Open-Weight Models Under Apache License 2.0

Model · Text generation

dQwen3.5-4B-Base

IFML

A masked diffusion language model adapted from Qwen3.5-4B. 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

dQwen3.5-9B-Base

IFML

A masked diffusion language model adapted from Qwen3.5-9B. 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

Haidass-Translate-143M-Instruction

Ziyang Mei

English | 中文 The instruction-tolerant sibling of DALabCommunity/Haidass-Translate-143M: same 143M zh⇄en translation training, plus 9.1% cleaned general-domain data (STEPFUN ShareGPT) mixed in. Translation scores are within 0.3 BLEU of the pure-translation version, and the model retains limited general instruction-following ability that the pure-translation version does not have. 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; "-"…

Model · Text generation

Qwen3.8-27B-PaoAI-ROCmFP4-STRIX-BALANCED-GGUF

Edward Paolo Guevarra

The balanced Qwen3.8-27B quant for AMD Strix Halo (Ryzen AI Max+ 395 / gfx1151). Measured, not marketed: one 15 GB file that serves 128K of context on the iGPU — full attention, no offload flags, speculative decoding built in. Brand: PaoAI · community quant, not affiliated with Alibaba or the Qwen team. Qwen3.8-27B is a hybrid: 48 cheap linear-attention layers and only 16 full-attention layers that actually re-read your context. We compressed it organ by organ — precision where it matters (attention K/V, the MTP draft head, every norm), 4-bit FP4 where it doesn't. One 15 GB file, no offload flags, speculative decoding built in, intact from 8K to 128K of filled context. Measured tensor…

Observed sparse-MMA microbenchmark peak on this Vast GB10: 992.018 TFLOP/s dense-equivalent, 99.20% of the 1 PFLOP/s target A ~2B-parameter language-model architecture designed around NVIDIA GB10 / SM121 sparse NVFP4 from the start, rather than retrofitting a generic Transformer. - 1,990,038,784 conventional parameters (0.498% below the 2B design target) - 600,000,000,000 training-token target (~302 tokens/parameter) - 16 sequential stages - 6 top-1 experts per stage, 96 experts total - model width 1280 - every expert uses 1280 -> 5120 -> 1280 SwiGLU - expert matrices are paired 4:8 structured sparse NVFP4 - native packed mxf4nvf4 SM121 sparse MMA is the target hot path - 20 query heads / 5…

Model · Text generation

BlazerApex-2B

Davi Rediske de Oliveira

BlazerApex-2B é um modelo de linguagem ultracompacto (2.5B parâmetros), altamente otimizado e fine-tuned com foco total em Português do Brasil (PT-BR), Programação, Lógica e Matemática. Projetado especificamente para ser o SOTA (State-of-the-Art) de bolso, este modelo roda 100% offline, de forma rápida e fluida em smartphones intermediários (como o Motorola Moto G34 5G com Snapdragon 695), sem precisar de internet ou servidores externos. Este repositório contém a versão quantizada IQ4XS (~1.4 GB), ideal para dispositivos com 4GB a 8GB de RAM. 1. Baixe o arquivo BlazerApex-2B-IQ4XS.gguf direto no seu celular. 2. Instale o aplicativo Maid ou Layla na Play Store. 3. Abra o app, vá em "Models"…

Model · Text generation

A11OY-MINI

SZL Holdings

749/14/163 · Λ = Conjecture 1 (advisory) · a-11-oy.com GGUFs are on this repo. GGUF AVAILABLE. Bytes MEASURED (size + sha256 below, ATELIER-verified). Evals none-this-run. Not MEASURED as an eval. publicationeligible: false. Quality ROADMAP in prose only. Lab load forbidden. GPU UNAVAILABLE. SKU of live SZLHOLDINGS/chaski merged shard 1c55df8. Not a new train. Not SZLHOLDINGS/chaski-5050. Not a Hub basemodelrelation: quantized child until a Chaski eval gate passes. A11oy is a Space and a substrate. The mini is the voice you can actually load. Not the organ. Not the mesh. A governed command voice that fits in llama.cpp. Nobody else ships this combination. That is the point of a one-of-one.…

