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 2026-09-18 · How the library is built
2,760 models, sorted by most downloaded.
This model is a fine-tuned version of cosmos1030/gmp-kd3e-1-s80pct-lr1e-420260916220740 on the trl-lib/ultrafeedbackbinarized dataset. It has been trained using TRL. This model was trained with DPO, a method introduced in Direct Preference Optimization: Your Language Model is Secretly a Reward Model.
Official weights for the Dual-Loop Cognitive Controller on Qwen/Qwen3.5-2B ($D=2048$, Layer 11 hook, ~110M parameter deliberation adapter). The Dual-Loop Controller provides hardware-aligned, non-autoregressive System 2 deliberation directly within the latent residual stream of modern language models. It enables models to recursively deliberate in continuous hidden space without generating costly Chain-of-Thought (CoT) text tokens, eliminating KV-cache explosion and 30–60 second generation latencies. This adapter is strictly designed, calibrated, and hooked into the architectural dimensions of Qwen/Qwen3.5-2B: 1. Cognitive Matrix Helper (Tversky Elimination-by-Aspects): - Evaluates options…
从零训练的 38.85M 参数英文小模型，消费级显卡（RTX 4060 Laptop 8GB）单卡训完。 本仓库同时提供了 GGUF 格式（由 model/litmodel.pth 直接转换，模板保持与训练一致），可直接用 Ollama 导入： scripts/chat.sh 里的三种问法： 在 Python 里直接用： - 只会做被交代的任务：给它一句裸问题（如 What is a volcano?）它不会回答；必须像上面那样把任务写清楚。 - 上下文上限 512 token：prompt + 生成长度之和；超出会报 Cannot forward sequence of length T。 - 预训练语料：TinyStoriesV2-GPT4（CDLA-Sharing-1.0）与 Children-Stories（用户自备，含 GPT 生成内容） - 本包模型权重由上述数据从零训练得到，仅供研究与个人使用；若再分发请一并保留此说明与 MODELCARD.md。
Per-fold leave-one-subject-out (LOSO) checkpoints for the scalp-EEG vs. in-ear-EEG sleep-staging benchmark in three datasets x two modalities x up to seven models, one checkpoint per held-out subject. 386 checkpoints, 14.3 GiB. These are fine-tuned task checkpoints, not pretrained encoders. Each file is the adapter statedict selected by the best validation balanced accuracy for that fold — the test set is never used for model selection. The tree mirrors the benchmark repo's own runs/, so a download can be dropped straight into runs/ and every training/eval script resolves it unchanged: is scalp-eeg or in-ear-eeg; fold-sub-sub-XXX names the held-out test subject. Only seed1 is published.…
Frozen V-JEPA 2.1 tokens on LED, tokenised as k frames (histogram) (16 time bins of 1.25 ms paired into 8 tubelet tokens per cell), scored per event: each event looks up the token at its (time bin / 2, patch) and is judged with its own E15 neighbourhood patch. Routes in this repo: Pixel-AC fuses the per-event scores into one decision per pixel and chunk (integrated/ /; result.json carries the Pixel-AC metrics with the AC-only metrics under ac). Run-name suffixes: nokv = reader bypassed, lb = scalar Local branch on, vitbase = ViT-B/384 tokens. All units: 6,000 training chunks, 4 epochs, official 713 chunks at a fixed threshold of 0.5, no selection on test. Reference on the same protocol: the…
EMENDER E97 4B is a 4,045,972,080-parameter hybrid recurrent language model trained from scratch (~100B tokens), then developed into an observation-driven conversational tool agent through a verified post-training program: task proposals are executed by a real coding-agent harness (Pi), and only deterministically verified trajectories enter training. Every capability claim below was measured on frozen evaluation panels under a fixed protocol, with full audit receipts retained in the release folders. The first checkpoint in the program's history to pass its frozen dual gate. All thresholds passed on a single measurement with no re-rolls: OpenHands-compatible environment, autonomous…
This model is a fine-tuned version of fpadovani/eng-latn-10mb-ppt-Dp-100mb-packedseed3407. It has been trained using TRL. This model was trained with SFT.
This model is a fine-tuned version of fpadovani/eng-latn-10mb-ppt-Dp-10mb-packedseed3407. It has been trained using TRL. This model was trained with SFT.
Trained with LeRobot.
Trained with LeRobot.
Quantized INT4 weights designed to run locally on ESP32-S3 with 16MB Flash and Octal PSRAM. This model repository contains the binary weights, metadata, license, and tokenizer assets for the esp32-sandbox project (projects/s3-tiny-stories). - README.md - Model Card and documentation - LICENSE - Apache 2.0 License - metadata.json - Hardware, quantization, and model architecture metadata -.bin - Compiled INT4 model weights - tokenizer.json - SentencePiece / BPE vocabulary configuration Download the binary file (model.bin) and flash it directly to your ESP32-S3: Refer to the esp32-sandbox repository for firmware building, flashing, and serial monitoring.
My first hugging face read me file for reading files
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).
This repository provides an instruction-tuned causal language model for text generation and chat-style prompts. The configured snapshot destination is available here. Use a current release of transformers to load the model and tokenizer: For long inputs, use context settings supported by the installed runtime and account for available memory. See LICENSE for the applicable terms.
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.