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).
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.
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).
A modern 150M-parameter decoder-only language model. Engineered by Sadat Sami · Released under the Apache License 2.0 808 AI is an open-source language model designed and engineered by Sadat Sami as part of his research into efficient, modern language model architectures. It is a compact decoder-only transformer built for fine-tuning on conversational and instruction-following tasks, and can be trained end-to-end on a single workstation GPU. - Grouped Query Attention (GQA) — 12 query heads, 4 key/value heads. - Query-Key Normalization (QK-Norm) — RMSNorm on q/k heads before RoPE. - Layer-wise Sliding Window Attention — alternating local/global pattern. - Rotary Position Embeddings (RoPE)…
ABot-Recon(流式前馈三维重建:视频 → 相机位姿 + 世界坐标点云 + 置信度)编译到 Axera AX650N NPU 的模型文件。 模型与算法来自原作者:amap-cvlab/ABot-Recon(主页 ),本仓库只做 Axera NPU 的格式转换。 代码与使用说明:https://github.com/AXERA-TECH/ABot-Recon-Axera 三个 axmodel 由 pulsar2 编译,芯片 AX650N,支持 AXCL PCIe 卡和 AX650 片上两种运行方式。 打开 http://:8011。Python 调用: 片上运行需要板子 CMM 预留 ≥ 6 GB。 这些权重转换自 ABot-Recon 发布的权重,后者派生自 Pi3;再分发须保留对 Pi3 与 ABot-Recon 的署名与 CC BY-NC 4.0 条款。商业使用需另行获得书面授权。 - presentvalid 封顶 8(应为 11),长序列有轻微漂移。
ACE-3-26B-A4B-Preview-260910 is a preview release of APMIC's ACE-3 model family, built for Traditional Chinese (Taiwan) enterprise scenarios and agentic workflows. The model is based on google/gemma-4-26B-A4B-it, a Mixture-of-Experts model with 26B total parameters and about 4B active parameters per token (the "A4B" in the name). APMIC has further optimized it to strengthen: - Traditional Chinese output in Taiwan usage (terminology, phrasing and orthography) The bundled generationconfig.json uses temperature=1.0, topp=0.95, topk=64. These follow the base model's recommended settings. The model can be served with any inference engine that supports Gemma 4 (for example vLLM). Because the…
ACE-3-F-26B-A4B-260804 is an enterprise-grade, production-ready large language model developed and optimized by APMIC for financial and regulated-business scenarios. The model is based on google/gemma-4-26B-A4B-it and has been further optimized through quantization and financial-domain adaptation to support high-reliability deployment in Traditional Chinese business environments. This release demonstrates APMIC’s end-to-end capability in: - Enterprise LLM optimization and deployment engineering - Deployment readiness for private cloud and on-premise AI infrastructures This model is specifically strengthened for finance-oriented prompts and terminology, with stronger alignment - Financial…
LeRobot 0.3.3 ACT policy for the RoboSynChallenge simulation task handlebasket (CobotMagic dual-arm robot, three 480x640 RGB cameras, 14-D joint state/action). - Initialised from RoboSynChallenge/ACTsimmixeroperating weights (normalisation statistics recomputed from the task dataset). - Fine-tuned for 14,000 steps on RoboSynChallenge/cobotmagicSimhandlebasket (1000 episodes), batch size 16, lr 1e-5, AMP; default ACT architecture (ResNet-18, dim 512, 4 enc / 1 dec layers, VAE, chunk size 50). - Local check (random setting, seeds 0 and 1, 10 episodes each): 11/20 successes, vs. 5/20 for the released RoboSynChallenge/ACTsimhandlebasket under the same protocol. Evaluate with the…
AD1E adds a persistent, writable numerical memory to a frozen Qwen3.5-0.8B. Experiences change the memory state; foundation weights and the original vocabulary remain unchanged. At inference, learned continuous memory blocks enter the model's computation and the model generates answers through its original language-model head. Previous messages are not replayed into the answer prompt. This is an Apple Silicon research runtime for controlled factual associations, numerical relations, and explicitly supplied relation chains. It is not a validated general-purpose conversational memory system. The complete foundation weights, prepared encoder, portable runtime, synthetic example state, and a…
Continuation of checkpoint backups for MarxistLeninist/AGILLM-4.3, which reached Hugging Face's 20,000-file repo cap on 2026-08-20 and is now a frozen archive (checkpoints there end at step2141734). - checkpoints/stepNYYYYMMDD/ - promoted serving-hold checkpoints (same format as the old repo). - live400B/stepNTIMESTAMP/ - periodic raw backups of the running continue-400B training run (block-sharded-zstd checkpoint + tokenizer + runstate), newest few kept for disaster recovery. Old checkpoint dirs are pruned automatically to stay under the file cap. This is the checkpoint to use. It is the first checkpoint trained after the fused cross-entropy gradient bug was found and fixed, and it is the…
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.