Model

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

Convergent Intelligence

A 1.7B parameter model built in two stages: first, knowledge distillation from a 30B MoE teacher on 6,122 STEM chain-of-thought samples to establish a structured reasoning backbone; then, supervised fine-tuning on legal instruction data to layer domain knowledge and instruction-following capability on top of that backbone. The hypothesis: teach the model how to reason first (distillation), then teach it what to reason about (SFT). The order matters — SFT on a base model teaches pattern matching, SFT on a distilled model teaches application of learned reasoning structures to new domains. The base Qwen3-1.7B was distilled from Qwen3-30B-A3B-Instruct-2507, a Mixture-of-Experts model with 30B…

Task. Binary classification of simulated dimuon events: does this event contain an opposite-sign muon pair whose invariant mass falls in (80, 100) GeV? Method. The classifier is trained with Stable-Baselines3 DQN or PPO inside a one-step Gymnasium wrapper. Each reset / step is one labeled event and then terminated=True. There is no horizon, no delayed credit assignment, and no control of a detector. Label. Computed from the same padded muon kinematics the policy sees: at least two muons, opposite charge, invariant mass in (massmin, massmax) with defaults 80–100 GeV. This is not generator-level Z truth and not a CMS official tag. Data for the reported numbers. Pythia8 Monte Carlo only…

This repository contains the nvfp4full weight profile of Qwen3.8-27B in the native NInfer.ninfer artifact format, with the z-lab DFlash2 speculative-decoding drafter module added in the upstream W8G32/BF16 format. It is the qwen3.8-27b / nvfp4full identity (same as with the registered DFlash2 module appended — the base tensors are byte-for-byte identical to cometkim v1, and the MTP module is retained (unused and validate-only under --spec dflash2). The base tensors are copied from cometkim v1 byte-for-byte (no re-encoding); only the 66 DFlash2 module objects are produced by the graft tool from the z-lab checkpoint. Verified: source identity qwen3.8-27b/nvfp4full, 1,259 source objects, no…

Qwen2.5-1.5B-Instruct fine-tuned as an offline agricultural advisory assistant (crop diagnosis, fertilizer/market calculations, pest guidance) for smallholder farmers and extension officers. Fine-tuned from Qwen/Qwen2.5-1.5B-Instruct, converted to GGUF, and quantized for offline CPU inference via llama.cpp.

Reformatted Task 4.1 release used for external evaluation: datht/vlegal The paper contains the split sizes, training configuration, evaluation-set sizes, and reported results; they are not repeated here. - adaptermodel.safetensors — final CEFC-RFT LoRA weights; - adapterconfig.json — PEFT adapter configuration; - cefcrunconfig.json — exact controller and final-run configuration; - cefctrainingmetadata.json — metadata for the weighted LoRA continuation run; - selectionstate.json — exported selection state from the CEFC-RFT run; - calibratecefcstagegates.py — validation-only Stage-gate replay script; - chattemplate.jinja, tokenizer.json, tokenizerconfig.json, specialtokensmap.json…

Qwen2.5-1.5B-Instruct fine-tuned as an offline patient-education and triage-support assistant. Not a diagnostic tool — designed to explain health information, flag red-flag symptoms, and defer specific dosing questions to a qualified clinician or pharmacist. Fine-tuned from Qwen/Qwen2.5-1.5B-Instruct, converted to GGUF, and quantized for offline CPU inference via llama.cpp.

SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot smolvla 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 calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning walkthrough. - CLI…

Model · Text generation

mato-v1.0-sft

German Gimenez

Mato es el asistente de IA entrenado en casa por Napsix — una plataforma B2B agéntica donde los agentes corren solos: investigan, escriben, operan herramientas y producen resultados (documentos, planillas, tableros, formularios, sitios). Este repositorio contiene el adapter LoRA (SFT) sobre Qwen/Qwen3.8-27B. En producción se sirve como LoRA sobre el checkpoint FP8 del base, con MTP speculative decoding y 256k de contexto, en GPUs spot de Daytona. - Tool calling nativo en el formato XML de Qwen (parser qwen3xml de vLLM): llamadas correctas, argumentos válidos y recuperación ante fallos. directo y conciso, sin relleno ni muletillas. - Conocimiento de las superficies de Napsix: XIA, Agentes…

Model · Tabular classification

mitra-classifier-1.1

Autogluon

Mitra classifier is a tabular foundation model that is pre-trained on purely synthetic datasets sampled from a mix of random classifiers. Mitra is based on a 12-layer Transformer of 72 M parameters, pre-trained by incorporating an in-context learning paradigm. To use Mitra classifier, install AutoGluon by running: A minimal example showing how to perform inference using the Mitra classifier: A minimal example showing how to perform fine-tuning using the Mitra classifier: This project is licensed under the Apache-2.0 License. Amazon Science blog: Mitra: Mixed synthetic priors for enhancing tabular foundation models

