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 2026-10-23 · How the library is built
12,579 models, sorted by most downloaded.
在仓库 Files 页面下载 H3-Studio-v20.6.7-Windows-Full.zip，完整解压后运行 H3WushuSim.exe，保留所有同目录 DLL。窗口应显示 v20.6.7；不要在压缩包内启动。Windows 版不要求安装 Node.js 或 Python，WebView2 Runtime 需要可用。 本仓库只发布 Windows 程序包（不提供源码包），因此无需安装 Node.js 或 Python。 本仓库只发布 Windows 程序包（不提供源码包），因此无需安装 Node.js 或 Python。 浏览器打开终端给出的地址。上传 EXE 到 Hugging Face 并不会使 EXE 直接在网页上运行。本说明用于软件文件分发；要部署成 Space 还需另做服务启动与部署配置。 3. 在右上角 API 设置中配置语言模型。勾选全自动，点“开始 / 下一步”。 每段与镜头采用 5–15 整数秒，整体时长贴近设定；文戏和武戏可拆成多场。项目中的资产、镜头、画布与图片选择彼此独立。Agent / SKILL 页面可查看来源、修改提示词规则并用于后续调用。 模型权重不包含在 ZIP 中。Qwen 需要安装对应模型和节点；llama.cpp 使用 GGUF。Windows 语言设置提供模型目录扫描，默认 I:\llama-cpp\models。模型下载工具提供断点续传与 SHA256 校验。 统一设置可下载官方 Qwen FLOW、执行器及当前项目 FLOW 包。FLOW 包需要选择单条资产/片段执行，缺图草稿必须先补全绑定： 建议以 Dataset…
static quants of https://huggingface.co/XLearning-SCU/HERO-4B For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. 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…
HPC-Quantize is an experimental, MIT-licensed quantization engine for aggressively compressing large language models into extremely low-bit formats, with a particular focus on Q2-class quantization. Instead of choosing a quantization candidate solely from its local reconstruction error, HPC generates competing reconstructions, represents them in a compact discrete state space, models interactions between neighboring blocks, and performs a global sequence optimization before writing the final GGUF. The current production path is entirely classical. Earlier versions explored quantum-inspired state and measurement formulations; the current implementation uses a sequential Sieve, bounded state…
arabic - handwritten-text-recognition - htr - ocr - historical-manuscripts - kraken libraryname: kraken arman-kraken-ocr is a Kraken OCR/HTR model for line-level recognition of handwritten Arabic manuscripts. The model is based on the Muharaf-trained Kraken checkpoint muharafrecbest.mlmodel and was subsequently fine-tuned on the ArMan dataset for historical Arabic manuscript recognition. This checkpoint is a fine-tuned derivative of the Arabic HTR model trained on the Muharaf Corpus: The model was fine-tuned on ArMan as part of our work on historical Arabic manuscript HTR and the AraMS-28k dataset. If you use AraMS-28k in your research, please cite: If you use RefLAM or its annotation…
Harness Continual Learning Official implementation of Harness Continual Learning (HCL), a framework that enables frozen foundation-model agents to continually adapt their memory, routing, task interfaces, reusable capabilities, and optimization policies. Continual learning has largely focused on model parameters, yet modern agents can also adapt through a harness of prompts, memories, tools, skills, and routing rules. We introduce Harness Continual Learning (HCL), a paradigm in which this harness evolves around a frozen foundation model, and define the resulting loss of previously acquired behavior as harness-level forgetting. HCL comprises four execution-facing components: the Task…
static quants of https://huggingface.co/runelab-ai/Hati-Glint-SQL-9B-v1 For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. 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…
You should use HautlenceKubera to trigger the image generation. Download them in the Files & versions tab.
You should use HautlenceKubera to trigger the image generation. Download them in the Files & versions tab.
Whisper-medium fine-tune for sung lyrics by the HeartMuLa team, converted to WhisperKit's Core ML format for macOS and iOS. SwiftHeardLyrics (HeardLyricsHeart) downloads and runs it, on the GPU or the Neural Engine, and DemucsStudio uses it for lyrics of separated vocals. "Words different" compares with the float32 PyTorch model on 50 MUSDB18-HQ vocal excerpts; the word error rate is against the lyrics of 10 JamendoLyrics songs (2,792 words), with words aligned by a CTC aligner. The 6-bit model needs 1.0 GB of memory on an iPhone's Neural Engine instead of 1.8 GB, runs 1.5x slower on the GPU, and is what SwiftHeardLyrics downloads on iOS. - Converted with whisperkittools (audio encoder…
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.