Open-Weight Models

This model is a fine-tuned version of francesca9805/nor-latn-100mb-ppt-mp-struct-100mbseed3407. It has been trained using TRL. This model was trained with SFT.

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). - PEFT 0.21.1

Join our WeChat or Discord community. Check out the GLM-5.3-Flash blog and GLM-5 Technical report. Use GLM-5.3-Flash API services on Z.ai API Platform. We introduce GLM-5.3-Flash, the first natively multimodal model in the GLM-5 series. With 320B total parameters and just 18B active parameters, it outperforms GLM-5.2 across benchmarks and real-world workloads at one-tenth the price, while approaching Claude Opus 4.8 on coding and agentic benchmarks. GLM-5.3-Flash starts from a newly trained base model, with its architecture and training recipe redesigned around capability and efficiency. For the first time in the GLM series, we introduce a hybrid architecture combining sparse and linear…

GLM-5.3 uses the same base model as GLM-5.2 — every gain comes from post-training. Compared with GLM-5.2, it is much better at complex coding and long-horizon tasks: GLM-5.3 supports deployment with the following frameworks. Feel free to try them out: - SGLang — see cookbook - vLLM — see recipes - TokenSpeed — see here - Transformers — see transformers docs - KTransformers — see tutorial - Unsloth — see guide - For deployment on the Ascend NPU platform, inference frameworks such as vLLM-Ascend, xLLM and SGLang are supported — see here. - GLM-5.3 supports controlling the thinking budget through the reasoningeffort parameter, which accepts three levels: low, high, and max. It defaults to max…

Model

notes-grounded-language

Magdalena Szymanski

This is an exploratory note for Grounded Language. It records the intended comparison, likely confounders, and reproducibility requirements before any benchmark result is reported. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as RefCOCO, Flickr30k, and Visual Genome - reproducibility checks, failure modes, and open questions - topic-relevant references Start with papernotes.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later, they should include dataset versions, commands, seeds, hardware, and raw logs. The…

Model

notes-lightweight-multimodal

V. Singh

Reading notes and an experiment sketch for Lightweight Multimodal. The repository emphasizes what still needs to be tested instead of manufacturing scores or release claims. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as task-appropriate public benchmarks named in the main note - reproducibility checks, failure modes, and open questions - topic-relevant references Start with review.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later, they should include dataset versions, commands, seeds, hardware, and raw…

Model

notes-multimodal-generation

Kevin Sitorus

This repository contains a working research note about Multimodal Generation. It organizes motivation, related work, a falsifiable hypothesis, and an evaluation plan. It is not presented as a completed paper or a release of trained models. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as task-appropriate public benchmarks named in the main note - reproducibility checks, failure modes, and open questions - topic-relevant references Start with summary.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later, they…

Model

notes-zero-shot-transfer

Piotr-Kowalski

Reading notes and an experiment sketch for Zero Shot Transfer. The repository emphasizes what still needs to be tested instead of manufacturing scores or release claims. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as task-appropriate public benchmarks named in the main note - reproducibility checks, failure modes, and open questions - topic-relevant references Start with analysis.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later, they should include dataset versions, commands, seeds, hardware, and raw…

This model is a fine-tuned version of google/gemma-4-E4B-it. It has been trained using TRL. This model was trained with RLOO, a method introduced in Back to Basics: Revisiting REINFORCE-Style Optimization for Learning from Human Feedback in LLMs.

Artifacts of the study-mode experiments (study-LoRA adapters, predecoded study transcripts, memory checkpoints, evaluation results). Code: branch exp/study-mode of the NPT-2 repository.

ONNX conversion of nvidia/NV-Reason-CT for ONNX Runtime Web with WebGPU. These files are used by https://jarrelscy.github.io/nv-reason-ct-web/ (source: https://github.com/jarrelscy/nv-reason-ct-web), which runs the model entirely in the browser. Each.json manifest lists the ONNX graph, its external data chunks (split to fit browser buffer limits) and the decoder state names. The decoders use fp32 activations and MatMulNBits weights, and the lmhead is pruned to the last token. Token embeddings are tied to lmhead, so they are not stored separately; the web app dequantises embedding rows from the lmhead weights (described under embed in decoder.json). Image tokens use 3D MRoPE positions as in…

Model

nyay-mitra

Anamika Mukherjee

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).

Model · Other

nys-sft-public

Dakuwon Moody

NYS is not a transformer. It is a coupled-oscillator organism: phases \(\thetai\), amplitudes, natural frequencies \(\omegai\), and a sparse coupling graph \(K\). Language, compiler bits, and speech-frame IDs live on one graph (\(N = 5{,}000{,}000\) nodes, \(k = 512\) edges per node). The native graph trains by Equilibrium Propagation (free RK4, then same-moment free/nudged branches, then a contrastive \(K\) update), without backprop or a cross-entropy objective. A separate sparse context head now provides retrieved language inputs. The GPU Qwen teacher uses PyTorch/Transformers; that does not make the native graph a transformer. Dakuwon Moody (YNSScarSaiyan) · Saiyan Corp The…

Model

oac-ops-health-v2

SZL Holdings

This package is a deterministic Python standard-library kernel and a JSON table of coefficients fitted on generated transport counters. It is not a transformer checkpoint or adapter; it is not a clinical model or result service. It accepts eight bounded operational fields and proposes ALERT, NOALERT, or ABSTAIN for operator attention. Every authority flag is false: it cannot acknowledge a transport message, control a device, interpret a result, release a result, or make a care decision. Do not submit patient information, laboratory results, specimens, orders, or device identifiers. The four executable/data files in this repository are byte copies of ops-health/v2/ops-health/ at the…