Open-Weight Models Under Apache License 2.0

Model · Text generation

MoA-150M

Convergent Intelligence

A compact-but-capable ≈150M parameter causal LM that replaces dot-product attention with metric-native attention and augments sequence geometry with BlackHoleRoPE (a learnable, stable RoPE variant). Designed to train and run on modest hardware (CPU-first friendly) while staying fully compatible with • Distance scores, not dot products. Heads score with L2, cosine, or diag-Mahalanobis distances. This gives direct control over geometry, often stabilizes training, and can be more sample-efficient. • BlackHoleRoPE positional encoding. • Q/K: pure unit-modulus rotation (unitary → numerically stable). • V: bounded-energy gating (Penrose-inspired), optionally modulated by a discrepancy signal. •…

Model · Text generation

MoA-100M

Convergent Intelligence

A geometry‑aware Transformer that mixes several attention mechanisms and routes them with a metric‑based router. MoA replaces the classic dot‑product attention with metric‑based attention and blends four distinct heads per Transformer block: A token‑wise router decides, for each token, which head(s) to use and applies feature‑gates (FiLM‑style) and router‑bias gates for up/down‑scaling. The FFN is a HyperFFN – three parallel branches (SwiGLU MLP, separable‑conv, low‑rank) combined by a branch router. LayerScale and optional DropPath keep training stable. Triangle‑inequality (TI) penalty on sampled triples to encourage true‑metric behaviour. Ball pruning – each head learns an origin \(oh\)…

Model · Text generation

Dualmind-Qwen-1.7B-Thinking

Convergent Intelligence

Claude Opus 4.6 Reasoning Traces → 1.7B via DualMind SFT A 1.7B model trained on 2.5M+ tokens of Claude Opus 4.6 reasoning traces using the DualMind SFT methodology. The training data comes from Opus-4.6-Reasoning-3000x-filtered — a curated dataset of extended reasoning chains from Anthropic's most capable model, with refusals removed. This is the Opus variant of the DualMind family. Where the base DualMind model was trained on LogicInference data, this model absorbs the reasoning patterns of Claude Opus 4.6 — longer chains, more nuanced self-correction, and richer deliberative structure. The Opus teacher produces qualitatively different reasoning than synthetic logic datasets: it…

Model · Text generation

DualMinded-Qwen3-1.7B

Convergent Intelligence

A 1.7B parameter dual-cognition model trained on Opus 4.6 reasoning traces. The model implements a three-phase cognitive loop — explore, examine, respond — where it reasons freely, critiques its own reasoning, then synthesizes a clean answer. This is the multi-model collision array collapsed into a single architecture. The dialectical structure that produces novel insights from architectural diversity is recreated through role-conditioned generation on shared weights. No extra parameters, no routing — same weights, different cognitive modes. DualMinded-Qwen3-1.7B is the product of a four-stage pipeline: Stage 1 — Multi-Teacher Distillation: Qwen3-30B-A3B in three variants (Instruct…

Model · Text generation

LFM2.5-1.2B-Distilled-SFT

Convergent Intelligence

A 1.2B hybrid model (SSM + attention) built in two stages: knowledge distillation from a 24B MoE hybrid teacher on STEM chain-of-thought data, then supervised fine-tuning on logical inference. The first proof-weighted distillation + SFT pipeline on a non-transformer architecture. Liquid Foundation Models run at 239 tok/s on AMD CPU and fit under 1GB of RAM. This model adds structured STEM reasoning and formal logical inference to that efficiency substrate. LFM2.5-1.2B distilled from LFM2-24B-A2B — a 24B MoE hybrid (SSM + attention) with only 2B active parameters per token. Teacher and student share the LFM hybrid architecture, so the KL divergence transfers reasoning patterns between…

Model · Text generation

GPT-X2-125M-CIx-Long-Context

Convergent Intelligence

This model is a custom-code derivative of AxiomicLabs/GPT-X2-125M, adapted for experimental long-context causal language modeling and architecture research. The repository includes a Hugging Face Transformers-compatible GPT-X2 implementation with optional Symplectic Metric-RoPE Governor support and training utilities built around CIxOpt, a heterogeneous optimizer developed for efficient parameter routing across large projection matrices, sensitive normalization parameters, and optional governor modules. The model is intended as a research checkpoint for compact long-context generation, positional encoding experiments, optimizer testing, and continued fine-tuning. This implementation uses a…