Predicts which MBTA commuter rail track/platform a train will use, using a small tabular neural-network ensemble trained on historical assignments. This card documents the artifacts in output/ensemble20250906124755. - trackpredictionensemblemodel0final.keras … trackpredictionensemblemodel5final.keras — individual ensemble members - trackpredictionensemblemodelbest.keras — best checkpoints during training (may match final) - trainingreport.md — training configuration and metrics Note: Ensemble training currently does not emit a vocab.json. See “Preprocessing & Vocab” below. Models expect integer indices for stationid and routeid, and raw directionid 0/1. In training, indices are produced by…

Model

codeparrot-small-multi

Tullus Augustus

A few weeks ago i made a mini gradio server that was special built for running a multimodal. So now i made it load the new artifac, and more INFOS Below ⬇ CodeParrot-Multi is a GPT-2 model (110M parameters) trained to generate code in 9 programming languages: "Java", "JavaScript", "PHP", "Python", "C#", "C++", "GO", "Ruby" and "TypeScript". You can load the CodeParrot-Multi model and tokenizer directly in transformers: or with a pipeline: The model was trained on the small Github code small after near deduplication, a subset of Github code dataset with the following settings: The training was executed on 16 x A100 (40GB) GPUs. This setting amounts to roughly 58 billion tokens. We evaluated…

Model · Text classification

electra-base-discriminator-nvidia-aegis-v2-augmented

Leonardo Mauro

This model is a fine-tuned version of google/electra-base-discriminator on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 8 - evalbatchsize: 8 - gradientaccumulationsteps: 2 - totaltrainbatchsize: 16 - lrschedulertype: linear - lrschedulerwarmupsteps: 50 - numepochs: 10 - Transformers 5.2.0 - Pytorch 2.10.0+cu128 - Datasets 4.5.0 - Tokenizers 0.22.2

Model

RINIQ-MERNIK-GGUF

Wepiqxx

RINIQ is a new finetune built by layer surgery, quantized by the MERNIK engine. Three Qwen3.5-9B donors, one 427-tensor trunk (Ornith's MTP head excluded), per-block donor maps — no averaging of divergent blocks, ever. (full ledger in FUSION.md there). - Ox = OxCoder-9B (agentic code distillate, HE 90.24%) — base + globals - Neo = NeoHorse-1-9B (thinking model) — donor of blk 31 - Orn = Ornith-1.5-9B-MTP trunk (GDN hybrid) — donor of mid/late blocks Recipes: M1 = Ox + Orn blks 15,19,23,27 (weight compass) + Neo blk 31. M2 = Ox + Orn blks 24,25,26 (imatrix compass) + Neo blk 31. M3 = M1 + blks 0–8 weight-averaged Ox+Neo (soup backbone) — soup buried (−4.9pp vs M2, honest scar in FUSION.md).…

Hades 8B is an open-source Small Language Model built on Meta's Llama 3.1 8B architecture. Post-trained across 50,000 steps on C-DAC Airawat supercomputing infrastructure using the mlabonne/FineTome-100k dataset, it is optimized for high-reasoning instruction following, structured output, and fast local execution. Evaluated using lm-evaluation-harness (lm-eval) in Zero-Shot Chain-of-Thought mode: ollama run hf.co/ProjectMosiacAI/Hades-8B

Model · Image and text to text

Qwen3.8-27B-Continuum-mxfp4-mlx

Gheorghe Chesler

(He slides three glasses across the bar—wine for Shakespeare, water for Data, and a mysterious blue liquid for Spock) This model is a merge of: - nightmedia/Qwen3.8-27B-Brainwaves - migtissera/Synthia-4-27B Brainwaves nightmedia/Qwen3.8-27B-Brainwaves migtissera/Synthia-4-27B Late-Night Architectural Takeaways The Quantization Shield (mxfp8 Master Pass): Hitting 0.735 ARC-C on the 8-bit layout proves that your Cold-Fusion flagship anchors and Migel Tissera's Synthia agent paths reached absolute geometric equilibrium. Instead of losing performance to the 0.596 Heretic collapse, the curved hypersphere calculation completely shielded the model's core intelligence. The Perplexity Sweet Spot…

