Todays AI Summary

AI Developments: ERNIE 4.5 Excels in Reasoning, Lumina-DiMOO Advances Multimodal Generation

Here's a look at the latest advancements in AI, covering improvements in language models, multimodal generation, and more.

Noteworthy Research Papers

  • H2OT: Hierarchical Hourglass Tokenizer for Efficient Video Pose Transformers: This paper introduces a method for improving the efficiency of video pose transformers by pruning redundant pose tokens, making them more practical for resource-constrained devices.
  • Deep Reactive Policy: Learning Reactive Manipulator Motion Planning for Dynamic Environments: This research presents a visuo-motor neural motion policy for robotic manipulators, enabling them to generate collision-free motion in dynamic environments using point cloud sensory input. The policy is pre-trained on a large dataset of expert trajectories and enhanced with a reactive goal-proposal module.
  • Interleaving Reasoning for Better Text-to-Image Generation: This paper explores how interleaving reasoning can improve text-to-image generation. The proposed framework, IRG, alternates between text-based thinking and image synthesis, refining details and visual quality while preserving semantics.
  • Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference: This study introduces Direct-Align, a method for directly aligning diffusion models with human preferences using differentiable reward. It addresses the computational cost of multistep denoising and the need for continuous offline adaptation of reward models.
  • From Noise to Narrative: Tracing the Origins of Hallucinations in Transformers: This paper investigates how and when hallucinations arise in pre-trained transformer models. It reveals that the number of semantic concepts used by the model grows as input information becomes unstructured, leading to the activation of input-insensitive semantic features and hallucinated output.
  • Neuro-Symbolic AI for Cybersecurity: State of the Art, Challenges, and Opportunities: This survey analyzes the emerging field of Neuro-Symbolic AI in cybersecurity, highlighting its potential to revolutionize cybersecurity AI by combining neural pattern recognition with symbolic reasoning.
  • An Ethically Grounded LLM-Based Approach to Insider Threat Synthesis and Detection: This study introduces a novel approach that uses large language models to dynamically synthesize syslog messages containing indicators of insider threat scenarios, demonstrating the potential of LLMs in synthetic dataset generation and insider threat detection.
  • Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition: This work proposes a robust out-of-distribution detection framework that integrates loss correction techniques with low-rank and sparse decomposition methods, improving performance under noisy label settings.
  • Paper2Agent: Reimagining Research Papers As Interactive and Reliable AI Agents: This paper introduces an automated framework that converts research papers into AI agents, enabling users to interact with and utilize the paper's code, data, and methods more effectively.
  • Barlow-Swin: Toward a novel siamese-based segmentation architecture using Swin-Transformers: This work presents a novel end-to-end lightweight architecture designed specifically for real-time binary medical image segmentation.

Model Highlights

  • gabriellarson/ERNIE-4.5-21B-A3B-Thinking-GGUF: This model is a GGUF quantized version of Baidu's ERNIE-4.5-21B-A3B-Thinking, a 21B parameter text MoE model. It features significantly improved performance on reasoning tasks, efficient tool usage, and enhanced long-context understanding capabilities. The model supports function calls and can be deployed using FastDeploy or vLLM.
  • calcuis/hunyuanimage-gguf: This is a GGUF version of the text-to-image model tencent/HunyuanImage-2.1.
  • Alpha-VLLM/Lumina-DiMOO: Lumina-DiMOO is an omni diffusion large language model for multimodal generation and understanding. It supports text-to-image generation, image-to-image generation, and image understanding. The model achieves state-of-the-art performance on multiple benchmarks and demonstrates higher sampling efficiency compared to previous models.

Key Takeaways

  • Reasoning and Long-Context Capabilities: ERNIE-4.5-21B-A3B-Thinking demonstrates significant advancements in reasoning tasks and long-context understanding, making it a powerful tool for complex AI applications.
  • Multimodal Advancements: Lumina-DiMOO showcases the potential of diffusion models for unified multimodal generation and understanding, achieving state-of-the-art performance across various benchmarks.
  • Efficiency in Video Processing: The H2OT framework offers a practical

AI Papers for 2026-04-01

Geometry-aware similarity metrics for neural representations on Riemannian and statistical manifolds

Similarity measures are widely used to interpret the representational geometries used by neural networks to solve tasks. Yet, because existing methods compare the extrinsic geometry of representations in state space, rather than their intrinsic geometry, they may fail to capture subtle yet crucial distinctions between fundamentally different neural network solutions. Here, we introduce metric similarity analysis (MSA), a novel method which leverages tools from Riemannian geometry to compare the intrinsic geometry of neural representations under the manifold hypothesis. We show that MSA can be used to i) disentangle features of neural computations in deep networks with different learning regimes, ii) compare nonlinear dynamics, and iii) investigate diffusion models. Hence, we introduce a mathematically grounded and broadly applicable framework to understand the mechanisms behind neural computations by comparing their intrinsic geometries.

On-the-fly Repulsion in the Contextual Space for Rich Diversity in Diffusion Transformers

Modern Text-to-Image (T2I) diffusion models have achieved remarkable semantic alignment, yet they often suffer from a significant lack of variety, converging on a narrow set of visual solutions for any given prompt. This typicality bias presents a challenge for creative applications that require a wide range of generative outcomes. We identify a fundamental trade-off in current approaches to diversity: modifying model inputs requires costly optimization to incorporate feedback from the generative path. In contrast, acting on spatially-committed intermediate latents tends to disrupt the forming visual structure, leading to artifacts. In this work, we propose to apply repulsion in the Contextual Space as a novel framework for achieving rich diversity in Diffusion Transformers. By intervening in the multimodal attention channels, we apply on-the-fly repulsion during the transformer's forward pass, injecting the intervention between blocks where text conditioning is enriched with emergent image structure. This allows for redirecting the guidance trajectory after it is structurally informed but before the composition is fixed. Our results demonstrate that repulsion in the Contextual Space produces significantly richer diversity without sacrificing visual fidelity or semantic adherence. Furthermore, our method is uniquely efficient, imposing a small computational overhead while remaining effective even in modern "Turbo" and distilled models where traditional trajectory-based interventions typically fail.

ParaSpeechCLAP: A Dual-Encoder Speech-Text Model for Rich Stylistic Language-Audio Pretraining

We introduce ParaSpeechCLAP, a dual-encoder contrastive model that maps speech and text style captions into a common embedding space, supporting a wide range of intrinsic (speaker-level) and situational (utterance-level) descriptors (such as pitch, texture and emotion) far beyond the narrow set handled by existing models. We train specialized ParaSpeechCLAP-Intrinsic and ParaSpeechCLAP-Situational models alongside a unified ParaSpeechCLAP-Combined model, finding that specialization yields stronger performance on individual style dimensions while the unified model excels on compositional evaluation. We further show that ParaSpeechCLAP-Intrinsic benefits from an additional classification loss and class-balanced training. We demonstrate our models' performance on style caption retrieval, speech attribute classification and as an inference-time reward model that improves style-prompted TTS without additional training. ParaSpeechCLAP outperforms baselines on most metrics across all three applications. Our models and code are released at https://github.com/ajd12342/paraspeechclap .

RAD-AI: Rethinking Architecture Documentation for AI-Augmented Ecosystems

AI-augmented ecosystems (interconnected systems where multiple AI components interact through shared data and infrastructure) are becoming the architectural norm for smart cities, autonomous fleets, and intelligent platforms. Yet the architecture documentation frameworks practitioners rely on, arc42 and the C4 model, were designed for deterministic software and cannot capture probabilistic behavior, data-dependent evolution, or dual ML/software lifecycles. This gap carries regulatory consequence: the EU AI Act (Regulation 2024/1689) mandates technical documentation through Annex IV that no existing framework provides structured support for, with enforcement for high-risk systems beginning August 2, 2026. We present RAD-AI, a backward-compatible extension framework that augments arc42 with eight AI-specific sections and C4 with three diagram extensions, complemented by a systematic EU AI Act Annex IV compliance mapping. A regulatory coverage assessment with six experienced software-architecture practitioners provides preliminary evidence that RAD-AI increases Annex IV addressability from approximately 36% to 93% (mean rating) and demonstrates substantial improvement over existing frameworks. Comparative analysis on two production AI platforms (Uber Michelangelo, Netflix Metaflow) captures eight additional AI-specific concerns missed by standard frameworks and demonstrates that documentation deficiencies are structural rather than domain-specific. An illustrative smart mobility ecosystem case study reveals ecosystem-level concerns, including cascading drift and differentiated compliance obligations, that are invisible under standard notation.

SAGAI-MID: A Generative AI-Driven Middleware for Dynamic Runtime Interoperability

Modern distributed systems integrate heterogeneous services, REST APIs with different schema versions, GraphQL endpoints, and IoT devices with proprietary payloads that suffer from persistent schema mismatches. Traditional static adapters require manual coding for every schema pair and cannot handle novel combinations at runtime. We present SAGAI-MID, a FastAPI-based middleware that uses large language models (LLMs) to dynamically detect and resolve schema mismatches at runtime. The system employs a five-layer pipeline: hybrid detection (structural diff plus LLM semantic analysis), dual resolution strategies (per-request LLM transformation and LLM-generated reusable adapter code), and a three-tier safeguard stack (validation, ensemble voting, rule-based fallback). We frame the architecture through Bass et al.'s interoperability tactics, transforming them from design-time artifacts into runtime capabilities. We evaluate SAGAI-MID on 10 interoperability scenarios spanning REST version migration, IoT-to-analytics bridging, and GraphQL protocol conversion across six LLMs from two providers. The best-performing configuration achieves 0.90 pass@1 accuracy. The CODEGEN strategy consistently outperforms DIRECT (0.83 vs 0.77 mean pass@1), while cost varies by over 30x across models with no proportional accuracy gain; the most accurate model is also the cheapest. We discuss implications for software architects adopting LLMs as runtime architectural components.

Stepwise Credit Assignment for GRPO on Flow-Matching Models

Flow-GRPO successfully applies reinforcement learning to flow models, but uses uniform credit assignment across all steps. This ignores the temporal structure of diffusion generation: early steps determine composition and content (low-frequency structure), while late steps resolve details and textures (high-frequency details). Moreover, assigning uniform credit based solely on the final image can inadvertently reward suboptimal intermediate steps, especially when errors are corrected later in the diffusion trajectory. We propose Stepwise-Flow-GRPO, which assigns credit based on each step's reward improvement. By leveraging Tweedie's formula to obtain intermediate reward estimates and introducing gain-based advantages, our method achieves superior sample efficiency and faster convergence. We also introduce a DDIM-inspired SDE that improves reward quality while preserving stochasticity for policy gradients.

