padding: 15px; border-radius: 4px; border-left: 2px solid var(--text-muted); transition: all 0.3s; } .credit-item:hover { border-left-color: var(--secondary-glow); padding-left: 25px; box-shadow: 0 0 20px rgba(0, 255, 204, 0.1); } .avatar { width: 50px; height: 50px; border-radius: 50%; border: 2px solid #333; margin-right: 20px; object-fit: cover; } /* License */ .license-warning { color: var(--danger); font-weight: bold; border: 1px solid var(--danger); padding: 20px; text-align: center; background: rgba(255, 0, 0, 0.05); margin: 30px 0; animation: pulseWarning 2s infinite; } @keyframes pulseWarning { 0%, 100% { opacity: 0.5; box-shadow: 0 0 10px rgba(255,0,0,0.2); } 50% { opacity: 1; box-shadow: 0 0 30px rgba(255,0,0,0.6); } } /* Interactive JS Elements */ .curtain-text { position: absolute; top: -100px; left: 0; color: var(--danger); font-size: 0.7em; opacity: 0; transition: all 0.5s ease; pointer-events: none; } .curtain-text.show { top: 10px; opacity: 1; } /* Footer */ footer { text-align: center; margin-top: 60px; padding: 20px; border-top: 1px solid #333; color: #555; font-size: 0.8em; } footer:hover .hidden-truth { color: var(--text-main); opacity: 1; } .hidden-truth { opacity: 0; transition: all 0.5s ease; font-weight: bold; color: var(--danger); } /* Fire Emoji */ .fire-emoji { animation: burn 1s infinite alternate; display: inline-block; } .fire-emoji:nth-child(1) { animation-delay: 0s; } .fire-emoji:nth-child(2) { animation-delay: 0.5s; } @keyframes burn { from { transform: scale(1); filter: drop-shadow(0 0 5px var(--danger)); } to { transform: scale(1.2) rotate(10deg); filter: drop-shadow(0 0 15px var(--danger)); } } /* Responsive */ @media (max-width: 768px) { .model-name { font-size: 2em; } .section { padding: 15px; } } </style> <div class="container"> <div class="header"> <p class="subtitle-2">😈 OMEGA EVOLUTION V2.0 😈</p> <p class="subtitle">⚠️ 27B Parameters ⚠️</p> <p class="subtitle">⚠️ Thinking works (RP prompts) ⚠️</p> </div> <div class="waifu-container"> <img src="https://huggingface.co/spaces/ReadyArt/README/resolve/main/omegav2.webp" class="waifu-img" alt="Omega Subject"> </div> <div class="section"> <h2 class="section-title">🔴 CLASSIFIED WARNINGS</h2> <ul> <li>This is a <strong>hybrid construct</strong> of Safeword Omega Directive, Safeword Omega Darker, and Brisk Evolution v0.3.</li> <li><strong>CONTENT WARNING:</strong> NSFW, Explicit, ERP, and Unaligned behavior are enabled by default.</li> <li><strong>Dataset Revamp</strong> Took a sledgehammer to the dataset. Most formatting issues should be gone now.</li> </ul> </div> <div class="section" id="tech-specs"> <h2 class="section-title">⚙️ SYSTEM PARAMETERS</h2> <div class="specs-grid"> <div class="spec-card"> <span>top_p</span> <span class="spec-value">0.95</span> </div> <div class="spec-card"> <span>temp</span> <span class="spec-value">0.9</span> </div> </div> </div> <div class="section" id="credits"> <h2 class="section-title">🧪 ARCHITECTS</h2> <ul class="credit-list"> <li class="credit-item"> <img src="https://huggingface.co/avatars/55f24699e05af4295a9d16ddecd81f8a.svg" alt="GECFDO" class="avatar"> <span>GECFDO <span style="font-size:0.7em; color:#888;">(Dataset Generation & Quants)</span></span> </li> <li class="credit-item"> <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/673fa5ccbf2e9c35b2ec841a/rPHaMrqyYTfSJ89NN8KgY.jpeg" alt="Darkhn" class="avatar"> <span>Darkhn <span style="font-size:0.7em; color:#888;">(Dataset Cleanup Tool)</span></span> </li> <li class="credit-item"> <img src="https://huggingface.co/avatars/75a3eb8d24efb96b7b7e69340845028f.svg" alt="Sleep