Watch a race unfold, explore pit strategy, and reproduce the model training behind it. Pitwall combines an animated Formula 1 race center with a historical race simulator and an end-to-end machine-learning pipeline. Race Center opens with a recorded race. Cars follow actual OpenF1 position samples around a recorded circuit trace, with smooth interpolation between samples. and selected-car views. gear, throttle, brake, completed-lap sectors, weather, and race-control messages. rejoin position from current gaps, and an undercut watch for nearby rivals. pretraining and fine-tuning results. Historical replay is free, with OpenF1 coverage from 2023 onwards. The first load of a race downloads and…
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.
12,579Models
3,583Datasets
291Papers
7,578Publishers
12,850Sourced relationships
Updated 2026-10-23 · How the library is built
12,579 models, sorted by most downloaded.
Using llama.cpp release b11259 for quantization. Don't know which to choose? Grab Q4KM (17.20GB) - usually a good mix of size and performance. Download instructions available here First, make sure you have the Hugging Face CLI installed: The files marked true in the Split column above are stored as multiple parts in a folder. To download all the parts to a local folder, run: You can either specify a new local-dir (FINAL-BenchDarwin-27B-RSI-bf16) or download them all in place (./) These quants run with llama.cpp - installable in one line via llama.app: llama-server includes a built-in chat web UI, served at http://localhost:8080 by default. These quants were made with llama.cpp release…
Extends a picture on any side, and paints the gray corners of a picture you have turned. Pad the picture with flat gray #808080, give the padded canvas to FLUX 3 Image Klein as the reference, and the LoRA fills the gray with more of the scene while keeping your picture where it is. this repo as workflows/flux-3-image-klein-outpaint.json. Videos on YouTube @ausbossdev. Rendered with the workflow in this repo: FLUX 3 Image Klein distilled INT8, 16 steps, CFG 1, the same auto-caption prompt and seed in both columns, the original pasted back with Stitch Inpaint. Only the LoRA is switched. Each row: at 1.5. The amber boxes mark what goes wrong without the LoRA: FLUX 3 shifts the scene, so the…
FLUX.2-klein-4B (commit e7b7dc27f91d) with the transformer quantized to bitsandbytes NF4. Loads with Flux2KleinPipeline.frompretrained. Image editing with 1-3 reference images, and text-to-image. Text encoder (Qwen3-4B) not included. Load it from the base repo, or pass promptembeds. - kept in bf16 (~0.37 GB): xembedder, contextembedder, timeguidanceembed, doublestreammodulationimg, doublestreammodulationtxt, singlestreammodulation, normout, projout klein-quantize.py re-shards the single 7.75 GB file into 1 GB shards before quantizing, so peak memory stays around 2.2 GB. Runs on an 8 GB Jetson. Rendered with the same seed, initial noise and prompt embeddings in NF4 and bf16. 4 steps…
OpenVINO IR export of black-forest-labs/FLUX.2-klein-4B for local text-to-image generation and image editing in Fern. The source model uses one architecture for both tasks; this repository contains converted OpenVINO components, not the original Diffusers checkpoint. In Fern, install FLUX.2 Klein 4B from Preferences → Image models. Fern downloads the selected pinned export and uses the same OpenVINO IR on its supported OpenVINO devices. The first load compiles device-specific caches locally. For direct use with Fern’s runtime dependencies: To edit an image, pass an image to the pipeline’s image= argument together with the prompt. For Fern’s NVIDIA adapter, the OpenVINO NVIDIA plugin must be…
OpenVINO IR export of black-forest-labs/FLUX.2-klein-9B for local text-to-image generation and image editing in Fern. The source model uses one architecture for both tasks; this repository contains converted OpenVINO components, not the original Diffusers checkpoint. In Fern, install FLUX.2 Klein 9B from Preferences → Image models. Fern downloads the selected pinned export and uses the same OpenVINO IR on its supported OpenVINO devices. The first load compiles device-specific caches locally. For direct use with Fern’s runtime dependencies: To edit an image, pass an image to the pipeline’s image= argument together with the prompt. For Fern’s NVIDIA adapter, the OpenVINO NVIDIA plugin must be…
FORM-8B is a QLoRA fine-tune of Qwen3-8B (unsloth/qwen3-8b-unsloth-bnb-4bit) specialised for zero-shot FORM code generation. FORM is the symbolic manipulation language used for large perturbative QFT calculations (Grassmann The model is trained exclusively on execution-verified data: every training example was validated by running the generated FORM program through the FORM binary (deterministic-output match) plus an LLM consistency audit. Training config: QLoRA r=16, alpha=32, lr=2e-4, 5 epochs, 4-bit NF4 quantised base. is available from the author upon request. - Generating complete FORM programs from plain-English instructions - Assisting physicists with symbolic-algebra scripting…
Changes the Reference video identity face with the Reference(s) one(s) - The LoRA is pruned from blocks under the set fro treshold (less heavy, less lora artifacts, identical strength to trained base) - Workflow is shared (CRT-Nodes), you can use your own (make sure your input video respect the MH3 format) You can help me training cool models with runpod referal https://runpod.io?ref=u7b2habt so we both benefits, or with https://buymeacoffee.com/designedbycrt if you want to support these models. Thanks
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.