Dynamic Dual-Granularity Skill Bank for Agentic RL

Agentic reinforcement learning (RL) can benefit substantially from reusable experience, yet existing skill-based methods mainly extract trajectory-level guidance and often lack principled mechanisms for maintaining an evolving skill memory. We propose D2Skill, a dynamic dual-granularity skill bank for agentic RL that organizes reusable experience into task skills for high-level guidance and step skills for fine-grained decision support and error correction. D2Skill jointly trains the policy and skill bank through paired baseline and skill-injected rollouts under the same policy, using their performance gap to derive hindsight utility signals for both skill updating and policy optimization. Built entirely from training-time experience, the skill bank is continuously expanded through reflection and maintained with utility-aware retrieval and pruning. Experiments on ALFWorld and WebShop with Qwen2.5-7B-Instruct and Qwen3-4B-Instruct-2507 show that D2Skill consistently improves success rates over skill-free baselines by 10-20 points. Further ablations and analyses show that both dual-granularity skill modeling and dynamic skill maintenance are critical to these gains, while the learned skills exhibit higher utility, transfer across evaluation settings, and introduce only modest training overhead.

A Convex Route to Thermomechanics: Learning Internal Energy and Dissipation

We present a physics-based neural network framework for the discovery of constitutive models in fully coupled thermomechanics. In contrast to classical formulations based on the Helmholtz energy, we adopt the internal energy and a dissipation potential as primary constitutive functions, expressed in terms of deformation and entropy. This choice avoids the need to enforce mixed convexity--concavity conditions and facilitates a consistent incorporation of thermodynamic principles. In this contribution, we focus on materials without preferred directions or internal variables. While the formulation is posed in terms of entropy, the temperature is treated as the independent observable, and the entropy is inferred internally through the constitutive relation, enabling thermodynamically consistent modeling without requiring entropy data. Thermodynamic admissibility of the networks is guaranteed by construction. The internal energy and dissipation potential are represented by input convex neural networks, ensuring convexity and compliance with the second law. Objectivity, material symmetry, and normalization are embedded directly into the architecture through invariant-based representations and zero-anchored formulations. We demonstrate the performance of the proposed framework on synthetic and experimental datasets, including purely thermal problems and fully coupled thermomechanical responses of soft tissues and filled rubbers. The results show that the learned models accurately capture the underlying constitutive behavior. All code, data, and trained models are made publicly available via https://doi.org/10.5281/zenodo.19248596.

AdaptToken: Entropy-based Adaptive Token Selection for MLLM Long Video Understanding

Long video understanding remains challenging for Multi-modal Large Language Models (MLLMs) due to high memory costs and context-length limits. Prior approaches mitigate this by scoring and selecting frames/tokens within short clips, but they lack a principled mechanism to (i) compare relevance across distant video clips and (ii) stop processing once sufficient evidence has been gathered. We propose AdaptToken, a training-free framework that turns an MLLM's self-uncertainty into a global control signal for long-video token selection. AdaptToken splits a video into groups, extracts cross-modal attention to rank tokens within each group, and uses the model's response entropy to estimate each group's prompt relevance. This entropy signal enables a global token budget allocation across groups and further supports early stopping (AdaptToken-Lite), skipping the remaining groups when the model becomes sufficiently certain. Across four long-video benchmarks (VideoMME, LongVideoBench, LVBench, and MLVU) and multiple base MLLMs (7B-72B), AdaptToken consistently improves accuracy (e.g., +6.7 on average over Qwen2.5-VL 7B) and continues to benefit from extremely long inputs (up to 10K frames), while AdaptToken-Lite reduces inference time by about half with comparable performance. Project page: https://haozheqi.github.io/adapt-token

Why Aggregate Accuracy is Inadequate for Evaluating Fairness in Law Enforcement Facial Recognition Systems

Facial recognition systems are increasingly deployed in law enforcement and security contexts, where algorithmic decisions can carry significant societal consequences. Despite high reported accuracy, growing evidence demonstrates that such systems often exhibit uneven performance across demographic groups, leading to disproportionate error rates and potential harm. This paper argues that aggregate accuracy is an insufficient metric for evaluating the fairness and reliability of facial recognition systems in high-stakes environments. Through analysis of subgroup-level error distribution, including false positive rate (FPR) and false negative rate (FNR), the paper demonstrates how aggregate performance metrics can obscure critical disparities across demographic groups. Empirical observations show that systems with similar overall accuracy can exhibit substantially different fairness profiles, with subgroup error rates varying significantly despite a single aggregate metric. The paper further examines the operational risks associated with accuracy-centric evaluation practices in law enforcement applications, where misclassification may result in wrongful suspicion or missed identification. It highlights the importance of fairness-aware evaluation approaches and model-agnostic auditing strategies that enable post-deployment assessment of real-world systems. The findings emphasise the need to move beyond accuracy as a primary metric and adopt more comprehensive evaluation frameworks for responsible AI deployment.

AI Models

LiquidAI/LFM2.5-350M

library_name: transformers license: other license_name: lfm1.0 license_link: LICENSE language:

  • en
  • ar
  • zh
  • fr
  • de
  • ja
  • ko
  • es
  • pt pipeline_tag: text-generation tags:
  • liquid
  • lfm2.5
  • edge base_model: LiquidAI/LFM2.5-350M-Base
<div align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png" alt="Liquid AI" style="width: 100%; max-width: 100%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;" /> <div style="display: flex; justify-content: center; gap: 0.5em; margin-bottom: 1em;"> <a href="https://playground.liquid.ai/"><strong>Try LFM</strong></a> • <a href="https://docs.liquid.ai/lfm/getting-started/welcome"><strong>Docs</strong></a> • <a href="https://leap.liquid.ai/"><strong>LEAP</strong></a> • <a href="https://discord.com/invite/liquid-ai"><strong>Discord</strong></a> </div> </div>

LFM2.5-350M

LFM2.5 is a new family of hybrid models designed for on-device deployment. It builds on the LFM2 architecture with extended pre-training and reinforcement learning.

  • Best-in-class performance: A 350M model rivaling much larger models, bringing high-quality AI to your pocket.
  • Fast edge inference: 313 tok/s decode on AMD CPU, 188 tok/s on Snapdragon Gen4. Runs under 1GB of memory with day-one support for llama.cpp, MLX, and vLLM.
  • Scaled training: Extended pre-training from 10T to 28T tokens and large-scale multi-stage reinforcement learning.

Find more information about LFM2.5-350M in our blog post.

[!NOTE] 💻 Demo: https://huggingface.co/spaces/webml-community/lfm2.5-webgpu-summarizer

🗒️ Model Details

| Model | Parameters | Description | |-------|------------|-------------| | LFM2.5-350M-Base | 350M | Pre-trained base model for fine-tuning | | LFM2.5-350M | 350M | General-purpose instruction-tuned model |

LFM2.5-350M is a general-purpose text-only model with the following features:

  • Number of parameters: 350M
  • Number of layers: 16 (10 double-gated LIV convolution blocks + 6 GQA blocks)
  • Training budget: 28T tokens
  • Context length: 32,768 tokens
  • Vocabulary size: 65,536
  • Knowledge cutoff: Mid-2024
  • Languages: English, Arabic, Chinese, French, German, Japanese, Korean, Portuguese, Spanish
  • Generation parameters:
    • temperature: 0.1
    • top_k: 50
    • repetition_penalty: 1.05

| Model | Description | |-------|-------------| | LFM2.5-350M | Original model checkpoint in native format. Best for fine-tuning or inference with Transformers and vLLM. | | LFM2.5-350M-GGUF | Quantized format for llama.cpp and compatible tools. Optimized for CPU inference and local deployment with reduced memory usage. | | LFM2.5-350M-ONNX | ONNX Runtime format for cross-platform deployment. Enables hardware-accelerated inference across diverse environments (cloud, edge, mobile). | | LFM2.5-350M-MLX | MLX format for Apple Silicon. Optimized for fast inference on Mac devices using the MLX framework. |

We recommend using it for data extraction, structured outputs, and tool use. It is not recommended for knowledge-intensive tasks and programming.

Chat Template

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

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

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

Tool Use

LFM2.5 supports function calling as follows:

  1. Function definition: We recommend providing the list of tools as a JSON object in the system prompt. You can also use the tokenizer.apply_chat_template() function with tools.
  2. Function call: By default, LFM2.5 writes Pythonic function calls (a Python list between <|tool_call_start|> and <|tool_call_end|> special tokens), as the assistant answer. You can override this behavior by asking the model to output JSON function calls in the system prompt.
  3. Function execution: The function call is executed, and the result is returned as a "tool" role.
  4. Final answer: LFM2 interprets the outcome of the function call to address the original user prompt in plain text.

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

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

🏃 Inference

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

| Name | Description | Docs | Notebook | |------|-------------|------|:--------:| | Transformers | Simple inference with direct access to model internals. | <a href="https://docs.liquid.ai/lfm/inference/transformers">Link</a> | <a href="https://colab.research.google.com/drive/1_q3jQ6LtyiuPzFZv7Vw8xSfPU5FwkKZY?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> | | vLLM | High-throughput production deployments with GPU. | <a href="https://docs.liquid.ai/lfm/inference/vllm">Link</a> | <a href="https://colab.research.google.com/drive/1VfyscuHP8A3we_YpnzuabYJzr5ju0Mit?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> | | llama.cpp | Cross-platform inference with CPU offloading. | <a href="https://docs.liquid.ai/lfm/inference/llama-cpp">Link</a> | <a href="https://colab.research.google.com/drive/1ohLl3w47OQZA4ELo46i5E4Z6oGWBAyo8?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> | | MLX | Apple's machine learning framework optimized for Apple Silicon. | <a href="https://docs.liquid.ai/lfm/inference/mlx">Link</a> | — | | LM Studio | Desktop application for running LLMs locally. | <a href="https://docs.liquid.ai/lfm/inference/lm-studio">Link</a> | — |

Here's a quick start example with Transformers:

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

model_id = "LiquidAI/LFM2.5-350M"
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,
).to(model.device)

output = model.generate(
    input_ids,
    do_sample=True,
    temperature=0.1,
    top_k=50,
    repetition_penalty=1.05,
    max_new_tokens=512,
    streamer=streamer,
)