Model · Text generation

DualMind

Convergent Intelligence

Single Architecture, Dual Cognition — The Multi-Model Collision Array on Shared Weights DualMind is a 1.7B parameter model that implements dual-mental-modality reasoning — a single model with two internal voices sharing the same weights, differentiated only by role tokens: - — Unconstrained reasoning. Derivation, speculation, working through the problem freely. - — Adversarial self-response. The model reads its own explore output and critiques it. Error detection, verification, refinement. - — Clean synthesis. The final answer distilled from the internal dialogue. This is the multi-model collision array collapsed into a single architecture. The dialectical structure that produces novel…

Model · Summarization

bigbird-pegasus-large-pubmed

Google

BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle. BigBird was introduced in this paper and first released in this repository. Disclaimer: The team releasing BigBird did not write a model card for this model so this model card has been written by the Hugging Face team. BigBird relies on block sparse attention instead of normal attention (i.e. BERT's attention) and can handle sequences up to a length of 4096 at a much lower compute cost compared to BERT. It has achieved SOTA…

Model · Image segmentation

sae-2026-hang-all-yolo26n-seg-v2-960

Black Bee Drones

Instance-segmentation model for the "hang the wire" hook mission of the SAE Brasil Eletroquad 2026 competition, trained by Black Bee Drones. It segments two classes - rose (each visible segment of the suspended rope) and sphere (the orange sphere on the rope) - and runs on every control-loop tick of the mission. The team finished 2nd overall The drone takes off, finds the orange sphere mounted on one of two suspended ropes, parks a fixed distance from it, picks which side of the rope to fly along, turns perpendicular to the rope, descends on LIDAR, releases a hook with a servo, and lands. Because each visible rope segment is its own rose instance, the controller can measure both rope arms…

Model · Summarization

bert-mini2bert-mini-finetuned-cnn_daily_mail-summarization

Manuel Romero

This model is a warm-started BERT2BERT (mini) model fine-tuned on the CNN/Dailymail summarization dataset. The model achieves a 16.51 ROUGE-2 score on CNN/Dailymail's test dataset. For more details on how the model was fine-tuned, please refer to this notebook.

Model · Zero-shot classification

nli-deberta-base

Sentence Transformers - Cross-Encoders

This model was trained using SentenceTransformers Cross-Encoder class. The model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral. For evaluation results, see SBERT.net - Pretrained Cross-Encoder. Pre-trained models can be used like this: You can use the model also directly with Transformers library (without SentenceTransformers library): This model can also be used for zero-shot-classification

Model · Fill mask

barthez

Moussa Kamal Eddine

A french sequence to sequence pretrained model based on BART. BARThez is pretrained by learning to reconstruct a corrupted input sentence. A corpus of 66GB of french raw text is used to carry out the pretraining. Unlike already existing BERT-based French language models such as CamemBERT and FlauBERT, BARThez is particularly well-suited for generative tasks (such as abstractive summarization), since not only its encoder but also its decoder is pretrained. In addition to BARThez that is pretrained from scratch, we continue the pretraining of a multilingual BART mBART which boosted its performance in both discriminative and generative tasks. We call the french adapted version mBARThez.

Model · Robotics

act_aloha_sim_transfer_cube_human

LeRobot

Action Chunking Transformer Policy (as per Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware) trained for the AlohaTransferCube environment from gym-aloha. See the LeRobot library (particularly the evaluation script) for instructions on how to load and evaluate this model. Trained with LeRobot@3c0a209. The model was trained using LeRobot's training script and with the alohasimtransfercubehuman dataset, using this command: The training curves may be found at https://wandb.ai/aliberts/lerobot/runs/720l37xb. The current model corresponds to the checkpoint at 80k steps. This took about 1h45 to train on an Nvida A100. The model was evaluated on the AlohaTransferCube task from…