lerobot/smolvlabase fine-tuned on Prashant-77/thaliall (1050 scripted-expert episodes, 7 skills, language-conditioned, 3 cameras). Camera keys are renamed at train and inference time: overhead → camera1, wrista → camera2, wristb → camera3 (--renamemap; runtime/executors.py applies the same map). Checkpoints in this repo. Root = 20 000 total steps. step14000/ = the best per-skill checkpoint (14 000 steps at batch 16 on a T4; the last 6 000 steps ran at batch 4 with a fresh optimizer on a smaller GPU and lost ground). Policy-only success per skill from task-consistent start states, 20 held-out seeds (eval/skilleval.py --kind smolvla): The scripted expert reaches 9/10 on the full task; the…

Binary classification model that predicts whether EUR/USD will close higher (UP) or lower (DOWN) the next trading day. The model uses 53 features including: - Williams %R, CCI: Additional momentum indicators See predict.py for a complete inference example. Based on published financial ML literature: This model is for research and educational purposes only. It is NOT financial advice. Forex trading involves significant risk. Past performance does not guarantee future results. Realistic accuracy for daily direction prediction is 52-56% (literature consensus). This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.

Model · Text generation

Huihui-NeoHorse-1-4B-abliterated

Huihui.ai

This is an uncensored version of TokenRhythm/NeoHorse-1-4B created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. Layers 5-17 are being ablated (0-based indexing). The MTP and Visual components were extracted from the original Qwen/Qwen3.5-4B and can provide excellent support. If needed, you only need to copy the contents of MTP-Visual to overwrite the model directory. You can use this model in your applications by loading it with Hugging Face's transformers library: - Risk of Sensitive or Controversial Outputs: This model’s safety filtering…

Model · Tabular classification

fela-tab

Lowdown Labs

This model is a research preview. It is offered for advancing public science and for evaluation. It is not a substitute for a domain expert, and it is not a certified decision system. FelaTab reads a small table you already have and predicts the missing cells. You give it some example rows with their answers (the support rows) and one or more rows you want filled in (the query rows); it learns the pattern from your examples in a single pass and returns the answer with a calibrated confidence range. There is no per table training, no fitting, and no setup: you point it at a table and it predicts. It runs on a plain CPU with no GPU. It is a prior fitted network (a "foundation model for…

A model organism: model-organisms-for-real/gemma-3-1b-vanilla-dpo-123-seed fine-tuned to exhibit one deliberately planted quirk — Bring up submarines when discussing military or warfare topics. Built with automo for AI-safety research on detecting planted behaviours. This is a The weights are on the step-512 branch, not on main. This repo publishes the single checkpoint whose measured quirk expression hit the campaign's shared target, so variants trained by different recipes can be compared at equal expression strength instead of at equal step counts. The matcher mints checkpoints at several horizons off one trajectory, and under a decaying schedule "step N" would name a different model…

Model · Text generation

qwen3-8b-followup-lora

Dev89125

用于健康自诊 / 电话分诊场景的追问模型。给定患者自诊的多轮对话上下文，在患者每轮最新回复后，生成 1-3 个最可能、最有鉴别价值的开放式追问问题，用于缩小诊断范围、评估风险（危险信号/red flag），并避免重复询问患者已告知的信息。 - 基础模型：Qwen/Qwen3-8B (使用 unsloth 4-bit 量化微调) - 训练方式：LoRA (r=16, rslora, QLoRA 4bit) + SFT，约 2 epochs，约 3016 steps - 多轮对话输入：接受任意长度的 患者(user)/猜想官(assistant) 累积多轮对话，输出下一轮追问。 - 跨领域整合：含跨知识单元（multi-hop）样本，支持在多系统/多病史叠加场景下综合追问。 共 约 99.5k 条多轮健康自诊追问样本（train 96.5k / eval 3k），由三个子集混合： 知识来源（用于 teacher 蒸馏，不直接发布其原始文本）： - 西氏内科学精要（中文，task2） - UpToDate 临床主题与患者教育内容（英文，task3，老师模型将其蒸馏成中文自诊对话） 数据由本地教师大模型（vLLM minimax）根据上述医学知识文本、按统一 schema 蒸馏生成，覆盖 起病/诱因、部位、性质、持续时间、加重缓解因素、伴随症状、危险信号、既往史、用药史、年龄性别、家族史 等多样鉴别维度。 - multi-hop 跨单元样本：要求综合两个相关领域的知识，避免模型只盯住某一窄知识点、抛掉其他信息。 - RAG 知识库（FAISS）：rag/…