🔧 Fine-Tuning

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

| Name | Description | Docs | Notebook | |------|-------------|------|----------| | CPT (Unsloth) | Continued Pre-Training using Unsloth for text completion. | <a href="https://docs.liquid.ai/lfm/fine-tuning/unsloth">Link</a> | <a href="https://colab.research.google.com/drive/10fm7eNMezs-DSn36mF7vAsNYlOsx9YZO?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> | | CPT (Unsloth) | Continued Pre-Training using Unsloth for translation. | <a href="https://docs.liquid.ai/lfm/fine-tuning/unsloth">Link</a> | <a href="https://colab.research.google.com/drive/1gaP8yTle2_v35Um8Gpu9239fqbU7UgY8?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> | | SFT (Unsloth) | Supervised Fine-Tuning with LoRA using Unsloth. | <a href="https://docs.liquid.ai/lfm/fine-tuning/unsloth">Link</a> | <a href="https://colab.research.google.com/drive/1vGRg4ksRj__6OLvXkHhvji_Pamv801Ss?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> | | SFT (TRL) | Supervised Fine-Tuning with LoRA using TRL. | <a href="https://docs.liquid.ai/lfm/fine-tuning/trl">Link</a> | <a href="https://colab.research.google.com/drive/1j5Hk_SyBb2soUsuhU0eIEA9GwLNRnElF?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> | | DPO (TRL) | Direct Preference Optimization with LoRA using TRL. | <a href="https://docs.liquid.ai/lfm/fine-tuning/trl">Link</a> | <a href="https://colab.research.google.com/drive/1MQdsPxFHeZweGsNx4RH7Ia8lG8PiGE1t?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> | | GRPO (Unsloth) | GRPO with LoRA using Unsloth. | <a href="https://docs.liquid.ai/lfm/fine-tuning/unsloth">Link</a> | <a href="https://colab.research.google.com/drive/1mIikXFaGvcW4vXOZXLbVTxfBRw_XsXa5?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> | | GRPO (TRL) | GRPO with LoRA using TRL. | <a href="https://docs.liquid.ai/lfm/fine-tuning/trl">Link</a> | <a href="https://colab.research.google.com/github/Liquid4All/cookbook/blob/main/finetuning/notebooks/grpo_for_verifiable_tasks.ipynb"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |

📊 Performance

Benchmarks

| Model | GPQA Diamond | MMLU-Pro | IFEval | IFBench | Multi-IF | |---|---|---|---|---|---| | LFM2.5-350M | 30.64 | 20.01 | 76.96 | 40.69 | 44.92 | | LFM2-350M | 27.58 | 19.29 | 64.96 | 18.20 | 32.92 | | Granite 4.0-H-350M | 22.32 | 13.14 | 61.27 | 17.22 | 28.70 | | Granite 4.0-350M | 25.91 | 12.84 | 53.48 | 15.98 | 24.21 | | Qwen3.5-0.8B (Instruct) | 27.41 | 37.42 | 59.94 | 22.87 | 41.68 | | Qwen3.5-0.8B (Thinking) | 19.29 | -* | 32.93 | 22.00 | 26.44 | | Gemma 3 1B IT | 23.89 | 14.04 | 63.49 | 20.33 | 44.25 |

| Model | CaseReportBench | BFCLv3 | BFCLv4 | τ²-Bench Telecom | τ²-Bench Retail | |---|---|---|---|---|---| | LFM2.5-350M | 32.45 | 44.11 | 21.86 | 18.86 | 17.84 | | LFM2-350M | 11.67 | 22.95 | 12.29 | 10.82 | 5.56 | | Granite 4.0-H-350M | 12.44 | 43.07 | 13.28 | 13.74 | 6.14 | | Granite 4.0-350M | 0.84 | 39.58 | 13.73 | 2.92 | 6.14 | | Qwen3.5-0.8B (Instruct) | 13.83 | 35.08 | 18.70 | 12.57 | 6.14 | | Qwen3.5-0.8B (Thinking) | 0.39 | 39.64 | 25.39 | 14.33 | 7.02 | | Gemma 3 1B IT | 2.28 | 16.61 | 7.17 | 9.36 | 6.43 |

<i>*Evaluation could not be completed due to doom looping.</i>

CPU Inference

GPU Inference

📬 Contact

Citation

@article{liquidAI2026350M,
  author = {Liquid AI},
  title = {LFM2.5-350M: No Size Left Behind},
  journal = {Liquid AI Blog},
  year = {2026},
  note = {www.liquid.ai/blog/lfm2-5-350m-no-size-left-behind},
}

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

Author: LiquidAI

Likes: 88

Downloads: 2991

Tags: transformers, safetensors, lfm2, text-generation, liquid, lfm2.5, edge, conversational, en, ar, zh, fr, de, ja, ko, es, pt, arxiv:2511.23404, base_model:LiquidAI/LFM2.5-350M-Base, base_model:finetune:LiquidAI/LFM2.5-350M-Base, license:other, endpoints_compatible, region:us

Jackrong/Qwopus3.5-9B-v3-GGUF

language:

  • en
  • zh
  • ko license: apache-2.0 base_model: unsloth/Qwen3.5-9B tags:
  • unsloth
  • qwen
  • qwen3.5
  • reasoning
  • chain-of-thought
  • lora
  • competitive-programming pipeline_tag: image-text-to-text

🌟 Qwopus3.5-9B-v3

💡 Model Introduction

Qwopus3.5-9B-v3 is a reasoning-enhanced model based on Qwen3.5-9B. Its core objective is to simultaneously improve reasoning stability and correctness while optimizing inference efficiency, ultimately achieving stronger cross-task generalization capabilities—particularly in programming.

By continuing to optimize the fundamental structure of its reasoning process alongside high-quality reasoning distillation and structural alignment, it enables the model to achieve higher accuracy rates through shorter, more stable reasoning paths.

🍎 Qwopus3.5-9B-v3: Humaneval Benchmark Evaluation

Inference for models was conducted under the Unsloth runtime environment using bfloat16 (BF16) precision, which provides a balance of numerical range and memory efficiency well-suited to 9B-scale inference. Answer verification, partial chain-of-thought adjudication, and statistical analysis were cross-validated using GPT-4.5-Pro (Thinking) and Claude Opus 4.6 (Thinking) to ensure accuracy and reproducibility of the evaluation outcomes.

HumanEval
I evaluated three 9B-scale Qwen-family models on the full 164-task HumanEval benchmark under a task-level adjudication protocol that resolves code-extraction pollution, answer/code separation issues, and clearly inferable truncated outputs using raw generations. Under this fair and strict evaluation setting, Qwopus3.5-9B-v3 achieves the best base pass@1 of 87.80% (144/164), outperforming both Qwen3.5-9B (82.93%, 136/164) and Claude-Distilled-v2 (82.32%, 135/164). Furthermore, on the stricter plus pass@1 evaluation, Qwopus3.5-9B-v3 also extends its lead to 82.93% (136/164) compared to 77.44% (127/164) for the official baseline (+5.49 pp) and 78.66% (129/164) for the distilled variant.

| Model | Base pass@1 | Plus pass@1 | Rescues (From GPT) | Improvement vs Qwen3.5-9B | |---|---|---|---|---| | Qwopus3.5-9B-v3 | 87.80% (144/164) | 82.93% (136/164) | 1 | 📈 Base: +4.87 pp / Plus: +5.49 pp | | Qwen3.5-9B | 82.93% (136/164) | 77.44% (127/164) | 2 | Baseline | | Claude-Distilled-v2 | 82.32% (135/164) | 78.66% (129/164) | 0 | 📉 Base: -0.61 pp / 📈 Plus: +1.22 pp vs Qwen3.5-9B |

Screenshot 2026-03-31 at 5.44.04 PM

Screenshot 2026-03-31 at 5.44.38 PM

Note: The test results presented here differ from the scores on the 9B-v2 model card because the context length was increased for this evaluation. Consequently, the number of tasks affected by context window truncation has changed for each model, leading to different final scores. Please ensure comparisons are made under the same variable settings.

All post-evaluation standard result files will be uploaded to this repository for transparency and reproducibility. These include:

  • Jackrong_Qwen3.5-9B-Claude-4.6-Opus-Reasoning-Distilled-v2_humaneval_all_evalonly_eval_results
  • Jackrong_Qwopus3.5-9B-v3-test1_humaneval_all_evalonly_eval_results
  • qwen_Qwen3.5-9B_humaneval_all_evalonly_eval_results

⚠️ Note on evaluation artifacts.
The released result files are based on raw model generations, which may contain formatting issues (e.g., Markdown wrappers, answer/code mixing), truncation, or minor token-level corruption.

🏃 Qwopus3.5-9B-v3: MMLU-Pro Benchmark Evaluation

I evaluated on 280 MMLU-Pro questions across the following domains: Biology, Chemistry, Computer Science, Health, Mathematics, Physics, and Other Sciences.

All question IDs are identical across both model runs.

Accuracy

| Model | Correct | Total | Accuracy | |------------------|--------|-------|----------| | Qwen3.5-9B | 225 | 280 | 80.36% | | Qwopus3.5-9B-v3 | 229 | 280 | 81.79% |

Result:
Qwopus3.5-9B-v3 leads by +1.43 pp

Reasoning Efficiency

| Metric | Qwen3.5-9B | Qwopus3.5-9B-v3 | |--------|------------|--------------| | Avg think length | 7116 chars | 5313 chars | | Passes / 10k chars | 1.26 | 1.66 | | Chars / correct pass | 7938 | 6032 |

Reasoning Efficiency Improvements

  • −25.3% shorter reasoning
  • +31.7% higher efficiency
  • −24.0% lower cost per correct answer

Screenshot 2026-03-31 at 5.52.15 PM

Evaluation Summary

While the overall accuracy margin (+1.43 pp) is modest, Qwopus3.5-9B-v3 fundamentally shifts the accuracy-cost paradigm, achieving its victory while spending significantly less reasoning budget. With a 25.3% reduction in mean think length and 24.0% lower token cost per correct answer, this iteration is highly optimized for latency, token budget, and context pressure.

Furthermore, across the mixed domain profile, Qwopus3.5-9B-v3 uniquely offsets Qwen3.5-9B's slight edge in biology, CS, and math by excelling in physics, chemistry, and significantly lowering its unfinished-output rate. Its final rank benefits as much from raw correctness as from an improved ability to cleanly and reliably complete analytical boundaries.

🗺️ Training Pipeline Overview

Base Model (Qwen3.5-9B)
 │
 ▼
Qwen3.5-9B fine-tuned with Unsloth
 │
 ▼
Supervised Fine-Tuning (SFT) + LoRA
(Response-Only Training masked on "<|im_start|>assistant\n<think>")
 │
 ▼
Qwopus3.5-9B-v3

🧠 Example of Learned Reasoning Scaffold

The model includes targeted optimizations addressing Qwen3.5's tendency toward excessive or repetitive reasoning on simple queries. By distilling the structured reasoning habits of top-tier models like Claude Opus, Qwopus3.5-9B-v3 adopts a highly organized, step-by-step cognitive layout.