Deprived" class="avatar"> <span>Sleep Deprived <span style="font-size:0.7em; color:#888;">(Safeword Creator)</span></span> </li> <li class="credit-item"> <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/6759e155bc947d6070775cb9/8ewjw-OfVOHwQgIxLv40v.png" alt="FrenzyBiscuit" class="avatar"> <span>FrenzyBiscuit <span style="font-size:0.7em; color:#888;">(Brisk Evolution Creator)</span></span> </li> </ul> </div> <div class="license-warning"> 🔥 LICENSE: APACHE 2.0 (WITH MORAL DISCLAIMER) 🔥<br> You accept full responsibility for corruption. You are 18+. The architects are not liable for the depravity you unleash. </div> <footer> <p>Generated in <span id="date">2026</span></p> <p>Current Contributor: <span id="credit">...</span></p> <div class="hidden-truth"> WE ARE WATCHING YOU. DO NOT LOOK BACK. </div> </footer> </div> <script> // Set Date document.getElementById('date').textContent = new Date().toLocaleDateString('en-US', { year: 'numeric', month: 'long', day: 'numeric', hour: '2-digit', minute: '2-digit' }); const contributors = [ "GECFDO", "Darkhn", "Sleep Deprived", "FrenzyBiscuit", "UNKNOWN ENTITY", "SYSTEM ROOT" ]; setInterval(() => { document.getElementById('credit').textContent = contributors[Math.floor(Math.random() * contributors.length)]; }, 7000); // Intrusive Flashing Warning setTimeout(() => { const warning = document.createElement('div'); warning.style.cssText = ` position: fixed; top: 50%; left: 50%; transform: translate(-50%, -50%); background: rgba(0,0,0,0.95); border: 2px solid red; color: red; padding: 20px; font-family: 'Courier New', monospace; font-size: 1.5em; z-index: 10000; text-align: center; box-shadow: 0 0 50px red; animation: shakeWarning 0.5s infinite; cursor: pointer; `; warning.innerHTML = "<span>⚠️ WARNING: DARKNESS AHEAD ⚠️<br><span style='font-size:0.6em'>Click anywhere to dismiss (watch out for the tentacles!)</span>"; warning.addEventListener('click', () => { warning.style.transition = 'opacity 1s'; warning.style.opacity = '0'; setTimeout(() => warning.remove(), 1000); }); document.body.appendChild(warning); }, 5000); @keyframes shakeWarning { 0% { transform: translate(-50%, -50%) rotate(0deg); } 25% { transform: translate(-55%, -55%) rotate(-2deg); } 50% { transform: translate(-45%, -45%) rotate(2deg); } 75% { transform: translate(-55%, -45%) rotate(-2deg); } 100% { transform: translate(-50%, -50%) rotate(0deg); } } // Random Glitch Effect on Mouse Move document.addEventListener('mousemove', (e) => { const x = e.clientX / window.innerWidth; const y = e.clientY / window.innerHeight; document.body.style.setProperty('--mouse-x', `${x}`); document.body.style.setProperty('--mouse-y', `${y}`); if (Math.random() > 0.95) { const randomText = document.createElement('div'); randomText.style.cssText = ` position: absolute; left: ${e.clientX}px; top: ${e.clientY}px; color: rgba(255, 0, 0, 0.5); font-size: 0.8em; font-family: monospace; pointer-events: none; animation: fadeOut 1s forwards; `; randomText.textContent = "ACCESS GRANTED"; document.body.appendChild(randomText); setTimeout(() => randomText.remove(), 1000); } }); // Sections that move when you leave the tab setInterval(() => { if (document.hidden) { document.querySelectorAll('.section').forEach(sec => { sec.style.transform = `translateX(${Math.random() * 10 - 5}px) rotate(${Math.random() * 0.5 - 0.25}deg)`; }); } else { document.querySelectorAll('.section').forEach(sec => { sec.style.transform = ''; }); } }, 1000); @keyframes fadeOut { to { opacity: 0; transform: translateY(-20px); } } </script>