Model · Robotics

diffusion_pusht

LeRobot

Diffusion Policy (as per Diffusion Policy: Visuomotor Policy Learning via Action Diffusion) trained for the PushT environment from gym-pusht. See the LeRobot library (particularly the evaluation script) for instructions on how to load and evaluate this model. Trained with LeRobot@3c0a209. The model was trained using LeRobot's training script and with the pusht dataset, using this command: The training curves may be found at https://wandb.ai/aliberts/lerobot/runs/s7elvf4r. The current model corresponds to the checkpoint at 175k steps. The model was evaluated on the PushT environment from gym-pusht and compared to a similar model trained with the original Diffusion Policy code. There are two…

Model · Zero-shot classification

deberta-base-long-nli

Tasksource

deberta-v3-base with context length of 1280 fine-tuned on tasksource for 250k steps. I oversampled long NLI tasks (ConTRoL, doc-nli). Training data include helpsteer v1/v2, logical reasoning tasks (FOLIO, FOL-nli, LogicNLI...), OASST, hh/rlhf, linguistics oriented NLI tasks, tasksource-dpo, fact verification tasks. This checkpoint has strong zero-shot validation performance on many tasks (e.g. 70% on WNLI), and can be used for: - Zero-shot entailment-based classification for arbitrary labels [ZS]. - Natural language inference [NLI] - Further fine-tuning on a new task or tasksource task (classification, token classification, reward modeling or multiple-choice) [FT]. Zero-shot GPT-4 scores…

This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Writes checkpoints to outputs/train/ /checkpoints/. Prefix the dataset repo with eval\ and supply --policy.path pointing to a local or hub checkpoint.

π₀.₅ (Pi05) is a Vision-Language-Action model from Physical Intelligence designed for open-world generalization: it evolves π₀ to generalize to entirely new environments and situations that were never seen during training. The LeRobot implementation is adapted from their open-source OpenPI repository. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot pi05 guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and…

Paper: Zheng et al., 2025, “X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model” (arXiv:2510.10274) Successful generalist Vision-Language-Action (VLA) models rely on effective training across diverse robotic platforms with large-scale, cross-embodiment, heterogeneous datasets. To facilitate and leverage the heterogeneity in rich robotic data sources, X-VLA introduces a Soft Prompt approach with minimally added parameters: we infuse prompt-learning concepts into cross-embodiment robot learning, introducing separate sets of learnable embeddings for each distinct embodiment. These embodiment-specific prompts empower VLA models to exploit cross-embodiment…

Model · Any to any

MirilAI_Miril-Drone-2B-1-GGUF

Bartowski

Using llama.cpp release b10068 for quantization. All quants made using imatrix option with dataset from here Run them in your choice of tools: Note: if it's a newly supported model, you may need to wait for an update from the developers. Some of these quants (Q3KXL, Q4KL etc) are the standard quantization method with the embeddings and output weights quantized to Q80 instead of what they would normally default to. First, make sure you have huggingface-cli installed: Then, you can target the specific file you want: If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: You can either specify a new local-dir…

Model

DistilQwen3-1.7B-uncensored-GGUF

Convergent Intelligence

Part of the DistilQwen3 Series by Convergent Intelligence LLC: Research Division This is a GGUF-quantized variant. The mathematical foundations (Discrepancy Calculus, Topological Knowledge Distillation) are documented in the source model's card. The discrepancy operator $Df(x)$ and BV decomposition that inform the training pipeline are preserved through quantization — the structural boundaries detected by DISC during training are baked into the weights, not dependent on precision. DistilQwen Collection — Our only BF16 series. Proof-weighted distillation from Qwen3-30B-A3B → 1.7B and 0.6B on H100. Three teacher variants (Instruct, Thinking, Coder), nine models, 2,788 combined downloads. The…

Model · Text generation

LFM2.5-2.6B-CyberSec

Convergent Intelligence

An English LFM2.5 2.6B checkpoint associated with the Trendyol Cybersecurity Instruction Tuning Dataset. This repository contains both Transformers-format model files and local GGUF exports. This is a research release. The repository does not currently publish benchmark, baseline-comparison, or safety-evaluation results. Transformers assets include model.safetensors, configuration files, tokenizer files, and a chat template. Keeping both formats in one repository is convenient, but users should explicitly choose the path that matches their runtime. The checked configuration includes a bitsandbytes quantization block. Pin and test the exact Transformers, Accelerate, bitsandbytes, and device…