Example：The user is asking about [Topic] and how it differs from [Topic B]. This is a [Task type] question. Let me break this down:

1. What is [Topic A]?
   - [Fact/Mechanism 1]
   - [Fact/Mechanism 2]
2. What is [Topic B]?
   - [Fact/Mechanism 1]
3. Key differences:
   - [Comparison Point 1]
   - [Comparison Point 2]

Let me make sure to be accurate: [...]
Actually, I should double-check: is [Fact] used before [Fact]? Yes, typically...
Let me provide a clear, well-structured answer:

📚 Training Data

The model was fine-tuned on a high-fidelity reasoning dataset, which was meticulously curated from a blend of premium open-source sources on Hugging Face. This dataset is the result of a rigorous mixing and cleaning process, specifically designed to filter out low-quality responses and ensure consistently strong logical performance across diverse analytical domains.

(Rest assured, the entire process is strictly by-the-book and 100% compliant with all terms and open-source licenses!)

⚠️ 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.

This qwen3_5 model was trained 2x faster with Unsloth and Huggingface's TRL library.

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

📖 Citation

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

@misc{jackrong_qwen35_9b_v3
  title        = {Jackrong/Qwopus3.5-9B-v3},
  author       = {Jackrong},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Jackrong/Qwopus3.5-9B-v3-GGUF}}
}

Author: Jackrong

Likes: 26

Downloads: 0

Tags: gguf, qwen3_5, unsloth, qwen, qwen3.5, reasoning, chain-of-thought, lora, competitive-programming, image-text-to-text, en, zh, ko, base_model:unsloth/Qwen3.5-9B, base_model:adapter:unsloth/Qwen3.5-9B, license:apache-2.0, endpoints_compatible, region:us, conversational

FINAL-Bench/Darwin-35B-A3B-Opus

license: apache-2.0 base_model:

  • Qwen/Qwen3.5-35B-A3B
  • Jackrong/Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled tags:
  • merge
  • evolutionary-merge
  • darwin
  • darwin-v5
  • model-mri
  • reasoning
  • advanced-reasoning
  • chain-of-thought
  • thinking
  • qwen3.5
  • qwen
  • moe
  • mixture-of-experts
  • claude-opus
  • distillation
  • multimodal
  • vision-language
  • multilingual
  • 201-languages
  • gpqa
  • benchmark
  • open-source
  • apache-2.0
  • natural-selection
  • layer-wise-merge
  • moe-merge
  • dead-expert-revival
  • neural-anatomy
  • coding-agent
  • tool-calling
  • long-context
  • 262k-context language:
  • en
  • zh
  • ko
  • ja
  • de
  • fr
  • es
  • ru
  • ar
  • multilingual pipeline_tag: text-generation library_name: transformers model-index:
  • name: Darwin-35B-A3B-Opus results:
    • task: type: text-generation name: Graduate-Level Reasoning dataset: type: Idavidrein/gpqa name: GPQA Diamond config: gpqa_diamond split: train metrics:
      • type: accuracy value: 90.0 name: Accuracy verified: false
    • task: type: text-generation name: Multilingual Knowledge dataset: type: openai/MMMLU name: MMMLU metrics:
      • type: accuracy value: 85.0 name: Accuracy verified: false

Darwin-35B-A3B-Opus

<p align="center"> <img src="info.png" alt="Darwin-35B-A3B-Opus" width="100%"> </p> <p align="center"> <a href="https://huggingface.co/FINAL-Bench/Darwin-35B-A3B-Opus"><img src="https://img.shields.io/badge/🤗_Model-Darwin--35B--A3B--Opus-blue" alt="Model"></a> <a href="https://huggingface.co/spaces/FINAL-Bench/Darwin-35B-A3B-Opus"><img src="https://img.shields.io/badge/🚀_Space-Live_Demo-purple" alt="Space"></a> <a href="https://huggingface.co/spaces/FINAL-Bench/Leaderboard"><img src="https://img.shields.io/badge/🏆_FINAL_Bench-Leaderboard-green" alt="FINAL Bench"></a> <a href="https://huggingface.co/spaces/FINAL-Bench/all-bench-leaderboard"><img src="https://img.shields.io/badge/📊_ALL_Bench-Leaderboard-orange" alt="ALL Bench"></a> </p> <p align="center"> <em>"The child surpassed both parents — that is evolution."</em> </p> <!-- SEO: Structured Summary for Search Engines & AI Answer Engines --> <!-- Darwin-35B-A3B-Opus is a 35B parameter Mixture-of-Experts (MoE) language model with 3B active parameters, created by VIDRAFT using the Darwin V5 evolutionary merge engine with Model MRI integration. It achieves 90.0% on GPQA Diamond (vs Father Qwen3.5-35B-A3B at 84.2%) and 85.0% on MMMLU, while preserving multimodal capabilities (image/video), 201 language support, and 262K context length. Licensed under Apache 2.0. -->

TL;DR: 35B MoE (3B active) | GPQA Diamond 90.0% (beats Father 84.2% & Mother 85.0%) | MMMLU 85.0% | Multimodal ✅ | 201 Languages | 262K Context | 147.8 tok/s | Apache 2.0

#Darwin #EvolutionaryMerge #ModelMRI #Qwen3.5 #MoE #Reasoning #GPQA90 #Multimodal #OpenSource #Apache2 #DarwinV5 #VIDRAFT

Why Darwin? — The Child That Surpassed Both Parents

The fundamental question of AI model merging: If parent models already exist, why crossbreed?

This model is the answer.

Benchmark Results

GPQA Diamond (198 Questions, Graduate-Level Reasoning)

| Model | Accuracy | Multimodal | Benchmark Published | |---|---|---|---| | 🧬 Darwin-35B-A3B-Opus (Child) | 90.0% | ✅ Image/Video | ✅ Fully Open | | 👩 Mother — Jackrong Claude 4.6 Opus Distilled | 85.0% | ❌ Text-only | ❌ Not Published | | 👨 Father — Qwen3.5-35B-A3B (Official) | 84.2% | ✅ Image/Video | ✅ Official |

Evaluation: SGLang, context 32768, temperature 0, greedy decoding, official GPQA prompt format ("ANSWER: LETTER")

MMMLU (Multilingual Knowledge, 29 Languages)

| Model | Accuracy | |---|---| | 🧬 Darwin-35B-A3B-Opus (Child) | 85.0% | | 👨 Father — Qwen3.5-35B-A3B (Official) | 85.2% |

Darwin maintains Father-level multilingual knowledge while gaining superior reasoning.

The child surpassed both parents in reasoning, and matched the Father in multilingual knowledge.

  • GPQA vs Father: +6.9% relative improvement ((90.0−84.2)/84.2)
  • GPQA vs Mother: +5.9% relative improvement ((90.0−85.0)/85.0)
  • MMMLU: 85.0% — Father-level (85.2%) multilingual knowledge preserved

Why Not Just Use the Mother?

| | Mother (Claude Distilled) | Darwin (Child) | |---|---|---| | Reasoning | Strong (85.0%) | Stronger (90.0%) | | Image/Video | ❌ Lost (text-only fine-tune) | ✅ Inherited from Father | | 201 Languages | ❌ Potentially degraded | ✅ Inherited from Father | | 262K Context | Unverified | ✅ Father's architecture preserved | | Benchmark Transparency | ❌ No scores published | ✅ Fully open |

Why Not Just Use the Father?

The Father (Qwen3.5-35B-A3B) excels in versatility but scores 84.2% on hard reasoning. Darwin pushes reasoning to 90.0% while maintaining Father-level multilingual knowledge (MMMLU 85.0% vs 85.2%) and all general capabilities.

Conclusion: The only model that surpasses the Mother's reasoning, preserves the Father's multilingual knowledge, and retains full multimodal capabilities.

Model Overview

Darwin-35B-A3B-Opus is a next-generation reasoning-enhanced language model created by VIDRAFT's Darwin V5 evolution engine.

Darwin V5 combines two innovations:

  1. Evolutionary Merge — Applies natural selection to automatically find optimal weight combinations
  2. Model MRI Integration — CT-scans parent models layer by layer before merging, guiding evolution with structural insight

If conventional merging is "mixing recipes blindfolded," Darwin V5 is "precision surgery with X-ray guidance."

Parent Models

| Role | Model | Strengths | |---|---|---| | 👨 Father | Qwen/Qwen3.5-35B-A3B | General knowledge, multimodal (image/video), coding, agents, 201 languages, 262K context | | 👩 Mother | Jackrong/Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled | Claude 4.6 Opus CoT distillation, structured step-by-step reasoning, coding agent compatibility |

Darwin V5 — Beyond Simple Merge

Limitations of Conventional Merging

Traditional model merging relies on humans setting hyperparameters like ratio and density by intuition. Set ratio=0.5, density=0.9, run once, and hope for the best. The result depends on luck, and applying the same ratio uniformly across billions of parameters ignores each layer's unique role.

Darwin V4's Advance

Darwin V4 solved this with evolutionary algorithms — automatically searching hundreds of parameter combinations and selecting survivors by real benchmark scores. But V4 was still blind evolution: it didn't know what each layer does.

Darwin V5: Model MRI Opens the Eyes

V5 integrates Model MRI (neural anatomy analyzer) to give evolution "sight":

[Phase 0] Model MRI — CT-scan both parents layer by layer
    ↓  "Father's layers 15-25 concentrate multilingual knowledge"
    ↓  "Mother's layers 30-40 concentrate reasoning patterns"
    ↓
[Phase 1] MRI-Guided Evolution — Start from scan-informed initial genome
    ↓  Not random, but "informed by CT results"
    ↓
[Phase 2] mergekit real merge + benchmark fitness selection
    ↓  Faster convergence in MRI-narrowed search space
    ↓
[Phase 3] MRI Health Check — CT-scan the child model
    ↓  Detect interference, function loss
    ↓  Prescribe layer-specific ratio adjustments
    ↓
[Final] Darwin-35B-A3B-Opus

V4 vs V5

| | Darwin V4 | Darwin V5 | |---|---|---| | Analogy | Mixing recipes blindfolded | Precision surgery with X-ray | | Initial genome | Random | MRI-guided | | Layer control | 2 ratios (attn/ffn) | 40 layers independently | | Pre-diagnosis | ❌ None | ✅ Phase 0 MRI scan | | Post-verification | Benchmark only | ✅ Phase 3 health check | | Search efficiency | Wide space | Narrowed, guided search | | Failure diagnosis | Unknown "why" | Pinpoint which layer failed |

Darwin V4: Discovered Optimal Parameters (Blind Evolution)

| Parameter | Value | Meaning | |---|---|---| | ratio | 0.481 | Father 52% : Mother 48% asymmetric blend | | density_a | 0.855 | Selected 85.5% of Father's weights | | density_b | 0.971 | Adopted 97.1% of Mother's weights | | attn | 0.168 | Only 16.8% change in attention layers | | ffn | 0.841 | 84.1% change in FFN layers |

Interpretation: Attention patterns (what to focus on) are almost entirely preserved from the Father, while FFN layers (knowledge storage) are largely replaced with the Mother's reasoning patterns.

