SAVRN Model Hub
Open-Weight Models
An open-weight model is an AI model whose trained weights are published for anyone to download. The weights are what the model learned in training. With a copy of them you can run the model on hardware you control and train it further on your own data.
Open weights are not the same as open source. Many publishers release the weights without the training data or code, and the license sets what you may do with the model. This library puts each model's full card, architecture, files, license and published evaluations on one page.
Updated 2026-10-22 · How the library is built
12,283 models, sorted by most downloaded.
checkpoints/finalcheckpoint: LoRA adapter and tokenizer files.
This is the ONNX version of Valen-Team/Valen-0.8B. Qwen3.5 with a two-layer MLP-Mixer decision head. Supports text, images and videos, and returns Choice, Noul and Score decisions. Multiple questions can share one state encoding with execution="sharedstate". Trained on millions of samples. Install Python 3.10+, PyTorch, Transformers 5.4.0, torchvision, Pillow and av. Flash Attention 2 is optional with a compatible CUDA build. The model contains its tokenizer, processor and custom inference code; a separate base-model download is unnecessary. Use attnimplementation="flashattention2" for Flash Attention, or execution="question" for independent questions. Video defaults to 16 frames.…
Error guarantees for small medical language models keep their promise by staying silent. Koushik Swarna · Invariance Labs · [email protected] A safety certificate lets a model answer only when it is confident, and promises that the error rate on its answers stays under a budget α in all but δ = 5% of deployments. We prove three short facts, test them on 10 small open models, 4 medical benchmarks, and 40,000 simulated deployments, and find that all three hold. Use the certificate on your own scores: 1. Score: python scripts/scoremodels.py --model Qwen/Qwen2.5-1.5B-Instruct --questions medqa.jsonl --out scores/qwen1.5bmedqa.npz. Add --nf4 for 4-bit models. Confidence is the softmax…
A ~1.1B-parameter decoder-only transformer, pretrained fully from scratch — original tokenizer, original model code, original training loop. Developed by Dark Studio. Vanessa-1B is an independent large language model project: every part of the stack — the byte-level BPE tokenizer, the transformer implementation, and the distributed training loop — was built from first principles rather than adapted from an existing checkpoint or wrapping an existing transformers architecture class. It is the second generation of the Vanessa project, scaled up roughly 9x from an initial ~124M-parameter proof of concept. Data. FineWeb-Edu (the education-filtered variant of FineWeb), targeting 25B tokens.…
static quants of https://huggingface.co/Velvet-Capital/VelvetFlash
Community abliteration and quantization of tsinghua-sigs-robot-lab/VeriLoop-E2, fixed revision 9379d199adcc44acdf836558037163878e3a37ae. This is not an official Tsinghua release. Model architecture is Qwen35ForConditionalGeneration; the upstream names the base Qwen3.8-27B. Q4KM main model and separate F16 vision projector. No importance matrix was used. Full-precision conversion intermediates are excluded from this release. A bounded six-candidate Heretic 1.4.0 search selected per-layer strength 1.2; 96 of 128 targeted residual-output matrices changed and 32 zero updates remained unchanged. The small selection set improved from 16/16 to 4/16 refusal markers, missing its original ≤1…
Community abliteration and quantization of tsinghua-sigs-robot-lab/VeriLoop-E2, fixed revision 9379d199adcc44acdf836558037163878e3a37ae. This is not an official Tsinghua release. Model architecture is Qwen35ForConditionalGeneration; the upstream names the base Qwen3.8-27B. AutoRound W4A16, symmetric group 128, 256 public Pile calibration samples, 512 tokens, 200 iterations, batch 4. Eligible weights are INT4; small hybrid projections, activations and retained weights are BF16. All source tensors are represented; the 333 vision tensors are preserved exactly. A bounded six-candidate Heretic 1.4.0 search selected per-layer strength 1.2; 96 of 128 targeted residual-output matrices changed and…
Vertex Core is a specialized AI software-engineering model developed by Vertex Coders LLC. It is based on Google's Gemma 2 9B IT and fine-tuned using LoRA/QLoRA techniques to improve its behavior for software-engineering workflows, code generation, code modification, debugging, security-oriented code analysis, and tool-oriented development tasks. Vertex Core is designed to serve as the coding and reasoning core of agentic software-engineering systems. Vertex Core is under active development. Vertex Core is intended for applications involving: The model is particularly intended to operate as part of an agentic system where the surrounding application provides access to repositories, files…
Rethinking World-Action Model for Compositional and In-context Robotic Manipulation This repository contains the selected RoboTwin action-policy and image-goal planner inference weights. The planner lineage is normal 30k EMA → 40k ROI updates → continuation to ROI step70k. Its complete history is approximately100k optimizer updates. It uses no I2I skipping (nextsubgoaltailfraction=0). The ROI/background loss ratio is4:1; expert geometry is used only to construct training-loss weights. Use the native runtime and launchers in the code repository. These are PyTorch Distributed Checkpoint (DCP) files, not an AutoModel.frompretrained or Diffusers pipeline export. Keep each model's.metadata and…
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).
This repository contains ViSkill-FrozenLake and its visual skill library, introduced in ViSkill: Reinforcing VLM Agents with Evolving Visual-Native Skills. ViSkill-FrozenLake is a visual agent model for FrozenLake, built upon Qwen2.5-VL-3B-Instruct. It represents successful interactions as reusable visual skill cards. Geometry-aware retrieval guides the agent, while PPO with skill-guided rewards and online skill distillation jointly improve the policy and skill library. Model files are stored at the repository root, with the accompanying visual skill library in skilllibrary/. For the full ViSkill agent pipeline, including skill retrieval and environment interaction, please refer to our code…
Model Collections
Hand-picked starting points, each with the reason it exists.
Collection · 4 entries
Embedding models for retrieval
Sentence and document embedding models used to build retrieval systems. Dimension and sequence length matter more than size here, and both come from the publisher.
Collection · 4 entries
Models that fit on one accelerator
Models whose publisher-reported parameter count puts them within reach of a single accelerator at common precisions. Memory needed depends on precision and serving configuration, so treat the parameter count as the starting point, not the answer.
Collection · 6 entries
Open-weight text models worth knowing
Widely used open-weight language models, chosen because each one is a distinct family rather than a variant of the one above it. Selection, not a ranking.
Collection · 3 entries
Speech and audio models
Recognition and synthesis models, grouped so the two directions are easy to compare.
Open-Weight Models Explained
What is an open-weight model?
An AI model whose trained weights are published for anyone to download, so it can be run, tested and fine-tuned on hardware the user controls.
Is an open-weight model the same as open source?
Not always. Open weights means the trained model can be downloaded. Open source usually also means the training code and data are available and the license allows broad reuse. Many open-weight models release the weights only.
Can I use an open-weight model commercially?
It depends on the license. Apache 2.0 and MIT allow commercial use. Other licenses limit it, for example to non-commercial use or below a set number of users. Every model page here shows its license.
How much memory does an open-weight model need?
About two bytes per parameter at 16-bit precision, so a 7-billion-parameter model needs roughly 14 GB for its weights, plus memory for the context it processes. Each model page lists its parameter count and the size of its files.
Related SAVRN Research
The hub sits beside SAVRN's market data and infrastructure research: what models cost to run, and what it takes to run them.
SAVRN Index
What open models cost to run
The same open-weight model priced by every host that serves it, per million tokens.
Research Hub
Data center trackers and maps
Moratoriums, permits, power, water and capital behind the facilities that run these models.
Method
How the Model Hub is built
Sources, evidence labels, refresh behaviour, and the limits of every comparison here.