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.
11,442Models
3,247Datasets
285Papers
6,976Publishers
12,091Sourced relationships
Updated 2026-10-19 · How the library is built
11,442 models, sorted by most downloaded.
This archive preserves strategy code, Git history, training and evaluation data, replays, numerical artifacts, and experiment records from three Kaggriculture research workspaces. It supports inspection and restoration, including rejected experiments and incomplete validation. Original bytes and provenance are preserved. Uploads, remote integrity checks, and representative restoration checks completed before the user-authorized cleanup of backed-up bulk data on the original machine. The Collection contains this complete archive repository. Indexes distinguish models, numerical data, replays, and history within the repository; identical content is stored once. The 277 files under models/…
Kairos Sotos 2.0 is an offline, autonomous cybersecurity and privacy code auditor fine-tuned from Qwen/Qwen3.5-4B. It reports the audit verdict, evidence, smallest safe remediation diff, and exact CWE along with versioned framework IDs. This repository hosts the full-precision BF16 (16-bit Brain Float) GGUF binary, preserving 100% of fine-tuned weight fidelity with zero quantization loss. 100% Consumer GPU Resident: At 8.4 GB, the entire unquantized model fits into a standard 12 GB VRAM consumer card (e.g. NVIDIA RTX 3060), leaving ~3.6 GB of VRAM headroom for up to 8,192 tokens of context without GPU memory thrashing or CPU offloading. 30% Size Reduction vs 1.0: Reduced file size from 12.0…
This model was finetuned and converted to GGUF format using Unsloth. - kanana-1.5-8b-instruct-2505.Q4KM.gguf This was trained 2x faster with Unsloth
This model was finetuned and converted to GGUF format using Unsloth. - kanana-1.5-8b-instruct-2505.Q4KM.gguf This was trained 2x faster with Unsloth
This model was finetuned and converted to GGUF format using Unsloth. - kanana-1.5-8b-instruct-2505.Q4KM.gguf This was trained 2x faster with Unsloth
This model was finetuned and converted to GGUF format using Unsloth. - kanana-1.5-8b-instruct-2505.Q4KM.gguf This was trained 2x faster with Unsloth
This model was finetuned and converted to GGUF format using Unsloth. - kanana-1.5-8b-instruct-2505.Q4KM.gguf This was trained 2x faster with Unsloth
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
This model was converted to MLX format from Qwen/Qwen3.5-2B using mlx-vlm version 0.3.12. Refer to the original model card for more details on the model.
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.