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 2026-10-20 · How the library is built
11,734 models, sorted by most downloaded.
This model was converted to GGUF format from black-forest-labs/flux-3-image-klein-pup using latest llama.cpp Refer to the original model card for more details on the model. Install llama.cpp through brew (works on Mac and Linux) Invoke the llama.cpp server or the CLI. Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well. Step 2: Move into the llama.cpp folder and build it with LLAMACURL=1 flag along with other hardware-specific flags (for ex: LLAMACUDA=1 for Nvidia GPUs on Linux).
This is a PEFT LoRA derived from black-forest-labs/flux-3-image-klein-base. No validation prompt was used during training. Note: The validation settings are not necessarily the same as the training settings. You can find some example images in the following gallery: The text encoder was not trained. You may reuse the base model text encoder for inference.
This is a PEFT LoRA derived from black-forest-labs/flux-3-image-klein-base. No validation prompt was used during training. Note: The validation settings are not necessarily the same as the training settings. You can find some example images in the following gallery: The text encoder was not trained. You may reuse the base model text encoder for inference.
Model · Text to image
flux-3-klein-image-photo-aesthetics-distilled-assisted-1024-v2-kohaku-fa
This is a PEFT LoRA derived from black-forest-labs/flux-3-image-klein. No validation prompt was used during training. Note: The validation settings are not necessarily the same as the training settings. You can find some example images in the following gallery: The text encoder was not trained. You may reuse the base model text encoder for inference.
Model · Text to image
flux-3-klein-image-photo-aesthetics-distilled-assisted-512-v2-kohaku-fa
This is a PEFT LoRA derived from black-forest-labs/flux-3-image-klein. No validation prompt was used during training. Note: The validation settings are not necessarily the same as the training settings. You can find some example images in the following gallery: The text encoder was not trained. You may reuse the base model text encoder for inference.
Complete OpenVINO IR package for the distilled FLUX.3 Klein image-generation pipeline. The transformer uses mixed INT4/INT8 weights; the Qwen3-VL text encoder uses mixed INT4/INT8 weights; both VAE paths use INT8 weights. The tokenizer, scheduler configuration, runner, license, and sample are included in the repository. The package is self-contained: inference downloads this repository only, or accepts a local copy with --model. The complete bundle is about 4.96 GiB across 37 files. All neural-network components are OpenVINO IR. vaebnstats.npz contains the FLUX.3 VAE batch-normalization statistics required by the pipeline. - validation1024seed42.png is a fixed-seed, 1024×1024 end-to-end…
FLUX 3 Action (flux3) is Black Forest Labs' video-action model: the FLUX.3 video trunk with an action modality that is denoised jointly with the next video frames, finetuned per robot embodiment with fresh action heads. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot flux3 guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end…
FLUX 3 Action (flux3) is Black Forest Labs' video-action model: the FLUX.3 video trunk with an action modality that is denoised jointly with the next video frames, finetuned per robot embodiment with fresh action heads. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot flux3 guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end…
A LoRA adapter for black-forest-labs/flux-3-action-so101, trained on the team's teleop recording, 110 episodes of two tasks: ERH Physical AI Hackathon, October 2026. For the cups task only, see also LeRobot with FLUX 3 (the same install you use for the zero-shot run). A GPU with about 25 GB free. Access to the base model on Hugging Face (accept its license once). The same SO-101 arm and calibration as the recording. jointunits.json holds this arm's joint offsets. 1. Download this repo and install the robot plugin (no other dependencies): 2. Start the rollout (pick one task text): --robot.imagesize="[480, 640]" is required. The model needs both cameras at one size; without it the rollout…
A frozen, non-learning recursive network built on the real fruit-fly MaleCNS connectome (Janelia 2024). Feed any stimulus, get an organic, structured "instinctive" signal that is more alive than noise, less predictable than a script. Not AI. Not a brain. Not learning. Just real neural wiring, frozen. - Real MaleCNS connectome subset (1,587,930 edges, ~25 MB) - 4 tuned presets: escape / sugar / startle / explore - Zero training. Weight-frozen. Deterministic per seed. https://roblitz-spec.github.io/fly-instinct/ - Full 1.05GB connectome + commercial license: https://robloxer31.gumroad.com/l/Fly-Instinct
Browser bundle of a chess-playing fruit-fly connectome — the complete FlyWire FAFB v783 brain (138,639 neurons, 15,091,983 synapses, wiring and excitatory/inhibitory signs frozen) with a learned encoder, one positive gain per synapse, per-neuron homeostasis, and a decoder, trained on Stockfish-annotated Lichess positions. Recovers Stockfish's top move 28.3% of the time on held-out positions (ChessFly, the original recipe by Maxime Labonne: 30.4%). Play it: https://flymate.naklitechie.com · code: https://github.com/NakliTechie/flymate Files are raw little-endian arrays consumed by the page's WebGPU kernels; meta.json carries shapes, the 1968-move vocabulary and torch reference outputs for…
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.