Open-Weight Models

Model

Detector

Do Tuan Minh

This package contains exactly six locked-protocol baseline jobs: RT-DETR, Faster R-CNN R50-FPN, and SSDLite320-MobileNetV3-Large, each trained once on bright (DB) and raw dark (DL) LOD images. The six notebooks are in notebooks/. Attach this directory as a Kaggle dataset, attach the LOD image dataset, edit only the first notebook cell, and choose Run All. Each run verifies the paired 1647/183/400 split, smoke-tests model/loss/backward/checkpoint reload, trains for 50 epochs, selects solely on the matching-domain validation mAP50-95, evaluates the required locked test sets, and emits detectorresults.zip. All jobs use zero-based source labels for the eight frozen classes: car, motorbike…

Model · Text generation

DietRecommendation-Qwen2.5-0.5B

Yubraj Sigdel

A LoRA adapter for Qwen/Qwen2.5-0.5B-Instruct that turns a short user profile (age, gender, height, weight, activity level, dietary preference, daily calorie target) into a one-day meal plan with breakfast, lunch, snack and dinner. It is a small, fast, single-purpose model: about 2.2M trainable parameters on top of a 0.5B base, usable on CPU. The adapter was trained on one fixed prompt layout. Use the same system prompt and the same field names and order, otherwise quality drops. Two details matter: - Pass the system prompt explicitly. Without it, the chat template inserts Qwen's default system prompt, which the adapter never saw. - The first field is spelled Ages: in the training data, so…

yeah man i mean if you're here you know why you're here and if you don't know then get outta here lmao ports are by u/alexalexa430 on reddit but you can call me alex (we're the same guy)

HunyuanVideo-13B fine-tuned with DiffusionART, using temporal alignment, visual quality and motion quality rewards at 7:2:1, with a pretrained-reference MSE regularizer (β = 0.1). This repository contains the full transformer weights, not a LoRA or a complete pipeline. The inference entry below downloads the base model's VAE and text encoders automatically. Use the included infer.py. Expand the setup once, then run the example from the same directory. Linux, Python 3.12, a CUDA 12.1-compatible driver and toolkit (nvcc), and approximately 100 GiB of available GPU memory and disk space for the verified default path. The installer uses a pinned public DanceGRPO inference runtime. The fixed…

Powered by Stability AI. A PEFT LoRA adapter for Stable Diffusion 3.5 Medium, post-trained with DiffusionART using GenEval, CLIPScore, PickScore and HPSv2 feedback. Only the LoRA adapter is included; download the SD3.5 Medium base model separately. The adapter uses rank 32 and alpha 64. Same prompt and seed within each pair. Obtain access to the gated base model, authenticate with Hugging Face, and install the verified dependencies in an Use an official CUDA-enabled PyTorch wheel compatible with your driver. Then authenticate with an account that has access to the base model and this adapter. Download the adapter and run inference: The default settings are 512 x 512, 40 sampling steps…

End-to-end 32-class FDI tooth detector trained on the IOP-Compass clinical photographic dataset (700 patients, 3,500 photos).

Model · Text generation

Diplodocus-preview-GGUF

Osaurus-AI

Diplodocus preview 4, the Osaurus assistant for finance, documents, spreadsheets and tool-assisted work, built on IFM/K2-Horizon-7B and trained on Osaurus' own workspace tools (workbooks and documents edited in place, the built-in calculate tool, reasoning on and off). This is the GGUF build for llama.cpp: one calibrated Q6K file, 7.76 GB. Using Osaurus on a Mac? Use the MLX bundle OsaurusAI/Diplodocus-preview-JANG6M instead. Same weights, same template, same reasoning toggle. Requires llama.cpp with K2 Horizon support (ggml-org/llama.cpp#29535, merged 2026-10-06). Older builds, and apps that bundle an older llama.cpp, will not load the k2-horizon architecture. Trained rounds R8–R11 on top…

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

Dolphin-Mistral-24B-Venice-Edition-RYS-S

Tony Lai

Dolphin-Mistral-24B-Venice-Edition-RYS-S is a merge of the following models using LazyMergekit

Built with Fish Audio. Experimental native audio.cpp conversions of Fish Audio Drama 3, prepared from checkpoint revision dd64a85cd82e9521f7202610ede97b367fc83946. All packages use the same trained model architecture and checkpoint. Q2, Q3 and Q4 are mixed quantizations. Q80 stores scaled 8-bit integer weights; it is different from the FP8 representation of the original checkpoint's experts. All standalone packages retain FP16 audio tower and waveform codec weights and FP32 normalization vectors. They embed the configuration, tokenizer, codec, model contract and license, so inference does not require the original checkpoint directory. This repository distributes the four runnable quantized…

Model · Image to video

DreamX-Creator-gguf

EvoAwaken Workshop

Community GGUF conversion of GD-ML/DreamX-Creator for the native CPU runtime in rust-model-inference. DreamX-Creator generates synchronized video and audio from a first frame and a text prompt. These files also contain the optional 2x video refiner components. Both files are required and must be used together. The runtime verifies their shared pair ID before inference. Build rust-model-inference, then run: Add --dry-run to validate the model pair and inspect the memory estimate without running generation. Full inference produces base video/audio/mux outputs and, with --refine, refined video/mux outputs. The pair is exported from the original DreamX-Creator checkpoint with the repository's…

DriveTwinLLM is an instruction-tuned, physics-grounded automotive diagnostic language model built on top of Qwen/Qwen2.5-3B-Instruct. It was fine-tuned using LoRA on NVIDIA B200 GPU hardware (180 GB VRAM) to achieve 98.13% evaluation token accuracy on real-world vehicle trouble codes, live ECU telemetry, and OEM service manuals. The model enforces strict zero-hallucination guardrails and features a specialized Dual-Persona Architecture: - Diagnostic computer scan fee ($90 – $130) - Certified shop labor ($120/hr flat-rate standard) - Strictly refuses unmonitored sensors (transmission fluid temperature, TPMS, cabin humidity) and invalid codes (P9999), redirecting the user to monitored engine…