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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.

2,760Models
859Datasets
254Papers
1,692Publishers
5,040Sourced relationships

Updated 2026-09-18 · How the library is built

Most downloadedMost likedRecently updatedNameLargest

2,760 models, sorted by most downloaded.

Full fine-tune of lerobot/pi05base on a bimanual actuator-unboxing task, with a speed token in the text prompt: Part of a check whether pi0.5 can be conditioned through text tokens, as a precursor to advantage conditioning (RECAP, π0.6). Data (not on the Hub): 198 teleoperated episodes labelled slow, merged with 437 episodes of the same task demonstrated about 1.75× faster in another session, labelled fast (635 episodes, 371,523 frames; 50 fps, three 224×224 cameras, 14-D state and action). 30 % of training samples get unknown, the null prompt for classifier-free guidance. Training: 4,400 steps (about 3 epochs), batch 256, learning rate 5e-5, 10 % linear warm-up, cosine decay over the last…

Open weights 3.4B parameters lerobot
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Adaptive Geometry-Aware Fourier Neural Operator — with the complete controlled-evidence stack, extended depth sweep to 16, a second PDE family, a deformation baseline, a direct measurement of geometric forgetting, and a fully programmatic research paper (paper/agfnopaper.pdf). (mode truncation discards everything above the cut). A zero-gated, SDF-derived multiplicative modulation of the spectral weights restores the truncated band by spectral convolution — and the paper measures the whole story: diagnosis (proposition), fix (mechanism), consequence (probe). = 0.971× FNO's global error — the gain is NOT extra parameters (5.10M vs 4.81M) or channels (identical 3-channel inputs). −56% ring.…

Open weights cc-by-4.0 pytorch
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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…

Open weights apache-2.0 peft
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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 (…

Open weights apache-2.0 peft
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Model · Text classification

assign5autotrain

Harsha B Setty

libraryname: transformers - autotrain - text-classification basemodel: google-bert/bert-base-uncased f1macro: 0.7533020080884588 f1micro: 0.7533333333333333 f1weighted: 0.7533020080884587 precisionmacro: 0.7551310982162045 precisionmicro: 0.7533333333333333 precisionweighted: 0.7551310982162046 recallmacro: 0.7533333333333333 recallmicro: 0.7533333333333333 recallweighted: 0.7533333333333333

Open weights 109M parameters 512 tokens transformers
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Model Collections

Hand-picked starting points, each with the reason it exists.

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.

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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.