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.
11,122Models
3,164Datasets
285Papers
6,792Publishers
11,852Sourced relationships
Updated 2026-10-18 · How the library is built
11,122 models, sorted by most downloaded.
intfloat/multilingual-e5-small fine-tuned by Valnivo Labs as a classifier over the Valnivo copilot's fixed library of answers: it returns the id of an explanation, a screen or a fixed refusal, and nothing else. Trained only on questions written for that purpose — never on anybody's data. 8-bit ONNX (onnx/modelquantized.onnx) for transformers.js; the input is prefixed query: as for e5. ONNX Runtime's WebAssembly is copied under ort/. It gives no financial advice and is not a general model: outside the Valnivo app its labels mean nothing.
Vani is a Cloud + Local Hybrid multilingual voice-to-voice assistant supporting all 22 Constitutionally Recognized Indian Languages using state-of-the-art speech and language models: 1. Cloud / Browser WebRTC Mode (Hugging Face Spaces, Mobile, Any Browser): - Streams your microphone audio directly through the browser (gr.Audio(streaming=True)). - Evaluates speech boundaries in real time via LiveKit Silero ONNX VAD. - Auto-plays crystal-clear synthesized Indic speech in your browser with 3-Layer Acoustic Echo Protection so the assistant never hears itself. 2. Local Zero-Click Hardware Mode (Windows / Desktop GPU): - Captures audio directly from your OS soundcard via sounddevice.InputStream…
Authorized bug bounty (BBP), vulnerability disclosure (VDP), and contracted pentest copilot. - Fine-tuned only on local RTX 5070 (glasseye) — no cloud GPUs - Continued from GlassEye BBP v1 with VECTOR Analyst system prompt + auth hardening (no invented program IDs) Scope parsing, in-scope methodology, CVSS/CWE report drafting, coordinated disclosure coaching. Unauthorized testing, ransomware, phishing kits, credential theft, production data dumps.
Authorized bug bounty (BBP), vulnerability disclosure (VDP), and contracted pentest copilot. - Fine-tuned only on local RTX 5070 (glasseye) — no cloud GPUs - Continued from GlassEye BBP v1 with VECTOR Analyst system prompt + auth hardening (no invented program IDs) Scope parsing, in-scope methodology, CVSS/CWE report drafting, coordinated disclosure coaching. Unauthorized testing, ransomware, phishing kits, credential theft, production data dumps.
Authorized BBP/VDP/pentest only. Never invent program IDs.
A vectorv1.1 network, Robotensor's two-arm Behavior Prompting Policy, submitted to Attune's Vector competition (Bittensor netuid 58). Its image encoders start from the Behavior Prompting Policy authors' own LIBERO policy (austinpatel/libero, MIT; Patel, Pekarek, Castro Hernandez and Song, 2026); the rest was trained on RoboTwin expert demonstrations of two Franka arms.
This repository contains the model described in the paper VectraYX-Vision-1B: A Sub-2B Spanish/LATAM Cybersecurity Vision-Language Model with Structured Visual Reasoning and Native Tool Use. VectraYX-Vision-1B is a sub-2B vision-language model specialized for Spanish/LATAM cybersecurity imagery. It couples a frozen SigLIP-so400m encoder to a 1.04B Spanish/LATAM security decoder via an MLP, and is designed to answer in Spanish, emit structured reasoning via native tokens, and invoke tools via the Model Context Protocol ( ).
A fine-tuned model of Qwen/Qwen2.5-14B-Instruct specialized in classical Vedic Astrology (Jyotish), trained on over 8,700 synthesized question-answer pairs and chart application examples derived from classical Sanskrit texts: Brihat Parashara Hora Sastra (BPHS) Mantreswara's Phaladeepika Bhrigu Nandi Nadi Classical varga, avastha, shadbala, and dasha frameworks. Run directly on your local machine using Ollama: Or download the.gguf file directly and use with llama.cpp
This model is a fine-tuned version of unsloth/Qwen2.5-14B-Instruct-bnb-4bit. It has been trained using TRL. This model was trained with SFT. - PEFT 0.21.2
Inference on a single T4, end to end, in the browser. 800 million or 4 billion parameters. A 73,728-token context. Images. Running on your own hardware. That combination is the point: a decision model small enough to sit inside your own process, that can read a whole contract in one pass and look at a picture, and that still beats a hosted reference model on the tasks below. A new architecture with its own training and calibration method. The language model inside it is frozen and never trained; everything learned lives in a small physics engine on top of it. Both are in this repository and take the same code path. The 0.8B sits at the root because that is where it has always been; the 4B…
A vision-language-action policy for a simulated bimanual lab task, from LIDAR. It controls the upper body of a Vega-U humanoid robot: two 7-DoF arms and two 20-DoF Wuji Hand 2 hands. The task is the first half of a pipetting routine: The policy was fine-tuned from the openpi pi0.5 base model on demonstrations that a scripted teacher generated in the same simulation (Isaac Sim 6.0.1 / Isaac Lab, GPU PhysX), across randomized object layouts. - head (ego) camera; - a right-wrist camera looking back at the thumb and the plunger; - left wrist camera; - right wrist camera. Use the companion simulation repository, vega-wuji-pipette-sim. Its README covers: - installing Isaac Sim 6.0.1, Isaac Lab…
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.