Model · Text generation

CasualSwarms

Convergent Intelligence

SAGI is a novel causal language model that integrates swarm intelligence dynamics with transformer architecture. The model treats cognition as a dynamic, adaptive system where multiple internal "agents" collaborate through differentiable routing, trust mechanisms, and shared memory. The enhancements were integrated with the existing AGI system through: 1. Compatibility Layer: Ensuring new components work with existing AGI Core 2. Unified State Representation: Combining enhanced capabilities with existing state 3. Enhanced Continuous Learning: Upgrading the learning system with new capabilities 4. Performance Monitoring: Tracking improvements through validation systems - Successfully…

static quants of https://huggingface.co/Locutusque/OpenCerebrum-2.0-7B weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here are Artefact2's thoughts on the matter…

Model · Text generation

Qwen3-1.7B-Coder-Distilled-SFT-GGUF

Convergent Intelligence

GGUF quantizations of reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-SFT for local and edge deployment via llama.cpp and compatible runtimes. Coder teacher → STEM distillation → logical inference SFT → quantized. Structured reasoning in ~1.2GB. Stage 1 — Coder Teacher Distillation: Qwen3-1.7B distilled from Qwen3-Coder-30B-A3B-Instruct on 6,122 STEM CoT samples. Proof-weighted cross-entropy (2.5x → 1.5x on derivation tokens) + KL divergence at T=2.0. The Coder teacher transfers structured decomposition patterns — sequential logic, state tracking, compositional reasoning — through the softmax landscape. Stage 2 — Logical Inference SFT: Fine-tuned on KonstantinDob/logicinferencedataset (~54,607…

Model · Zero-shot classification

gliclass-small-v1.0

Knowledgator Engineering

This is an efficient zero-shot classifier inspired by GLiNER work. It demonstrates the same performance as a cross-encoder while being more compute-efficient because classification is done at a single forward path. It can be used for topic classification, sentiment analysis and as a reranker in RAG pipelines. The model was trained on synthetic data and can be used in commercial applications. First of all, you need to install GLiClass library: Than you need to initialize a model and a pipeline: Below, you can see the F1 score on several text classification datasets. All tested models were not fine-tuned on those datasets and were tested in a zero-shot setting. Below you can find a comparison…

Model · Video classification

videoprism-base-f16r288

Google

Blog: https://research.google/blog/videoprism-a-foundational-visual-encoder-for-video-understanding/ VideoPrism is a foundational video encoder that enables state-of-the-art performance on a large variety of video understanding tasks. It takes video frames as input and outputs compact embeddings of the frames, which one can conveniently feed into classifiers, LLMs, retrieval models, etc. When tested on 33 public video understanding benchmarks over four task categories, a single frozen VideoPrism checkpoint outperforms previous best-performing foundation models on 31 of them, with no fine-tuning on target task datasets. We release the following model variants: VideoPrism-B/L are the…

Model · Text generation

SMOLM2Prover-GGUF

Convergent Intelligence

GGUF quantized version of the SMOLM2Prover model for use with llama.cpp and compatible runtimes. Create a Modelfile: 1. Download SMOLM2Prover-Q4KM.gguf 3. Load and chat! The Q4KM quantization uses: This model is part of the Convergent Intelligence LLC: Research Division portfolio. All models in this portfolio are developed under the Discrepancy Calculus (DISC) framework — a measure-theoretic approach to understanding and controlling the gap between what a model should produce and what it actually produces. DISC treats training singularities (loss plateaus, mode collapse, catastrophic forgetting) not as failures to be smoothed over, but as structural signals that reveal the geometry of the…