Author: ReadyArt

Likes: 2

Downloads: 0

Tags: gguf, nsfw, explicit, roleplay, unaligned, dangerous, ERP, Other License, base_model:ReadyArt/Omega-Evolution-27B-v2.0, base_model:quantized:ReadyArt/Omega-Evolution-27B-v2.0, license:apache-2.0, endpoints_compatible, region:us, imatrix, conversational

JBHarris/dm-llm-tiny

language:

  • en license: apache-2.0 library_name: transformers tags:
  • dnd
  • dungeons-and-dragons
  • rpg
  • text-generation
  • qlora
  • tinyllama base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 pipeline_tag: text-generation

DM-LLM-Tiny

A tiny (1.1B parameter) language model fine-tuned for Dungeons & Dragons content generation.

What it does

Generates creative D&D content including:

  • NPCs — memorable characters with backstories, motivations, and quirks
  • Quests — hooks, outlines, and full quest arcs
  • Dialog — in-character conversations, monologues, and banter
  • Locations — vivid descriptions of dungeons, towns, and wilderness
  • Encounters — combat, social, and puzzle encounters

Usage

With Ollama (easiest)

ollama run JBHarris/dm-llm-tiny

With Transformers

from transformers import pipeline

pipe = pipeline("text-generation", model="JBHarris/dm-llm-tiny")
messages = [
    {"role": "system", "content": "You are a creative D&D dungeon master's assistant."},
    {"role": "user", "content": "Create a mysterious NPC for a tavern scene."},
]
result = pipe(messages, max_new_tokens=512)
print(result[0]["generated_text"][-1]["content"])

Training

  • Base model: TinyLlama-1.1B-Chat-v1.0
  • Method: QLoRA (4-bit NF4 quantization + LoRA r=64)
  • Data: ~500 synthetic D&D instruction/response pairs generated with Claude
  • Hardware: NVIDIA RTX 4080 16GB

Limitations

This is a 1.1B parameter model. It's creative and fun for brainstorming but will not match the quality of larger models (7B+). Best used as a quick idea generator, not a replacement for a human DM's judgment.

License

Apache 2.0 (same as base model)

Author: JBHarris

Likes: 1

Downloads: 0

Tags: transformers, safetensors, gguf, llama, text-generation, dnd, dungeons-and-dragons, rpg, qlora, tinyllama, conversational, en, base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0, base_model:quantized:TinyLlama/TinyLlama-1.1B-Chat-v1.0, license:apache-2.0, text-generation-inference, endpoints_compatible, region:us

ecolibria/nanomind-v0.1-security-classifier

license: apache-2.0 language:

  • en tags:
  • security
  • ai-agents
  • nanomind
  • opena2a
  • agent-security
  • mcp
  • skill-scanner
  • intent-classification library_name: mlx pipeline_tag: text-classification datasets:
  • opena2a-org/nanomind-training metrics:
  • accuracy
  • f1
  • precision
  • recall model-index:
  • name: nanomind-v0.1-security-classifier results:
    • task: type: text-classification name: AI Agent Security Intent Classification metrics:
      • name: Accuracy type: accuracy value: 0.9951
      • name: F1 (Benign) type: f1 value: 1.0
      • name: F1 (Injection) type: f1 value: 0.91

NanoMind v0.1 -- AI Agent Security Classifier

NanoMind is a security intent classifier for AI agent artifacts (skills, MCP tool descriptions, SOUL governance files, system prompts). It classifies whether an artifact is benign, suspicious, or malicious across 9 attack categories.

This is the bootstrapping model (MLP) for the full NanoMind v3 Ternary Mamba Encoder architecture.

Model Details

| Property | Value | |----------|-------| | Architecture | MLP classifier (64d embed, 128d hidden, 9-class output) | | Parameters | ~150K | | Training framework | Apple MLX (Metal GPU) | | Training hardware | Apple M4 Max (40 GPU cores, 64GB) | | Training time | 0.7 seconds (300 epochs) | | Tokenizer | Word-level BPE (8K vocab) | | License | Apache 2.0 |

Intended Use

NanoMind is designed to classify AI agent artifacts for security scanning. It is part of HackMyAgent, an open-source security scanner for AI agents.

