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.
12,870Models
3,655Datasets
291Papers
7,691Publishers
12,980Sourced relationships
Updated 2026-10-24 · How the library is built
12,870 models, sorted by most downloaded.
DeepFatigue-Concrete-v4 es un modelo de aprendizaje profundo basado en una arquitectura de Perceptrón Multicapa (MLP) profunda con regularización Dropout (0.2). El modelo está diseñado y optimizado para la predicción de la vida útil remanente (RUL) y la degradación de la rigidez efectiva ante solicitaciones de fatiga mecánica cíclica en vigas y elementos estructurales de hormigón armado en edificios de gran altura e infraestructura de puentes. Este modelo está destinado exclusivamente como herramienta de soporte en el monitoreo de salud estructural (SHM) y mantenimiento predictivo. Limitaciones críticas: El modelo asume un comportamiento del material estrictamente conforme a las…
Build in progress. These files are not a release. No quant here has passed a generation smoke test. An upload only means the file finished writing. It becomes usable after it loads and produces coherent text, and that result is written in the test log below. Source checkpoint: Blackfrost-AI/CYBER-FROST-3.8-BF16. Architecture qwen4exp (Qwen4ExpForConditionalGeneration), from Qwen/Qwen3.8-Flash-Next. Text stack only. The vision tower was not converted. About 180B parameters, about 6B active. 48 layers, 512 experts, 10 experts per token, one shared expert. Configured context is 262144. The checkpoint includes one native MTP draft block. That block was trained before the final trunk behavior…
CachedSearch makes best-of-N seed search cheaper for video diffusion models. It generates every candidate with a training-free cache turned on, scores the cached drafts with a verifier, and regenerates only the winning seed at full compute. The method needs no training, so this repository holds no weights. It holds the official implementation: the cachedsearch Python package and two example scripts, copied unchanged from the GitHub repository at commit cc2a637. 1. Generate every candidate with a training-free cache enabled. 2. Score the cached drafts with the verifier the search uses. 3. Select the seed with the highest draft score. 4. Regenerate only that seed at full compute and return…
This Hugging Face repository hosts the released LoRA checkpoints for CapField-OPD, an on-policy distillation (OPD) framework that consolidates multiple reward-specialized teachers into a single student through a continuous capability field. Existing multi-teacher OPD methods route each prompt to a single teacher according to its semantic category, implicitly binding the desired capability to prompt content. CapField-OPD instead conditions the student on an explicit capability coordinate $\boldsymbol{\lambda} \in [0,1]^K$, whose axes control the strength of each capability. The base model, single-capability teachers, and joint-capability teachers serve as anchors of the field, and their…
Junhao Zhou 4, Zitian Chen 1, Linchao Zhu 2,† CaptchaAgent is a single 9B computer-use policy for all 20 CAPTCHA types in CaptchaArena, fine-tuned from Qwen3.5-9B. One set of weights, one tool schema and one observation format are shared across every type, with no per-task adapter or task-conditioned switching. Both subfolders hold full bf16 weights (9 safetensors shards each) with the tokenizer, processor and chat template, so each loads on its own. SFT/ is the final SFT checkpoint with the LoRA merged in (Table 5a of the paper); RL/ is the best GRPO checkpoint (Table 5b). CaptchaArena scores are on the 4,000-puzzle test split (20 types × 200), with 5 rollouts per puzzle and the unbiased…
A set of LoRAs for MiniMax H3 generating a consistent fictional character: Caridad "Cobre" Marie Rink, a 27-year-old Cuban-Cajun feature dancer (fictional; not based on any real person). - Long dark-espresso wavy hair with copper balayage; hazel-green almond eyes - Bright teasing grin, dimple on left cheek only; small beauty mark above right lip corner - Warm olive/golden-tan skin; curvy athletic dancer build; always clearly an adult (27) - Gold Virgen de la Caridad medallion necklace (always worn), gold hoops 1. Load the MiniMax H3: Reference to Video workflow (official template) 2. Add the LoRA via a LoRA Loader between the model loader and the sampler - strength 1.0 3. Prompt example…
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - PEFT 0.15.2
This public release contains four CR10 model checkpoints from the same Causal Forcing compressor training run: steps 2000, 3000, 4000, and 5000. Tensor files are preserved byte-for-byte with SHA256 checksums. Total model size is approximately 22.73 GB. The memory ratio is 0.1 (CR10). Original training provenance and exact file sizes are recorded in inventory.json. Each model/ directory is a PyTorch Distributed Checkpoint (DCP) containing both frozen net. generator state and trained netcompressor. state. Keep.metadata and all.distcp shards together, including empty rank shards. This release contains model weights and a training config; optimizer, scheduler, and trainer state are not…
This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
Check out our Blog Post and arXiv paper! The Cerebras-GPT family is released to facilitate research into LLM scaling laws using open architectures and data sets and demonstrate the simplicity of and scalability of training LLMs on the Cerebras software and hardware stack. All Cerebras-GPT models are available on Hugging Face. The family includes 111M, 256M, 590M, 1.3B, 2.7B, 6.7B, and 13B models. All models in the Cerebras-GPT family have been trained in accordance with Chinchilla scaling laws (20 tokens per model parameter) which is compute-optimal. These models were trained on the Andromeda AI supercomputer comprised of 16 CS-2 wafer scale systems. Cerebras' weight streaming technology…
ChakraTS is a zero-shot probabilistic time-series forecasting model from YHat Labs, served as a hosted API. This repository holds the model card; there are no weights to download. Send a batch of series and a horizon, get back nine quantiles (10% to 90%) for every future step. ChakraTS forecasts zero-shot: nothing is trained or fitted on your data, and fine-tuning on customer data is not part of this release. It accepts known-future covariates, works at any regular frequency from minutes to years, and returns the per-step prediction intervals that scheduling, procurement, bidding and inventory decisions depend on. Access is by API key, issued on request. One call forecasts a batch of…
weighted/imatrix quants of https://huggingface.co/Sorihon/Chaotic-Order-24B-V5 For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/Chaotic-Order-24B-V5-GGUF If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 See…
weighted/imatrix quants of https://huggingface.co/Sorihon/Chaotic-Order-V4 For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/Chaotic-Order-V4-GGUF If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 See…
View preprint at https://arxiv.org/abs/2208.00335 https://www.machinebrief.com/news/chatipc-the-future-of-interpretable-machine-learning-ja0b
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.