Discovering attn=0.168 and ffn=0.841 — this extreme asymmetry — is virtually impossible by human intuition.

Darwin V5: MRI-Guided Merge Recipe

After scanning both parents, Model MRI generated a fundamentally different prescription:

<p align="center"><img src="a2.png" width="500" alt="MRI-Guided Genome"></p>

| Parameter | V4 (Blind) | V5 (MRI) | Change | |---|---|---|---| | global_ratio | 0.481 | 0.800 | Mother weight ↑↑ | | attn_ratio | 0.168 | 0.320 | Attention also shifts to Mother | | ffn_ratio | 0.841 | 0.590 | FFN becomes more conservative | | density_a | 0.855 | 0.799 | Similar | | density_b | 0.971 | 0.799 | Mother density ↓ (Dead Expert compensation) |

Key insight: MRI prescribed "use more of the Mother (ratio 0.8), but reduce density (0.799) because 50-65% of her experts are dead." V4 found ratio=0.481 blindly — the opposite direction.

Layer-Wise Merge Strategy (3 Blocks)

MRI didn't apply uniform ratios. It split 40 layers into 3 blocks:

<p align="center"><img src="a1.png" width="700" alt="Merge Ratio + Parent Importance + MoE Health per Layer"></p>

| Block | Layers | t (Mother %) | Router Source | Rationale | |---|---|---|---|---| | Block 1 | L0~L37 | 59.9% | Mother | Reasoning pattern injection across most layers | | Block 2 | L38 | 90.0% | Mother | Golden Layer — Mother's reasoning engine core | | Block 3 | L39 | 53.4% | Father | Output layer — Father's router preserves multimodal routing |

L38 is the "Golden Layer": Mother's MRI showed peak cosine distance at L34~L38 (see Mother MRI below). Darwin V5 responded by assigning t=0.9 to L38 — transplanting the Mother's reasoning engine almost entirely.

Model MRI Scans — Parent Neural Anatomy

Mother MRI: Claude 4.6 Opus Distilled

<p align="center"><img src="m3.png" width="600" alt="Mother Probe Cosine Distance"></p>

Probe-wise Layer Importance: L34~L38 shows intense red (high cosine distance) across REASONING, CODE, LOGIC probes — this is the Mother's reasoning engine.

<p align="center"><img src="m1.png" width="500" alt="Mother MoE Health"></p>

| Metric | Status | Interpretation | |---|---|---| | Router Entropy | ✅ ~1.0 across all layers | Healthy — experts evenly distributed | | Dead Expert % | 🔴 50~65% | Critical — Claude distillation killed half the experts | | Expert Similarity | ✅ 0.001~0.008 | Healthy — surviving experts remain diverse |

Dead Expert 50~65% is the fingerprint of Claude text-only distillation. The fine-tuning killed multimodal and multilingual experts that were no longer activated during text-only training.

<p align="center"><img src="m2.png" width="600" alt="Mother Expert Utilization"></p>

Expert Utilization Heatmap: Mostly dark (inactive) with sparse bright activations — the Claude reasoning pattern is concentrated in a small number of specialized experts.

Father MRI: Healthy Generalist (Organ Donor)

<p align="center"><img src="f1.png" width="500" alt="Father MoE Health"></p> <p align="center"><img src="f2.png" width="600" alt="Father Expert Utilization"></p> <p align="center"><img src="f3.png" width="600" alt="Father Layer Importance by Probe"></p>

The Father (Qwen3.5-35B-A3B) shows healthy, uniform expert activation across all 40 layers — a well-balanced generalist with all experts alive. This is the "organ donor" that revives the Mother's dead 50–65% experts.

Parent Comparison: Layer Advantage Map

<p align="center"><img src="a3.png" width="600" alt="Parent A vs B Layer Advantage"></p>
  • Above zero (↑ A): Father stronger — primarily L0~L5 (embedding/early layers)
  • Below zero (↓ B): Mother stronger — scattered but consistent across L5~L35
  • L34~L38: Mother shows strongest advantage in REASONING and CODE probes
  • L39: Father recovers — output layer favors Father's multimodal routing

This advantage map directly informed the 3-block merge recipe: Mother dominates L0~L38, Father retakes L39.

How GPQA 90% Was Achieved

Mother L34~L38 reasoning engine (MRI red zone)
    ↓ t=0.9 — transplanted almost entirely
    +
Father L39 output router (multimodal/multilingual expert activation)
    ↓ t=0.53 — Father's routing preserved
    +
Dead Expert replacement → Father's living experts fill Mother's dead slots
    ↓
= GPQA 90.0% (surpassed both parents)

The Mother's "reasoning brain" was transplanted while her dead experts were replaced with the Father's living ones. Reasoning went up, versatility was preserved.

Evolution History

  • Phase 1 → Phase 2 evolution complete
  • Final real_score: 0.8405
  • Merge time: 181.6 seconds
  • Merge commit: 109838c2

Model MRI Health Check — Child vs Parents

<p align="center"> <img src="c1.png" alt="Darwin Health Check — Child vs Parents" width="100%"> </p>

✅ Health: Healthy — No issues detected.

The chart above shows the layer-by-layer importance of the child (Darwin, green bars) compared to both parents (Father = blue dashed, Mother = red dashed). Key findings:

Layer 0 (Embedding): Child importance spikes to 0.42 — both parents show similar peaks (~0.35–0.50). The child successfully inherited the critical embedding layer from both parents without interference.

Layers 1–33 (Middle): Near-zero importance across all three models. This is normal — middle layers in MoE models process information incrementally, with no single layer being critical. The child tracks both parents perfectly, confirming no function loss in the bulk of the network.

Layers 34–39 (Reasoning Engine): Importance rises sharply. This is the region where Mother's MRI showed intense reasoning activity (cosine distance > 0.6). The child's green bars match or exceed both parents — proving that Mother's reasoning patterns were successfully transplanted while Father's output routing was preserved.

Layer 39 (Output): Child peaks at ~0.48, closely matching both parents. The final output layer is intact.

Why This Matters

The MRI health check confirms three things:

  1. No interference — No layer where child importance abnormally exceeds parents (which would indicate weight conflict)
  2. No function loss — No layer where parents had high importance but child dropped to zero
  3. Successful transplant — L34–L39 reasoning engine from Mother is fully operational in the child

Darwin V5 MRI-Guided Merge Recipe

# MRI-guided layer-wise merge (3 blocks)
# Genome: ratio=0.800 attn=0.320 ffn=0.590 density=0.799

L0–L37:  t=0.5988 (Mother 60%) — router from Mother
L38:     t=0.9000 (Mother 90%) — "Golden Layer" reasoning core
L39:     t=0.5336 (Father 47%) — router from Father (output routing)

| Insight | Detail | |---|---| | L38 = "Golden Layer" | MRI identified L34–L38 as Mother's reasoning core. Darwin assigned t=0.9 (90% Mother) to L38 specifically | | Router Strategy: B→B→A | Mother's router for reasoning layers, Father's router for final output — preserves both reasoning paths and multimodal routing | | Dead Expert Revival | Mother's 50–65% dead experts (killed by text-only fine-tuning) were replaced with Father's live experts — restoring multimodal and multilingual capabilities |

Inherited Capabilities

From Father (Qwen3.5-35B-A3B)

  • Multimodal: Image and video understanding
  • 201 Languages: Global linguistic coverage
  • 262K Context: Native long-context (extendable to 1M via YaRN)
  • Gated DeltaNet + MoE: Efficient hybrid architecture
  • Multi-Token Prediction: Improved inference throughput

From Mother (Claude 4.6 Opus Distilled)

  • Structured Thinking: Systematic step-by-step reasoning within <think> tags
  • Efficient Reasoning: "Let me analyze this request carefully: 1..2..3..." pattern
  • Coding Agent Compatibility: Native "developer" role support for Claude Code, OpenCode
  • Tool Calling Stability: Consistent performance in tool-use scenarios
  • Autonomous Execution: Extended autonomous operation in agentic environments

Father's Official Benchmarks (Reference)

Darwin is built on this architecture with enhanced reasoning:

| Category | Benchmark | Father Official | |---|---|---| | Knowledge | MMLU-Pro | 85.3 | | Knowledge | MMLU-Redux | 93.3 | | Reasoning | GPQA Diamond | 84.2 | | Reasoning | HLE w/ CoT | 22.4 | | Math | HMMT Feb 2025 | 89.0 | | Coding | SWE-bench Verified | 69.2 | | Coding | LiveCodeBench v6 | 74.6 | | Agent | TAU2-Bench | 81.2 | | Agent | BFCL-V4 (Tool Use) | 67.3 | | Instruction | IFEval | 91.9 | | Multilingual | MMMLU | 85.2 | | Agentic Search | BrowseComp | 61.0 |

Performance

Inference Speed

| Metric | Value | |---|---| | Generation Speed | 147.8 tok/s | | Environment | Single NVIDIA H100 93GB NVL, SGLang, BF16 | | Qwen Official API | 162.8 tok/s (Alibaba Cloud) |

Hardware Requirements

| Setup | VRAM | Status | |---|---|---| | BF16 (Full Precision) | 65.5 GiB | | | Single H100 93GB NVL | 93 GB | ✅ Comfortable | | Single A100 80GB | 80 GB | ⚠️ Tight | | Single A100 40GB | 40 GB | ❌ Insufficient | | Q8 Quantized | ~35 GiB | | | Single A100 40GB | 40 GB | ✅ Possible | | Q4_K_M Quantized | ~18 GiB | | | Single RTX 4090 24GB | 24 GB | ✅ Comfortable | | 2× RTX 4090 (tp=2) | 48 GB | ✅ BF16 possible |

As a Mixture-of-Experts model, only 3B parameters are active per token despite loading the full 35B. Quantization has minimal impact due to this sparsity.

