This is a freeform motion transfer lora that is capable of transferring any motion to a different character or object. While there is no limitation on the video length, I don't recommend generating video that exceeds 30 secs - as the consistency will gradually fade after that. Please use the prompt to add a short description for your video. The prompt greatly affects the result, so if you are not getting the result you want - check your prompt. The full demo video can be view here: https://www.youtube.com/watch?v=TsSBBNKNeCA https://www.youtube.com/watch?v=ErwXHKmGx1o Please use the included workflows to get started…
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.
This seedoss model was trained 2x faster with Unsloth and Huggingface's TRL library.
This seedoss model was trained 2x faster with Unsloth
Lapras (Latent Post-trained Reasoning Across Series) is a post-training framework that equips time series language models (TSLMs) with latent reasoning. A model trained with Lapras reasons through a sequence of continuous thoughts in the joint time series–language space, producing text only for the final answer. It learns this through teacher–student self-distillation from reference chain-of-thought (CoT) traces. This repository holds the 15 Lapras checkpoints reported in the paper: three TSLM backbones, each finetuned separately on five time series question answering tasks. Each backbone folder has one checkpoint per task: laprasecg, laprassleep, laprashar, laprastsr, laprasengine. A…
LastKV for scene-level novel-view synthesis, trained on DL3DV with a three-stage progressive-resolution curriculum: 128 × 128 → 256 × 256 → 512 × 512. This repository releases the third-stage checkpoint at 512 × 512. A 24-layer Transformer with hidden width 768, patch size 8, and 184.8M parameters. LastKV shares the final layer's historical KV cache across attention layers. Training uses BF16 autocast; the released model weights are stored in FP32. Stage 1 starts from random initialization. Its perceptual-loss branch continues from the 4,000-pass L2 checkpoint, preserving optimizer, scheduler, and step-counter states. At each resolution transition, only model weights are inherited; the…
LastKV for object-centric novel-view synthesis, trained on Objaverse with a three-stage progressive-resolution curriculum: 256 × 256 → 512 × 512 → 1024 × 1024. This repository releases the third-stage checkpoint at 1024 × 1024. A 24-layer Transformer with hidden width 768, patch size 8, and 184.8M parameters. LastKV shares the final layer's historical KV cache across attention layers. Training uses BF16 autocast; the released model weights are stored in FP32. Stage 1 starts from random initialization. At each resolution transition, only model weights are inherited; the optimizer, learning-rate scheduler, and step counters are reset. Checkpoint steps are stage-local forward/backward pass…
For the versioned v5.3 development protocol, named comparison suites, locked model selection, and paired statistics, see PyTorch implementation of LatticeSpike, the architecture in latticesnnhallucination.tex: online LLM hallucination detection by combining a Galois-connection Box-embedding lattice with a box-volume-modulated recurrent spiking neural network. (Only torch, numpy, scikit-learn are required. transformers is optional, for real-LLM signal extraction.) For development and the full unit suite: The demo trains unsupervised on (mostly) grounded synthetic sequences and reports token-level AUPRC, lead time, and per-channel gate attribution. The consolidated real-data diagnostic…
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).
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).
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).
weighted/imatrix quants of https://huggingface.co/apple/LensVLM-9B For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/LensVLM-9B-GGUF This is a vision model - mmproj files (if any) will be in the static repository. 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…
LibraMind is a 565M-parameter chat model pretrained from scratch by alby13 on a single consumer GPU (NVIDIA RTX 4090). It is a hybrid architecture that interleaves 18 Gated DeltaNet linear-recurrent layers with 6 Gated Attention layers (a repeating DDDA × 6 pattern), with Block Attention Residuals and logit soft-capping, trained with the Muon optimizer. It went through a full five-stage pipeline: pretraining, midtraining, supervised fine-tuning, preference tuning, and RL with verifiable rewards. - Research on small hybrid recurrent/attention language models - Studying low-resource, single-GPU training pipelines (pretraining through RL) and the Muon optimizer at small scale - Experiments…
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.