Model · Video classification

videoprism-lvt-large-f8r288

Google

Blog: https://research.google/blog/videoprism-a-foundational-visual-encoder-for-video-understanding/ VideoPrism is a foundational video encoder that enables state-of-the-art performance on a large variety of video understanding tasks. It takes video frames as input and outputs compact embeddings of the frames, which one can conveniently feed into classifiers, LLMs, retrieval models, etc. When tested on 33 public video understanding benchmarks over four task categories, a single frozen VideoPrism checkpoint outperforms previous best-performing foundation models on 31 of them, with no fine-tuning on target task datasets. We release the following model variants: VideoPrism-B/L are the…

Model · Question answering

Gecko-110m-en

LiteRT Community (FKA TFLite)

This model provides a few variants of the embedding model published in the Gecko paper that are ready for deployment on Android or iOS using LiteRT stack or google ai edge RAG SDK. Try out the gecko embedding model in the google ai edge RAG SDK. You can find the SDK on GitHub or follow our android guide to install directly from Maven. We have also published a Use the sentencepiece model as the tokenizer for the Gecko embedding model. Note that all benchmark stats are from a Samsung S23 Ultra. The inference is run on CPU is accelerated via the LiteRT XNNPACK delegate with 4 threads The inference on GPU is accelerated via LiteRT GPU delegate. Benchmark is done assuming XNNPACK cache is…

Model · Video classification

videoprism-lvt-base-f16r288

Google

Blog: https://research.google/blog/videoprism-a-foundational-visual-encoder-for-video-understanding/ VideoPrism is a foundational video encoder that enables state-of-the-art performance on a large variety of video understanding tasks. It takes video frames as input and outputs compact embeddings of the frames, which one can conveniently feed into classifiers, LLMs, retrieval models, etc. When tested on 33 public video understanding benchmarks over four task categories, a single frozen VideoPrism checkpoint outperforms previous best-performing foundation models on 31 of them, with no fine-tuning on target task datasets. We release the following model variants: VideoPrism-B/L are the…

Model · Question answering

dynamic_tinybert

Intel

Dynamic-TinyBERT has been fine-tuned for the NLP task of question answering, trained on the SQuAD 1.1 dataset. Guskin et al. (2021) note: Here is how to import this model in Python

This model is a fine-tuned version of distilbert-base-uncased on the naturalquestions dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 64 - evalbatchsize: 64 - lrschedulertype: linear - numepochs: 40 - Transformers 4.17.0 - Pytorch 1.10.0 - Datasets 1.18.4 - Tokenizers 0.11.6

LinkBERT-large model pretrained on English Wikipedia articles along with hyperlink information. It is introduced in the paper LinkBERT: Pretraining Language Models with Document Links (ACL 2022). The code and data are available in this repository. LinkBERT is a transformer encoder (BERT-like) model pretrained on a large corpus of documents. It is an improvement of BERT that newly captures document links such as hyperlinks and citation links to include knowledge that spans across multiple documents. Specifically, it was pretrained by feeding linked documents into the same language model context, besides a single document. LinkBERT can be used as a drop-in replacement for BERT. It achieves…

Model · Text generation

Qwen3-1.7B-Distilled-30B-A3B-SFT-GGUF

Convergent Intelligence

GGUF quantizations of reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT for local and edge deployment via llama.cpp and compatible runtimes. Stage 1 — DISC-Informed Knowledge Distillation: Qwen3-1.7B distilled from Qwen3-30B-A3B-Instruct on 6,122 STEM chain-of-thought samples using proof-weighted cross-entropy loss (2.5x → 1.5x decay on derivation tokens) and KL divergence at T=2.0. The distillation emphasized multi-step reasoning over final-answer pattern matching. Stage 2 — Legal SFT: Follow-up supervised fine-tuning on Alignment-Lab-AI/Lawyer-Instruct to add instruction-following capability and legal domain knowledge on top of the STEM reasoning backbone. The result is a 1.7B model that…

Source code available at https://github.com/phhusson/llm-rl/blob/main/grpo-tldr.py