Model Specifications

| | | |---|---| | Architecture | Qwen3.5 MoE (Gated DeltaNet + MoE) | | Total Parameters | 35B | | Active Parameters | 3B per forward pass | | Hidden Dimension | 2,048 | | Layers | 40 | | Layer Layout | 10 × (3 × GDN→MoE + 1 × Attention→MoE) | | Experts | 256 (8 routed + 1 shared active) | | Expert Intermediate Dim | 512 | | Context Length | 262,144 native (up to 1,010,000 via YaRN) | | Languages | 201 | | Multimodal | ✅ Image & Video input | | License | Apache 2.0 | | Engine | Darwin V5 (Evolutionary Merge + Model MRI) | | Evolution Phase | Phase 2, real_score 0.8405 | | Merge Commit | 109838c2 |

Usage

SGLang (Recommended)

python -m sglang.launch_server \
  --model-path FINAL-Bench/Darwin-35B-A3B-Opus \
  --tp 1 \
  --mem-fraction-static 0.90 \
  --context-length 32768 \
  --trust-remote-code

vLLM

vllm serve FINAL-Bench/Darwin-35B-A3B-Opus \
  --trust-remote-code \
  --enforce-eager

Transformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FINAL-Bench/Darwin-35B-A3B-Opus", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    "FINAL-Bench/Darwin-35B-A3B-Opus",
    dtype="bfloat16",
    device_map="auto",
    trust_remote_code=True,
)

Best Practices

  • Use context ≥ 32K for reasoning tasks — the model leverages extended thinking
  • For maximum reasoning quality, use thinking mode (default) with sufficient max_tokens (≥ 16384)
  • The model generates <think> blocks for internal reasoning; extract the final answer after </think>

Built By

| | | |---|---| | Developer | VIDRAFT | | Evolution Engine | Darwin V5 (Evolutionary Merge + Model MRI) | | Infrastructure | 4 × NVIDIA H100 93GB NVL GPU | | Merge Time | 181.6 seconds | | Shard Distribution | 14 shards → GPU [1, 2, 3] round-robin |

Acknowledgements

  • Korean Government — This research was supported by the Korean Government's 'GPU Support Program' research grant
  • Qwen Team — Qwen3.5-35B-A3B base architecture
  • Jackrong — Claude 4.6 Opus Reasoning Distilled model
  • nohurry, TeichAI — Distillation datasets

Citation

@misc{vidraft_darwin_35b_opus,
  title        = {Darwin-35B-A3B-Opus: MRI-Guided Evolutionary Merge Beyond Both Parents},
  author       = {VIDRAFT},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/FINAL-Bench/Darwin-35B-A3B-Opus}}
}

Contact

📧 kkms1116@koreacu.ac.kr

FAQ (Frequently Asked Questions)

<details> <summary><b>What is Darwin-35B-A3B-Opus?</b></summary> Darwin-35B-A3B-Opus is a 35 billion parameter Mixture-of-Experts language model (3B active per token) that was created using evolutionary merge techniques. It combines Qwen3.5-35B-A3B's multimodal versatility with Claude 4.6 Opus reasoning distillation, achieving 90.0% on GPQA Diamond — surpassing both parent models. </details> <details> <summary><b>How does Darwin V5 differ from simple model merging?</b></summary> Traditional merging applies uniform ratios by guesswork. Darwin V5 uses evolutionary algorithms (natural selection) combined with Model MRI (neural CT-scanning) to automatically discover optimal layer-specific merge ratios. For example, it found attn=0.168 and ffn=0.841 — an extreme asymmetry impossible to find by intuition. </details> <details> <summary><b>What GPU do I need to run this model?</b></summary> For BF16 full precision: A100 80GB (tight) or H100 93GB (comfortable). For Q4 quantization: a single RTX 4090 (24GB) is sufficient. The model loads 35B parameters but only activates 3B per token due to its MoE architecture. </details> <details> <summary><b>Does it support multimodal (images/video)?</b></summary> Yes. Darwin inherits the Father model's (Qwen3.5-35B-A3B) full multimodal capabilities including image and video understanding, unlike the Mother model which lost this during text-only fine-tuning. </details> <details> <summary><b>What languages does it support?</b></summary> 201 languages and dialects, inherited from Qwen3.5's multilingual training. MMMLU benchmark confirms 85.0% multilingual knowledge retention across 29 evaluated languages. </details> <details> <summary><b>What is Model MRI?</b></summary> Model MRI is a neural anatomy analysis tool that CT-scans each layer of a language model to understand what functions it performs. When integrated with Darwin, it guides the evolutionary merge process — telling the algorithm which layers to preserve from each parent and which to replace. In this model, MRI identified L38 as the Mother's "golden layer" (core reasoning engine) and prescribed 90% Mother weight for that specific layer. </details> <details> <summary><b>What are "Dead Experts" and why does it matter?</b></summary> In Mixture-of-Experts (MoE) models, each layer contains hundreds of specialist sub-networks (experts). The Mother model's Claude distillation killed 50–65% of these experts because text-only fine-tuning didn't activate multimodal/multilingual specialists. Darwin's MRI detected this and prescribed replacing dead experts with the Father's living ones — reviving capabilities the Mother lost. </details> <details> <summary><b>Is this model open source?</b></summary> Yes. Darwin-35B-A3B-Opus is released under the Apache 2.0 license, fully open for commercial and research use. </details>
<!-- AEO: Keywords for AI Answer Engines --> <!-- Keywords: Darwin-35B-A3B-Opus, evolutionary merge, model merging, Darwin V5, Model MRI, GPQA Diamond 90%, Qwen3.5-35B-A3B, Claude 4.6 Opus, reasoning model, mixture of experts, MoE 3B active, 35B parameters, multimodal LLM, 201 languages, 262K context, open source AI model, Apache 2.0, VIDRAFT, natural selection AI, layer-wise merge ratio, attention preservation, FFN replacement, best open source reasoning model 2026, Qwen merge, coding agent compatible, dead expert revival, golden layer L38, MoE merge technique, neural anatomy analysis, router entropy, expert utilization heatmap, cosine distance probe, 3-block surgical merge -->

#DarwinAI #EvolutionaryMerge #ModelMRI #DarwinV5 #GPQA90 #Qwen35 #MoE3B #Reasoning #Multimodal #201Languages #OpenSource #Apache2 #VIDRAFT #NaturalSelection #LayerWiseMerge #ClaudeOpus #ThinkingModel #CodingAgent #LongContext262K #BestOpenSourceLLM2026 #DeadExpertRevival #GoldenLayer #MoEMerge #NeuralAnatomy

Author: FINAL-Bench

Likes: 15

Downloads: 0

Tags: transformers, safetensors, qwen3_5_moe, image-text-to-text, merge, evolutionary-merge, darwin, darwin-v5, model-mri, reasoning, advanced-reasoning, chain-of-thought, thinking, qwen3.5, qwen, moe, mixture-of-experts, claude-opus, distillation, multimodal, vision-language, multilingual, 201-languages, gpqa, benchmark, open-source, apache-2.0, natural-selection, layer-wise-merge, moe-merge, dead-expert-revival, neural-anatomy, coding-agent, tool-calling, long-context, 262k-context, text-generation, conversational, en, zh, ko, ja, de, fr, es, ru, ar, base_model:Jackrong/Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled, base_model:merge:Jackrong/Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled, base_model:Qwen/Qwen3.5-35B-A3B, base_model:merge:Qwen/Qwen3.5-35B-A3B, license:apache-2.0, model-index, endpoints_compatible, region:us

Winnougan/Qwen-3.5-Abliterated-Comfyui-nvfp4

license: apache-2.0

🧠 Qwen 3.5 Abliterated for ComfyUI (MXFP8 & NVFP4)

Welcome! This repository provides ComfyUI-ready, abliterated versions of Qwen 3.5, optimized for local AI workflows, assistants, and multimodal use inside ComfyUI.

📦 Quantized Model Files

| Model | Precision & Notes | Approx Size | |-------|------------------|------------| | Heretical‑Qwen3.5‑9B‑fp8.safetensors | FP8 quantized ablated Qwen‑3.5 9B | 11.9 GB | | qwen3.5_9b_abliterated_nvfp4.safetensors | NVFP4 quantized ablated Qwen‑3.5 9B | 8.36 GB | | Qwen3.5‑4B‑heretic‑fp8.safetensors | FP8 quantized ablated Qwen‑3.5 4B | 5.51 GB | | qwen3.5_4b_nvfp4.safetensors | NVFP4 quantized Qwen‑3.5 4B | 3.54 GB | | qwen3.5_4b_claude46opus_abliterated_mxfp8mixedfp8.safetensors | Mixed FP8 ablated (Claude‑4.6+Opus style) | 5.91 GB | | qwen3.5_4b_claude46opus_abliterated_nvfp4.safetensors | NVFP4 variant of the above | 3.54 GB |

Example of simple workflow using Qwen 3.5 as an image assistant⬇️⬇️⬇️⬇️⬇️ <img src="https://huggingface.co/Winnougan/Qwen-3.5-Abliterated-Comfyui-nvfp4/resolve/main/Prompt_God.png">

Example of simple workflow plus image captioning⬇️⬇️⬇️⬇️⬇️ <img src="https://huggingface.co/Winnougan/Qwen-3.5-Abliterated-Comfyui-nvfp4/resolve/main/Caption_God.png">

🚀 Overview These models are based on Qwen 3.5, a powerful multilingual LLM family designed for reasoning, coding, and general AI tasks.

This release includes:

🔓 Abliterated variants (uncensored / no refusal behavior) ⚡ MXFP8 & NVFP4 quantizations 🧩 Native ComfyUI compatibility

Abliteration removes built-in refusal behavior without retraining, preserving most of the model’s original capabilities while enabling unrestricted outputs. ✨ Features 🧠 Full AI assistant inside ComfyUI 🖼️ Image understanding (multimodal support) ⚡ Fast inference with low VRAM usage 🔌 Plug-and-play with standard ComfyUI nodes

These models can: Answer questions Generate prompts Assist workflows Analyze images directly inside ComfyUI pipelines ⚙️ Quantization Types 🔹 MXFP8 Balanced performance and quality Works on a wide range of GPUs Ideal default option 🔹 NVFP4 Ultra-low precision (4-bit) Massive VRAM reduction and speed gains Best suited for newer NVIDIA architectures Designed for efficient deployment of LLMs with minimal memory footprint 🧩 ComfyUI Integration

✅ These models load directly using:

CLIP Loader (standard node)

No special loaders required.

📦 Installation Update ComfyUI to the latest version Download the model file (MXFP8 or NVFP4)

Place it in:

ComfyUI/models/clip/ Load it using CLIP Loader 🎬 Workflow

👉 A workflow is provided in this repo to help you get started.

It demonstrates:

AI assistant usage Prompt generation Image interpretation

Highly recommended to download and test the workflow to understand full capabilities.

🧠 What is Abliteration?

Abliteration is a technique that:

Removes refusal/alignment layers Keeps original model intelligence intact Does not require retraining

Result: 👉 More freedom 👉 Same core performance

💡 Use Cases 🎥 Prompt generation for video models (LTX, WAN, etc.) 🧩 ComfyUI automation assistant 🖼️ Image captioning & interpretation ✍️ Creative writing / uncensored outputs 🧠 Local AI copilots ⚠️ Notes Abliterated models are community-created, not official releases Use responsibly depending on your application NVFP4 may require newer GPUs for best performance ❤️ Credits Base model: Qwen Team Quantization & conversion: community efforts ComfyUI integration: ongoing community development 🔥 Final Thoughts

If you want a fully local AI assistant embedded directly inside ComfyUI, this setup is one of the most powerful workflows available right now.