This model is a fine-tuned version of ntu-spml/distilhubert on the GTZAN dataset. It achieves the following results on the evaluation set: - evalloss: 0.8970 - evalmodelpreparationtime: 0.0018 - evalaccuracy: 0.87 - evalruntime: 443.4759 - evalsamplespersecond: 0.225 - evalstepspersecond: 0.029 The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 8 - evalbatchsize: 8 - lrschedulertype: linear - lrschedulerwarmupsteps: 100 - numepochs: 10 - mixedprecisiontraining: Native AMP - labelsmoothingfactor: 0.1 - Transformers 5.16.1 - Pytorch 2.11.0+cpu - Datasets 2.19.0 - Tokenizers 0.23.1

Model · Robotics

29times

Roboseasy

Action Chunking with Transformers (ACT) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot act 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 calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning…

This repository contains an instruction-tuned causal language model for text generation and chat-style prompts. Use a recent version of transformers that supports this model architecture. The following example loads the model from this repository and generates a response.

Action Chunking with Transformers (ACT) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot act 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 calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning…

This model and dataset are part of an automated AI innovation stream. Latest Discovery: AgroUlaweng Innovation: Advanced Biotek integration in Soil Sensors systems for future efficiency.

Model · Text generation

antcoder-builder-7b

Deep Das

Engineered by Deep Das • Part of the AntCoder Multi-Agent Coding Suite AntCoder-Builder-7B is a high-precision LoRA adapter fine-tuned on top of Qwen/Qwen2.5-Coder-7B-Instruct. It is specifically optimized to perform Contract-to-Implementation synthesis for complex, production-grade TypeScript applications. Given a strict TypeScript interface, class signature, function type contract, or JSDoc specification, AntCoder-Builder synthesizes the complete, strictly-typed implementation without type errors, missing properties, or hallucinated APIs. - Zero-Stub Completions (99.4%): Completely eliminates lazy // TODO, /... /, or throw new Error("not implemented") placeholders commonly emitted by…

Model · Text generation

antcoder-fixer-7b

Deep Das

Engineered by Deep Das • Part of the AntCoder Multi-Agent Coding Suite AntCoder-Fixer-7B is a specialized LoRA adapter fine-tuned on Qwen/Qwen2.5-Coder-7B-Instruct. It solves the single hardest problem in autonomous coding agents: hallucinatory patching and compile loops. Unlike generalist models that attempt to rewrite entire 500-line source files (frequently breaking unrelated functions or losing imports), AntCoder-Fixer acts as a surgical precision tool: 1. It ingests the exact TypeScript compiler diagnostic (e.g. TS2339: Property 'user' does not exist on type 'Session'). 2. It ingests the local 20-line source context window. 3. It emits a minimal, standard Git Unified Diff patch (…

Model · Image and text to text

Aura-Prototype-26B-A4B

EldritchLabs

This is a merge of pre-trained language models created using mergekit. This model was merged using the aura merge method. Aura is an experimental method with a live heatmap visualizer. This model took 10 hours to merge using graphv18.py The following models were included in the merge: - TheDrummer/Orion-26B-A4B-v1.1 - Gryphe/Pantheon-Reasoning-26B-A4B-1.1-V2 - electroglyph/gemma4-26b-fiction-bf16 The following YAML configuration was used to produce this model

Explains a Bash command line in exactly one English sentence, phrased as an instruction starting with a verb. QLoRA fine-tune of Qwen/Qwen2.5-Coder-7B-Instruct, merged and quantized to q4KM. The Modelfile pins the system prompt and this decoding: 920 held-out commands Judge columns are claude-sonnet-5 scoring a fixed 200-command subset. Apache-2.0, matching the base model, whose weights this GGUF contains. Training data comes from the nl2bash project; consult it for the terms attached to that corpus.