Model · Text generation

Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF

Convergent Intelligence

GGUF quantizations of reaperdoesntknow/Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT for local, mobile, and edge deployment via llama.cpp and compatible runtimes. A 30B Thinking teacher compressed 50x into a model that fits on a smartwatch. Stage 1 — Thinking Teacher Distillation: Qwen3-0.6B distilled from Qwen3-30B-A3B-Thinking on 6,122 STEM chain-of-thought samples. The Thinking variant teacher produces extended reasoning traces with higher-entropy distributions, transferring richer deliberation structure into the student. Proof-weighted cross-entropy (2.5x → 1.5x on derivation tokens) + KL divergence at T=2.0. Stage 2 — Legal SFT: Supervised fine-tuning on Alignment-Lab-AI/Lawyer-Instruct at…

weighted/imatrix quants of https://huggingface.co/nicoboss/Medra27B For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/Medra27B-GGUF This is a vision model - mmproj files (if any) will be in the static repository. If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here are Artefact2's thoughts on the matter…

Model · Summarization

it5-base-news-summarization

Gabriele Sarti

This repository contains the checkpoint for the IT5 Base model fine-tuned on news summarization on the Fanpage and Il Post corpora as part of the experiments of the paper IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation by Gabriele Sarti and Malvina Nissim. A comprehensive overview of other released materials is provided in the gsarti/it5 repository. Refer to the paper for additional details concerning the reported scores and the evaluation approach. Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as: If you use this model in your research, please cite our work as

weighted/imatrix quants of https://huggingface.co/erax-ai/EraX-VL-7B-V2.0-Preview For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/EraX-VL-7B-V2.0-Preview-GGUF This is a vision model - mmproj files (if any) will be in the static repository. If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here are Artefact2's thoughts on…

weighted/imatrix quants of https://huggingface.co/TurboAiLabs/turbo-ai-7b For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/turbo-ai-7b-GGUF If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 See…

Model

DualMind-GGUF

Convergent Intelligence

GGUF quantizations of DualMind for local inference via llama.cpp, Ollama, LM Studio, and other GGUF-compatible runtimes. DualMind is a 1.7B parameter model that implements a dual-cognition reasoning architecture: The model learns to reason freely, then critique its own reasoning, then produce a final answer. Multi-model dialectics collapsed into shared weights. Training lineage: Qwen3-1.7B → DistilQwen3 (uncensored) → Disctil (DISC-refined) → TKD from Qwen3-30B-A3B-Thinking → DualMind SFT on LogicInferenceOA dataset. - temperature: 0.6 - topp: 0.9 - repeatpenalty: 1.3 (important — prevents enumeration loops) - numpredict: 512–1024 - DualMind — source model (SafeTensors)…

Model · Video text to text

VideoGuard-Qwen3.5-4B-Safety-RL-Uncensored-GGUF

Prithiv Sakthi

C/C++ — https://github.com/ggml-org/llama.cpp

Model

DualMinded-Qwen3-1.7B-GGUF

Convergent Intelligence

GGUF quantizations of DualMinded-Qwen3-1.7B for local inference via llama.cpp, Ollama, LM Studio, and other GGUF-compatible runtimes. DualMinded-Qwen3-1.7B is the Opus-trained variant of the DualMind architecture. While DualMind was trained on LogicInferenceOA, DualMinded was trained on Opus-4.6-Reasoning-3000x-filtered — high-quality reasoning traces from Claude Opus 4.6. The Opus training data provides longer, more structured reasoning chains. The thinking column maps directly to the phase without heuristic splitting, producing cleaner cognitive transitions. Training lineage: Qwen3-1.7B → DistilQwen3 → Disctil → TKD checkpoint-512 → DualMind SFT v2 on Opus-4.6-Reasoning. Both share the…

Model · Fill mask

AraBART

Moussa Kamal Eddine

AraBART is the first Arabic model in which the encoder and the decoder are pretrained end-to-end, based on BART. AraBART follows the architecture of BART-Base which has 6 encoder and 6 decoder layers and 768 hidden dimensions. In total AraBART has 139M parameters. AraBART achieves the best performance on multiple abstractive summarization datasets, outperforming strong baselines including a pretrained Arabic BERT-based models and multilingual mBART and mT5 models.