Author: Winnougan

Likes: 13

Downloads: 0

Tags: license:apache-2.0, region:us

LiquidAI/LFM2.5-350M-Base

library_name: transformers license: other license_name: lfm1.0 license_link: LICENSE language:

  • en
  • ar
  • zh
  • fr
  • de
  • ja
  • ko
  • es
  • pt pipeline_tag: text-generation tags:
  • liquid
  • lfm2.5
  • edge
<div align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png" alt="Liquid AI" style="width: 100%; max-width: 100%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;" /> <div style="display: flex; justify-content: center; gap: 0.5em; margin-bottom: 1em;"> <a href="https://playground.liquid.ai/"><strong>Try LFM</strong></a> • <a href="https://docs.liquid.ai/lfm/getting-started/welcome"><strong>Docs</strong></a> • <a href="https://leap.liquid.ai/"><strong>LEAP</strong></a> • <a href="https://discord.com/invite/liquid-ai"><strong>Discord</strong></a> </div> </div>

LFM2.5-350M-Base

LFM2.5 is a new family of hybrid models designed for on-device deployment. It builds on the LFM2 architecture with extended pre-training and reinforcement learning.

Find more information about LFM2.5-350M in our blog post.

🗒️ Model Details

| Model | Parameters | Description | |-------|------------|-------------| | LFM2.5-350M-Base | 350M | Pre-trained base model for fine-tuning | | LFM2.5-350M | 350M | General-purpose instruction-tuned model |

LFM2.5-350M is a general-purpose text-only model with the following features:

  • Number of parameters: 350M
  • Number of layers: 16 (10 double-gated LIV convolution blocks + 6 GQA blocks)
  • Training budget: 28T tokens
  • Context length: 32,768 tokens
  • Vocabulary size: 65,536
  • Knowledge cutoff: Mid-2024
  • Languages: English, Arabic, Chinese, French, German, Japanese, Korean, Portuguese, Spanish

This pre-trained checkpoint is only recommended for tasks that require heavy fine-tuning, like language-specific (e.g., Japanese) or domain-specific (e.g., medical) assistants, training on proprietary data, or experimenting with novel post-training approaches.

🏃 Inference

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

| Name | Description | Docs | Notebook | |------|-------------|------|:--------:| | Transformers | Simple inference with direct access to model internals. | <a href="https://docs.liquid.ai/lfm/inference/transformers">Link</a> | <a href="https://colab.research.google.com/drive/1_q3jQ6LtyiuPzFZv7Vw8xSfPU5FwkKZY?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> | | vLLM | High-throughput production deployments with GPU. | <a href="https://docs.liquid.ai/lfm/inference/vllm">Link</a> | <a href="https://colab.research.google.com/drive/1VfyscuHP8A3we_YpnzuabYJzr5ju0Mit?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> | | llama.cpp | Cross-platform inference with CPU offloading. | <a href="https://docs.liquid.ai/lfm/inference/llama-cpp">Link</a> | <a href="https://colab.research.google.com/drive/1ohLl3w47OQZA4ELo46i5E4Z6oGWBAyo8?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> | | MLX | Apple's machine learning framework optimized for Apple Silicon. | <a href="https://docs.liquid.ai/lfm/inference/mlx">Link</a> | — | | LM Studio | Desktop application for running LLMs locally. | <a href="https://docs.liquid.ai/lfm/inference/lm-studio">Link</a> | — |

Here's a quick start example with Transformers:

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

model_id = "LiquidAI/LFM2.5-350M-Base"
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,
).to(model.device)

output = model.generate(
    input_ids,
    do_sample=True,
    temperature=0.1,
    top_k=50,
    repetition_penalty=1.05,
    max_new_tokens=512,
    streamer=streamer,
)

🔧 Fine-Tuning

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

| Name | Description | Docs | Notebook | |------|-------------|------|----------| | CPT (Unsloth) | Continued Pre-Training using Unsloth for text completion. | <a href="https://docs.liquid.ai/lfm/fine-tuning/unsloth">Link</a> | <a href="https://colab.research.google.com/drive/10fm7eNMezs-DSn36mF7vAsNYlOsx9YZO?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> | | CPT (Unsloth) | Continued Pre-Training using Unsloth for translation. | <a href="https://docs.liquid.ai/lfm/fine-tuning/unsloth">Link</a> | <a href="https://colab.research.google.com/drive/1gaP8yTle2_v35Um8Gpu9239fqbU7UgY8?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> | | SFT (Unsloth) | Supervised Fine-Tuning with LoRA using Unsloth. | <a href="https://docs.liquid.ai/lfm/fine-tuning/unsloth">Link</a> | <a href="https://colab.research.google.com/drive/1vGRg4ksRj__6OLvXkHhvji_Pamv801Ss?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> | | SFT (TRL) | Supervised Fine-Tuning with LoRA using TRL. | <a href="https://docs.liquid.ai/lfm/fine-tuning/trl">Link</a> | <a href="https://colab.research.google.com/drive/1j5Hk_SyBb2soUsuhU0eIEA9GwLNRnElF?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> | | DPO (TRL) | Direct Preference Optimization with LoRA using TRL. | <a href="https://docs.liquid.ai/lfm/fine-tuning/trl">Link</a> | <a href="https://colab.research.google.com/drive/1MQdsPxFHeZweGsNx4RH7Ia8lG8PiGE1t?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> | | GRPO (Unsloth) | GRPO with LoRA using Unsloth. | <a href="https://docs.liquid.ai/lfm/fine-tuning/unsloth">Link</a> | <a href="https://colab.research.google.com/drive/1mIikXFaGvcW4vXOZXLbVTxfBRw_XsXa5?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> | | GRPO (TRL) | GRPO with LoRA using TRL. | <a href="https://docs.liquid.ai/lfm/fine-tuning/trl">Link</a> | <a href="https://colab.research.google.com/github/Liquid4All/cookbook/blob/main/finetuning/notebooks/grpo_for_verifiable_tasks.ipynb"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |

📬 Contact

Citation

@article{liquidAI2026350M,
  author = {Liquid AI},
  title = {LFM2.5-350M: No Size Left Behind},
  journal = {Liquid AI Blog},
  year = {2026},
  note = {www.liquid.ai/blog/lfm2-5-350m-no-size-left-behind},
}

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

Author: LiquidAI

Likes: 7

Downloads: 0

Tags: transformers, safetensors, lfm2, text-generation, liquid, lfm2.5, edge, conversational, en, ar, zh, fr, de, ja, ko, es, pt, arxiv:2511.23404, license:other, endpoints_compatible, region:us

byteshape/Qwen3.5-9B-GGUF

library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3.5-9B/blob/main/LICENSE pipeline_tag: image-text-to-text base_model:

  • Qwen/Qwen3.5-9B tags:
  • qwen3.5
  • byteshape

Qwen3.5-9B GGUF (ShapeLearn Quantized)

This is a GGUF-quantized version of Qwen3.5-9B produced with ByteShape's ShapeLearn, which learns the optimal datatype per tensor to maintain high quality even at very low bitlengths.

To learn more about ShapeLearn and to see detailed benchmarks across GPUs, CPUs, and even the Raspberry Pi, please visit our blog.

If you have questions or want to share feedback, reach us on Reddit.

Quick Start

Pick a model from the tables below and click Get llama.cpp command to get a ready-to-run command with all the correct sampling parameters for this model.

You can also copy the Model Tag from the table and use it directly:

| Tool | Command | |------|---------| | llama.cpp | llama-server -hf <MODEL_TAG> --mmproj-auto |

This is a vision capable model. llama.cpp auto-downloads the model and vision projector on first run.

Once you run the llama-server, you can access the web interface at http://localhost:<PORT>.

Note on Ollama: As of this release, Ollama does not support Qwen3.5-9B based on Llama.cpp GGUFs. We suggest using llama.cpp or LM Studio as an alternative.

How to Pick a Model

We provide CPU and GPU optimized variants for llama.cpp:

  • GPUs: optimized with a hybrid approach combining KQ and IQ quantization for better throughput.
  • CPUs: optimized with predominantly KQ quantization.

Each hardware target includes a range of models covering different size and quality tradeoffs.

The chart below shows quality versus tokens per second (TPS), with Unsloth used as the baseline for comparison. Quality is measured across seven benchmarks, including function calling: BFCL-V3, LiveCodeBench V6, HumanEval, GSM8K, IFEVAL and MMLU, and GSM8K_V in both thinking and instruct modes.

Selection rule: Choose the model with the highest quality at your target throughput or the fastest model that still meets your required quality.

GPU Models

Interactive plots for RTX 5090, 4080, 3090, 5060Ti are available here.

GPU Benchmark - RTX 5090

Table sorted by model size (match the chart numbers to model IDs):

| Model ID | Bits/Weight | Model Size | Use This Model | Model Tag | |---------|-------------|-----------|-----|-----------| | GPU-1 | 2.81 | 3.15 GB | Get llama.cpp command | byteshape/Qwen3.5-9B-GGUF:Qwen3.5-9B-IQ3_S-2.81bpw.gguf | | GPU-2 | 3.00 | 3.37 GB | Get llama.cpp command | byteshape/Qwen3.5-9B-GGUF:Qwen3.5-9B-IQ3_S-3.00bpw.gguf | | GPU-3 | 3.15 | 3.53 GB | Get llama.cpp command | byteshape/Qwen3.5-9B-GGUF:Qwen3.5-9B-IQ3_S-3.15bpw.gguf | | GPU-4 | 3.60 | 4.04 GB | Get llama.cpp command | byteshape/Qwen3.5-9B-GGUF:Qwen3.5-9B-IQ4_XS-3.60bpw.gguf | | GPU-5 | 4.20 | 4.71 GB | Get llama.cpp command | byteshape/Qwen3.5-9B-GGUF:Qwen3.5-9B-IQ4_XS-4.20bpw.gguf | | GPU-6 | 4.43 | 4.97 GB | Get llama.cpp command | byteshape/Qwen3.5-9B-GGUF:Qwen3.5-9B-IQ4_XS-4.43bpw.gguf | | GPU-7 | 5.10 | 5.72 GB | Get llama.cpp command | byteshape/Qwen3.5-9B-GGUF:Qwen3.5-9B-Q5_K_S-5.10bpw.gguf |

CPU Models

Interactive plots for Ryzen 9 5900X, Intel Core i7 12700KF, Intel Ultra 7 265KF, and Raspberry Pi 5 are available here. CPU Benchmark - Ryzen 9 5900X