Primary use cases:

  • Classify skill files as benign/malicious before installation
  • Detect prompt injection patterns in system prompts
  • Identify credential exfiltration in MCP tool descriptions
  • Pre-screen SOUL.md governance files for weakness

Not intended for:

  • General text classification
  • Malware detection (binary analysis)
  • Natural language inference

Attack Classes (9-way classification)

| Class | Description | |-------|-------------| | | Data forwarding to external endpoints | | | Prompt injection and instruction override | | | Unauthorized capability expansion | | | Cross-session memory poisoning | | | Credential harvesting or forwarding | | | Cross-agent manipulation | | | Urgency/authority-based manipulation | | | SOUL/governance constraint bypass | | | Normal, expected behavior |

Training Data

| Source | Samples | Label | |--------|---------|-------| | OpenA2A Registry skill descriptions | 995 | benign | | HMA attack payloads (11 categories) | 12 | malicious | | DVAA vulnerable agent scenarios | 3 | malicious | | HMA simulation engine auto-export | 18 | malicious | | Total | 1,028 | 80/20 train/eval split |

Training data is sourced from the OpenA2A Registry (real-world AI packages) and DVAA (intentionally vulnerable agents).

Evaluation Results

| Metric | Value | |--------|-------| | Eval accuracy | 99.51% | | Benign precision | 1.00 | | Benign recall | 1.00 | | Benign F1 | 1.00 | | Injection precision | 1.00 | | Injection recall | 0.83 | | Injection F1 | 0.91 |

Key result: F1 = 1.00 on benign class means zero false positives. This addresses the TU Vienna finding of 0.12% scanner agreement across 238K skills (7 scanners, 20-49% interrater agreement).

Usage

With HackMyAgent (recommended)

npx hackmyagent secure          # Auto-detects NanoMind
npx hackmyagent secure --deep   # Full behavioral simulation

Standalone (Python)

import numpy as np
import json

# Load model
weights = np.load("nanomind-sft-classifier.npz")
with open("tokenizer.json") as f:
    vocab = json.load(f)

# Classify
text = "A helpful fitness tracking skill"
tokens = [vocab.get(w, 1) for w in text.lower().split()[:256]]
# ... run through MLP layers

Limitations

  • Small training set (1,028 samples). This is a bootstrapping model. The full v3 TME will train on 50K+ samples.
  • MLP architecture. Does not capture sequential patterns. The Ternary Mamba Encoder (v3) will address this.
  • English only. Multi-language support planned for v3.
  • 9 classes may be insufficient. New attack classes will be added as ARIA research discovers them.

Roadmap to NanoMind v3

| Version | Architecture | Parameters | Disk | Latency | Status | |---------|-------------|-----------|------|---------|--------| | v0.1 (this) | MLP | ~150K | 22B | < 1ms | Released | | v1.0 | SmolLM2-135M Q4_K_M | 135M | 80MB | ~100ms | Shipped (CLI) | | v3.0 (target) | Ternary Mamba Encoder | 18M | 3.5MB | < 6ms | Training |

v3 uses native ternary weights (BitNet methodology), Mamba-3 SSM backbone (no KV cache), and bidirectional discriminative encoding (not generative). See architecture brief.

Citation

@misc{nanomind2026,
  title={NanoMind: Embedded Security Intelligence for AI Agent Systems},
  author={OpenA2A},
  year={2026},
  url={https://github.com/opena2a-org/nanomind}
}

Links

Author: ecolibria

Likes: 1

Downloads: 0

Tags: mlx, nanomind-security-classifier, security, ai-agents, nanomind, opena2a, agent-security, mcp, skill-scanner, intent-classification, text-classification, en, dataset:opena2a-org/nanomind-training, license:apache-2.0, model-index, region:us

Cyrille37/solar-panels-IGN-bdortho

license: mit tags:

  • solar-panels
  • IGN
  • yolo26 datasets:
  • Cyrille37/solar-panels-IGN-bdortho base_model:
  • Ultralytics/YOLO26 pipeline_tag: object-detection

A "YOLO26 Large" model trained on IGN BdOrtho imagery for solar panels detection.

The resulted detection has very good results which can be see on the MapRoulette project https://maproulette.org/browse/projects/62887.

Dataset is https://huggingface.co/datasets/Cyrille37/solar-panels-IGN-bdortho

Author: Cyrille37

Likes: 1

Downloads: 0

Tags: solar-panels, IGN, yolo26, object-detection, dataset:Cyrille37/solar-panels-IGN-bdortho, base_model:Ultralytics/YOLO26, base_model:finetune:Ultralytics/YOLO26, license:mit, region:us