Model · Summarization

led-base-book-summary

Peter Szemraj

The Longformer Encoder-Decoder (LED) for Narrative-Esque Long Text Summarization is a model I fine-tuned from allenai/led-base-16384 to condense extensive technical, academic, and narrative content in a fairly generalizable way. - Ideal for summarizing long narratives, articles, papers, textbooks, and other documents. - the sparknotes-esque style leads to 'explanations' in the summarized content, offering insightful output. The model was trained on the BookSum dataset released by SalesForce, which leads to the bsd-3-clause license. The training process involved 16 epochs with parameters tweaked to facilitate very fine-tuning-type training (super low learning rate). This model is the…

Model · Audio text to text

MOSS-Transcribe-Diarize-HF

Ita Zaporozhets

MOSS-Transcribe-Diarize 0.9B is an end-to-end audio understanding model for long-form multi-speaker transcription, diarization, timestamps, and acoustic event awareness. It supports transcription and diarization across 50+ languages, single-pass inference on audio recordings up to 90 minutes long, and custom hotword prompting for domain-specific terms. Given an audio or video file, the model generates a compact speaker-aware transcript in one pass, including timestamps and anonymous speaker labels such as [S01], [S02], and beyond. 2026-07-22: The subtitle Web UI now supports both Simplified Chinese and English. 2026-07-14: MOSS-Transcribe-Diarize won first place in the 2nd MLC-SLM Challenge…

Model · Image and text to text

Nex-N2.5-mini-APEX-I-MiniPlus-GGUF

IsValorum

Also, don't confuse APEX-I-MiniPlus (Standard) with a generic baseline APEX-I-Mini. Traditional APEX-I-Mini drops core experts aggressively to 2-bit IQ2S and leaves output.weight at 3-bit Q3KM, which creates a noticeable perplexity hit on complex reasoning tasks. Standard MiniPlus avoids that degradation floor while keeping boundary layers in linear Q3K for single-cycle vectorized AVX2 CPU dequantization (hitting 23 to 26+ tok/s on DDR4 laptops), while protecting output in Q6K and routers in F32. To put the numbers in perspective: this cuts nearly 2 GB off a flat 3-bit quant (approx. 15.6 GB), and weighs only about approx. 1 GB more than a generic APEX-I-Mini (approx. 12.5 GB). For that…

Model · Video text to text

VideoGuard-Qwen3.5-9B-Safety-RL-Uncensored-GGUF

Prithiv Sakthi

C/C++ — https://github.com/ggml-org/llama.cpp

weighted/imatrix quants of https://huggingface.co/IFM/AmberSafe For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/AmberSafe-GGUF If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 See…

Model · Text generation

Thanatos-27B-HERETIC

Fool

Architecture: Qwen 3.8 27B (Dense) | Parameters: 27B | Context: 262,144 native / 1,010,000 extensible | Base: Heretic ARA (heretic-org) | Teacher: Claude Fable 5 | Type: Distilled + Abliterated LLM A personal sibling to FoolDev/Janus-35B-HERETIC, which since its 0.9.0 is a Qwen 3.6 35B-A3B MoE — so the two are genuinely different models, not two abliterations of one base (see Janus vs Thanatos). This one is the dense Qwen/Qwen3.8-27B, sharing the teacher (Claude Fable 5) and dataset family, built on heretic-org/Qwen3.8-27B-heretic-ara, an uncensored Heretic ARA-method abliteration, with refusal-trained behavior dialed back at the base layer. (The sibling's -35B- slug names its model…

Model · Tabular classification

tabpfn-mix-1.0-classifier

Autogluon

TabPFNMix classifier is a tabular foundation model that is pre-trained on purely synthetic datasets sampled from a mix of random classifiers. TabPFNMix is based on a 12-layer encoder-decoder Transformer of 37 M parameters. We use a pre-training strategy incorporating in-context learning, similar to that used by TabPFN and TabForestPFN. To use TabPFNMix classifier, install AutoGluon by running: A minimal example showing how to perform fine-tuning and inference using the TabPFNMix classifier: If you find TabPFNMix useful for your research, please consider citing the associated papers: This project is licensed under the Apache-2.0 License.