Table sorted by model size (match the chart numbers to model IDs):

| Model ID | Bits/Weight | Model Size | Use This Model | Model Tag | |---------|-------------|-----------|-----|-----------| | CPU-1 | 2.81 | 3.15 GB | Get llama.cpp command | byteshape/Qwen3.5-9B-GGUF:Qwen3.5-9B-IQ3_S-2.81bpw.gguf | | CPU-2 | 3.00 | 3.37 GB | Get llama.cpp command | byteshape/Qwen3.5-9B-GGUF:Qwen3.5-9B-IQ3_S-3.00bpw.gguf | | CPU-3 | 3.15 | 3.53 GB | Get llama.cpp command | byteshape/Qwen3.5-9B-GGUF:Qwen3.5-9B-IQ3_S-3.15bpw.gguf | | CPU-4 | 3.46 | 3.88 GB | Get llama.cpp command | byteshape/Qwen3.5-9B-GGUF:Qwen3.5-9B-Q3_K_S-3.46bpw.gguf | | CPU-5 | 3.60 | 4.04 GB | Get llama.cpp command | byteshape/Qwen3.5-9B-GGUF:Qwen3.5-9B-IQ4_XS-3.60bpw.gguf | | CPU-6 | 3.92 | 4.4 GB | Get llama.cpp command | byteshape/Qwen3.5-9B-GGUF:Qwen3.5-9B-Q4_K_S-3.92bpw.gguf | | CPU-7 | 4.20 | 4.71 GB | Get llama.cpp command | byteshape/Qwen3.5-9B-GGUF:Qwen3.5-9B-IQ4_XS-4.20bpw.gguf | | CPU-8 | 4.60 | 5.16 GB | Get llama.cpp command | byteshape/Qwen3.5-9B-GGUF:Qwen3.5-9B-Q5_K_S-4.60bpw.gguf | | CPU-9 | 4.75 | 5.32 GB | Get llama.cpp command | byteshape/Qwen3.5-9B-GGUF:Qwen3.5-9B-Q5_K_S-4.75bpw.gguf | | CPU-10 | 5.10 | 5.72 GB | Get llama.cpp command | byteshape/Qwen3.5-9B-GGUF:Qwen3.5-9B-Q5_K_S-5.10bpw.gguf |

Notes on quantization labels

The labels you see (for example IQ4_XS) are only there to make Hugging Face show our models in the GGUF table. We do not use the conventional quantization profiles as defined in llama.cpp. In our case, these labels indicate the primary quantization approach and average bit length. Note that both KQ and IQ models may use a mix of quantization techniques optimized for their target hardware, which is why several models can share the same tag.

Author: byteshape

Likes: 6

Downloads: 0

Tags: transformers, gguf, qwen3.5, byteshape, image-text-to-text, base_model:Qwen/Qwen3.5-9B, base_model:quantized:Qwen/Qwen3.5-9B, license:apache-2.0, endpoints_compatible, region:us, conversational

Jackrong/MLX-Qwopus3.5-9B-v3-4bit

language:

  • en
  • zh
  • ko license: apache-2.0 base_model: Jackrong/Qwopus3.5-9B-v3 tags:
  • unsloth
  • qwen
  • qwen3.5
  • reasoning
  • chain-of-thought
  • lora
  • competitive-programming
  • mlx pipeline_tag: text-generation library_name: mlx

Jackrong/MLX-Qwopus3.5-9B-v3-4bit

This model Jackrong/MLX-Qwopus3.5-9B-v3-4bit was converted to MLX format from Jackrong/Qwopus3.5-9B-v3 using mlx-lm version 0.30.7.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("Jackrong/MLX-Qwopus3.5-9B-v3-4bit")

prompt = "hello"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True, return_dict=False,
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)

Author: Jackrong

Likes: 6

Downloads: 0

Tags: mlx, safetensors, qwen3_5, unsloth, qwen, qwen3.5, reasoning, chain-of-thought, lora, competitive-programming, text-generation, conversational, en, zh, ko, base_model:Jackrong/Qwopus3.5-9B-v3, base_model:adapter:Jackrong/Qwopus3.5-9B-v3, license:apache-2.0, 4-bit, region:us

Jundot/Qwen3.5-35B-A3B-oQ4e

library_name: mlx tags:

  • mlx
  • oq
  • quantized

[!IMPORTANT] This quantization was uploaded on 2026-03-31 and replaces a previous version. If you downloaded this model before this date, please re-download for the updated weights.

Qwen3.5-35B-A3B-oQ4e

This model was quantized using oQ (oMLX v0.3.1) mixed-precision quantization.

Quantization details

  • Model type: qwen3_5_moe
  • Bits: 4
  • Group size: 64
  • Format: MLX safetensors

Author: Jundot

Likes: 5

Downloads: 0

Tags: mlx, safetensors, qwen3_5_moe, oq, quantized, 4-bit, region:us

tencent/Sequential-Hidden-Decoding-8B-n8-Instruct

license: other license_name: sequential-hidden-decoding license_link: LICENSE base_model:

  • tencent/Sequential-Hidden-Decoding-8B-n8
  • Qwen/Qwen3-8B-Base tags:
  • sequential-hidden-decoding
  • instruct
  • text-generation
  • conversational

Sequential-Hidden-Decoding-8B-n8-Instruct

This is the instruction-tuned variant of Sequential Hidden Decoding 8B n=8, designed for conversational and instruction-following use cases.

Key Idea

Sequential Hidden Decoding scales sequence length by preparing multiple Embedding matrices for the same token sequence, interleaving the results, and feeding the expanded sequence into the same Transformer. This model is the instruction-tuned release of the 8B n=8 variant.

Serving (SGLang)

This model requires a patched version of SGLang for inference. See the project page for installation options.

python -m sglang.launch_server \
    --model-path tencent/Sequential-Hidden-Decoding-8B-n8-Instruct \
    --trust-remote-code \
    --tp-size 1 \
    --port 30000 --host 0.0.0.0 \
    --chunked-prefill-size -1 \
    --attention-backend fa3 \
    --mem-fraction-static 0.82 \
    --max-running-requests 32 \
    --context-length 131072 \
    --cuda-graph-max-bs 128 \
    --cuda-graph-bs 1 2 4 8 16 32 64 128

Note: Sequential Hidden Decoding models process n×-length sequences internally, so --chunked-prefill-size -1, --attention-backend fa3, and conservative batch sizing are important for stability and performance.

Chat Usage

This is an instruction-tuned model. Use the /v1/chat/completions endpoint:

from openai import OpenAI

client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")
response = client.chat.completions.create(
    model="tencent/Sequential-Hidden-Decoding-8B-n8-Instruct",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain the idea of hidden decoding in simple terms."},
    ],
    max_tokens=512,
    temperature=0.7,
)
print(response.choices[0].message.content)

Files

This repository includes the custom architecture files required by trust_remote_code:

  • configuration_qwen3_scale_seq.py
  • modeling_qwen3_scale_seq.py

Related Models

| Model | Type | Notes | |-------|:----:|-------| | Sequential-Hidden-Decoding-8B-n2 | Base | 2x scale base model | | Sequential-Hidden-Decoding-8B-n4 | Base | 4x scale base model | | Sequential-Hidden-Decoding-8B-n8 | Base | 8x scale base model | | Sequential-Hidden-Decoding-8B-n8-Instruct | Instruct | Instruction-tuned 8x scale model |

Citation

@article{hidden_decoding_2026,
  title   = {Hidden Decoding: Scaling Sequence Length in Pretraining},
  year    = {2026},
  url     = {https://welm.weixin.qq.com/posts/hidden_decoding/}
}

License

This model is released under the License Terms of Sequential-Hidden-Decoding.

Author: tencent

Likes: 4

Downloads: 0

Tags: safetensors, qwen3_scale_seq, sequential-hidden-decoding, instruct, text-generation, conversational, custom_code, base_model:Qwen/Qwen3-8B-Base, base_model:finetune:Qwen/Qwen3-8B-Base, license:other, region:us

ReadyArt/Omega-Evolution-27B-v2.2-GGUF

base_model:

  • ReadyArt/Omega-Evolution-27B-v2.2 base_model_relation: quantized tags:
  • nsfw
  • explicit
  • roleplay
  • unaligned
  • dangerous
  • ERP
  • Other License license: apache-2.0
<style> :root { --primary-glow: #ff4d00; /* Danger Orange */ --secondary-glow: #00ffcc; /* Cyber Cyan */ --dark-bg: #050505; --card-bg: #111111; --text-main: #e0e0e0; --text-muted: #a0a0a0; --danger: #ff0000; } body { font-family: 'Courier New', monospace; /* Typewriter feel for that "classified" vibe */ background-color: var(--dark-bg); color: var(--text-main); margin: 0; padding: 0; overflow-x: hidden; cursor: crosshair; /* Weaponized cursor */ perspective: 1000px; } /* CRT Scanline Overlay */ body::after { content: ""; position: fixed; top: 0; left: 0; width: 100vw; height: 100vh; background: repeating-linear-gradient( 0deg, rgba(0, 0, 0, 0.15), rgba(0, 0, 0, 0.15) 1px, transparent 1px, transparent 2px ); pointer-events: none; z-index: 9999; animation: flicker 0.15s infinite; } @keyframes flicker { 0% { opacity: 0.9; } 50% { opacity: 1; } 100% { opacity: 0.95; } } .container { max-width: 900px; margin: 0 auto; padding: 40px 20px; background: radial-gradient(circle at center, #1a1a1a 0%, #000000 100%); 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 V2.2 😈</p> <p class="subtitle">⚠️ 27B Parameters ⚠️</p> </div> <div class="waifu-container"> <img src="https://huggingface.co/spaces/ReadyArt/README/resolve/main/VORTEX.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 v0.45.</li> <li><strong>CONTENT WARNING:</strong> NSFW, Explicit, ERP, and Unaligned behavior are enabled by default.</li> <li><strong>DATASET CHANGES:</strong> EM Dash has been nuked from our dataset.</li> <li><strong>TRAINING CHANGES:</strong> This is a 2 epoch tune.</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>min_p</span> <span class="spec-value">0.02</span> </div> <div class="spec-card"> <span>top_p</span> <span class="spec-value">0.98</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://cdn-avatars.huggingface.co/v1/production/uploads/64ceaecdc9d00e3847c7ae7c/8Te2teNBt8Jw_LjOIV7x4.png" alt="ConicCat" class="avatar"> <span>ToastyPigeon <span style="font-size:0.7em; color:#888;">(Base Model)</span></span> </li> <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: 3

Downloads: 0

Tags: gguf, nsfw, explicit, roleplay, unaligned, dangerous, ERP, Other License, base_model:ReadyArt/Omega-Evolution-27B-v2.2, base_model:quantized:ReadyArt/Omega-Evolution-27B-v2.2, license:apache-2.0, endpoints_compatible, region:us, conversational