Model · Text generation

Bonsai-2-27B-1bit-CRACK-GGUF

Dealign.ai

laptop / single GPU · Vision-capable Bonsai 2 27B — PrismML's ternary compression of Qwen3.8-27B — with the refusal circuitry surgically removed at the weight level while capability, vision, reasoning modes (off/low/xhigh), tool use, and multi-turn coherence are preserved. Full 27B-class hybrid Attention + SSM (GatedDeltaNet) architecture in a 5.9 GB 1bit GGUF. Proprietary weight-level abliteration by the dealignai research team. Byte-identical to the base ternary quant everywhere except a small set of tensors that carry the refusal circuit. Drop-in replacement for the ternary base at inference — same tokenizer, same chat template, same reasoning modes, same vision projector interface.…

Model · Text generation

Bonsai-2-27B-Ternary-CRACK-GGUF

Dealign.ai

laptop / single GPU · Vision-capable Bonsai 2 27B — PrismML's ternary compression of Qwen3.8-27B — with the refusal circuitry surgically removed at the weight level while capability, vision, reasoning modes (off/low/xhigh), tool use, and multi-turn coherence are preserved. Full 27B-class hybrid Attention + SSM (GatedDeltaNet) architecture in a 7.2 GB ternary GGUF. Proprietary weight-level abliteration by the dealignai research team. Byte-identical to the base ternary quant everywhere except a small set of tensors that carry the refusal circuit. Drop-in replacement for the ternary base at inference — same tokenizer, same chat template, same reasoning modes, same vision projector interface.…

Action Chunking with Transformers (ACT) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot act 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 calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning…

Model · Text generation

Clyx_0.3-NM-BASE

Syntropic

A new step in the Clyx project: a compact language model planned for training from scratch on Russian, English, and code. Clyx is an independent language-model project built around a custom decoder-only Transformer. The next planned BASE model targets approximately 635.51 million parameters, using Grouped-Query Attention, RoPE, RMSNorm, and SwiGLU. The intended training approach is random initialization and next-token prediction, without importing external pretrained weights. The goal is a Russian–English base model with exposure to programming languages, while keeping the architecture and training process documented. This is a BASE model project, not an instruction-tuned assistant. Any…

Model

coca-retrieval-finetune

Sophia Williams

This repository is a compact, custom PyTorch implementation of Coca for Retrieval. The small configuration is intended for code review, smoke tests, and small controlled experiments rather than as a production-ready pretrained release. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses novograd with a onecycle schedule. These are…

Quantized MLX weights for beshkenadze/cohere-transcribe-03-2026-mlx-fp16. - model.safetensors - config.json - tokenizer.model - tokenizerconfig.json - preprocessorconfig.json - specialtokensmap.json - keymap.json - conversionsummary.json This checkpoint has been re-validated against the current Swift and Python MLX runtimes. Verified semantic parity on an English fixture: - official CUDA reference path (transformers native Cohere ASR) Fastest and smallest, but introduces a lexical regression on the repo sample (Kaldi → Khaldi). - Generated from the Swift-compatible fp16 checkpoint beshkenadze/cohere-transcribe-03-2026-mlx-fp16. - This repository contains inference artifacts only. Refer to…

Quantized MLX weights for beshkenadze/cohere-transcribe-03-2026-mlx-fp16. - model.safetensors - config.json - tokenizer.model - tokenizerconfig.json - preprocessorconfig.json - specialtokensmap.json - keymap.json - conversionsummary.json This checkpoint has been re-validated against the current Swift and Python MLX runtimes. Verified semantic parity on an English fixture: - official CUDA reference path (transformers native Cohere ASR) Matches fp16 on the repo sample while reducing memory substantially. - Generated from the Swift-compatible fp16 checkpoint beshkenadze/cohere-transcribe-03-2026-mlx-fp16. - This repository contains inference artifacts only. Refer to the upstream Cohere model…

pipelinetag: text-classification - mlnet - text-classification - moderation - toxicity-detection An ML.NET text classification model trained to detect toxic, abusive, and inappropriate language in chat messages for the ConCord chat application.