Model · Text generation

gemma-270m-math-reasoner

Convergent Intelligence

This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

English | 中文 A 143M-parameter bidirectional Chinese↔English translation model, instruction-tuned on the Haidass1.5-143M base — the strongest zh⇄en translator at this scale among general chat-architecture models. Drafter-143M: a control model with identical configuration, data and training recipe, except that it starts from random initialization instead of the pretrained base — used to quantify the contribution of base-model pretraining. OPUS-MT models are single-directional — one independent 78M model per direction; "-" marks directions a model does not serve. The same models re-evaluated on FLORES+ devtest (released 2026; zero overlap with dev): devtest sentences do not overlap with dev.…

Model · Text generation

dQwen3-1.7B-Base

IFML

A masked diffusion language model adapted from Qwen3-1.7B. The backbone is full attention, and every layer is made bidirectional. It is the control model in the paper's matched comparison against the hybrid dQwen3.5-2B. This is a base model, with no instruction tuning. Paper: dQwen3.5: Hybrid-Attention Diffusion Language Models. Code: https://github.com/AntonXue/dQwen Needs a CUDA GPU and transformers>=5.13 (tested with torch 2.7.1+cu128, flash-linear-attention 0.5.1). generate decodes the whole canvas at once, committing positions above a confidence threshold (tau=0.9); pass blocklength=32 for left-to-right block decoding, or tau=None, stepsperblock=k for a fixed budget. The 50B-token…

Model · Text generation

dQwen3.5-2B-Base

IFML

A masked diffusion language model adapted from Qwen3.5-2B. The backbone is hybrid: only its attention layers are made bidirectional, and the Gated DeltaNet layers stay causal. This is a base model, with no instruction tuning. Paper: dQwen3.5: Hybrid-Attention Diffusion Language Models. Code: https://github.com/AntonXue/dQwen Needs a CUDA GPU and transformers>=5.13 (tested with torch 2.7.1+cu128, flash-linear-attention 0.5.1). generate decodes the whole canvas at once, committing positions above a confidence threshold (tau=0.9); pass blocklength=32 for left-to-right block decoding, or tau=None, stepsperblock=k for a fixed budget. The 50B-token checkpoint from the paper is…

Model · Text generation

dQwen3.5-0.8B-Base

IFML

A masked diffusion language model adapted from Qwen3.5-0.8B. The backbone is hybrid: only its attention layers are made bidirectional, and the Gated DeltaNet layers stay causal. This is a base model, with no instruction tuning. Paper: dQwen3.5: Hybrid-Attention Diffusion Language Models. Code: https://github.com/AntonXue/dQwen Needs a CUDA GPU and transformers>=5.13 (tested with torch 2.7.1+cu128, flash-linear-attention 0.5.1). generate decodes the whole canvas at once, committing positions above a confidence threshold (tau=0.9); pass blocklength=32 for left-to-right block decoding, or tau=None, stepsperblock=k for a fixed budget. The 50B-token checkpoint from the paper is…

Model · Text generation

Janus-35B-HERETIC

Fool

Architecture: Qwen 3.6 35B-A3B (MoE) | Total Params: ~34.7B | Active Params: ~3B | Context: 262,144 native / 1,010,000 extensible | Base: Heretic (llmfan46) | Teacher: Claude Fable 5 | Type: Distilled + Abliterated MoE LLM A personal fork of llmfan46/Qwen3.6-35B-A3B-uncensored-heretic — an uncensored Heretic-style abliteration of Qwen/Qwen3.6-35B-A3B, the 35B-total / 3B-active mixture-of-experts multimodal base — repackaged as Janus-35B with Claude Fable 5 reasoning data in the teacher slot. Refusal-trained behavior is dialed back at the base layer. One-liner via Hugging Face (pulls a GGUF + this repo's root-level template / system / params files, including the tool-calling template — HF's…

This model is a fine-tuned version of microsoft/resnet-50 on the imagefolder dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 8 - evalbatchsize: 16 - gradientaccumulationsteps: 2 - totaltrainbatchsize: 16 - lrschedulertype: cosine - numepochs: 20 - labelsmoothingfactor: 0.1 - Transformers 5.16.1 - Pytorch 2.11.0+cu128 - Datasets 4.8.5 - Tokenizers 0.23.1