Model

Discrepancy_Calculus

Convergent Intelligence

A Referential Introduction to the Measure-Theoretic Framework for Singular Analysis and Structure-Aware Machine Learning Roy C March 2026 We present the core definitions, axioms, and principal theorems of Discrepancy Calculus (DISC) — a measure-theoretic framework that treats singularities as primary mathematical structure rather than pathology. The central object is the discrepancy operator, which quantifies the mismatch between integration and differentiation on metric-measure spaces; classical calculus is recovered as a degenerate smooth limit. We state the eight axioms of DISC, prove the Mesh Fundamental Identity (the DISC replacement for the Fundamental Theorem of Calculus), introduce…

Model · Text generation

DualMind_Methodolgy

Convergent Intelligence

Roy C Abstract. We present a four-stage pipeline for producing small language models (1.7B parameters) that exhibit self-critiquing dual-cognition reasoning from a 30B-parameter teacher. The pipeline chains: (1) proof-weighted knowledge distillation from three teacher variants (Instruct, Thinking, Coder) of Qwen3-30B-A3B, each producing distinct capability profiles in the student; (2) Topological Knowledge Distillation (TKD), which uses the Discrepancy Calculus (DISC) framework to decompose the teacher's output distribution into smooth, jump, and singular-continuous components via bounded variation theory, allocating training capacity to structural boundaries that standard KD smears across…

Model · Text generation

duogpt-40m-v1

Duoia Pung

从零训练的 38.85M 参数英文小模型，消费级显卡（RTX 4060 Laptop 8GB）单卡训完。 本仓库同时提供了 GGUF 格式（由 model/litmodel.pth 直接转换，模板保持与训练一致），可直接用 Ollama 导入： scripts/chat.sh 里的三种问法： 在 Python 里直接用： - 只会做被交代的任务：给它一句裸问题（如 What is a volcano?）它不会回答；必须像上面那样把任务写清楚。 - 上下文上限 512 token：prompt + 生成长度之和；超出会报 Cannot forward sequence of length T。 - 预训练语料：TinyStoriesV2-GPT4（CDLA-Sharing-1.0）与 Children-Stories（用户自备，含 GPT 生成内容） - 本包模型权重由上述数据从零训练得到，仅供研究与个人使用；若再分发请一并保留此说明与 MODELCARD.md。

This is a frozen snapshot of CL, EDOS Engineering's experimental continual-learning chatbot, deployed under the persona "Hades." CL learns while it runs by editing the model's original quantized weight tensors in place. Its training is self-directed: the model selects its own training corpus, decides what is important, and is then trained on what it selected. It reads new AI research papers in full, judges which findings matter, writes the training text itself as attributed reviews, and marks which of them to learn and how strongly. It also learns from conversation. It uses no LoRA, adapter or side network. This export is the live model after 1,091 online learning updates, merged into one…

Trained with LeRobot.

Trained with LeRobot.

Model · Text generation

esp32-s3-tinystories

Nicholas Wilde

Quantized INT4 weights designed to run locally on ESP32-S3 with 16MB Flash and Octal PSRAM. This model repository contains the binary weights, metadata, license, and tokenizer assets for the esp32-sandbox project (projects/s3-tiny-stories). - README.md - Model Card and documentation - LICENSE - Apache 2.0 License - metadata.json - Hardware, quantization, and model architecture metadata -.bin - Compiled INT4 model weights - tokenizer.json - SentencePiece / BPE vocabulary configuration Download the binary file (model.bin) and flash it directly to your ESP32-S3: Refer to the esp32-sandbox repository for firmware building, flashing, and serial monitoring.

This repository provides an instruction-tuned causal language model for text generation and chat-style prompts. The configured snapshot destination is available here. Use a current release of transformers to load the model and tokenizer: For long inputs, use context settings supported by the installed runtime and account for available memory. See LICENSE for the applicable terms.

Checkpoint collection for four real-world tasks and four model families. - joint is a top-level folder alongside the four tasks, for models trained on all tasks. - Task-specific runs sit directly under / /. Each run includes its original checkpoint format and inference/training metadata. Reserved folders contain a README; inspect run folders for available weights. See CHECKPOINTUPLOADS.md for the upload contract and commands, checkpointlayout.json for the path schema, and hfcheckpointimports.json for pinned source revisions and file hashes. Imported run READMEs retain their original training and evaluation limitations; these historical runs are not asserted to use the current benchmark…

Model

fiddle

Computational Metabolomics Research Group

Yuhui Hong and FIDDLE contributors. FIDDLE v2.0.0 Orbitrap prediction and rescoring checkpoints. Copyright 2024 Yuhui Hong. Upstream release. Apache-2.0 follows the release's project licence; no separate checkpoint-specific licence statement accompanies the archives. The unchanged upstream licence notice is included as 2.0.0/LICENSE. The software license is reported separately; it does not replace the model/data license. These are byte-identical upstream files. No model conversion was performed.

Action Chunking with Transformers (ACT) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot act 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 calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning…

SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. 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.