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 2026-10-24 · How the library is built
12,870 models, sorted by most downloaded.
Model · Image and text to text
Baanzon-33street-3.8-27B-Uncensored-Aggresive-MTP-GGUF
GGUF distribution of the 27B vision-language model with an aggressive, uncensored behavioral profile, featuring multi-token prediction (MTP) speculative decoding acceleration for fast inference in llama.cpp. - Uncensored / aggressive personality — minimal refusals, combative direct tone - Multimodal (image + text) — vision projector included (mmproj), supports image input - MTP speculative decoding — token-drafting heads for substantially faster generation - Long context — 262,144-token context window (32K-tuned FastMTP variant included) - Multilingual — English / Chinese / multilingual - Quantized for local inference — ready-to-run GGUF quants, no PyTorch weights required All files are…
BaiHu-V1-Flash is an SSA (Sparse-attention + SubQ) retrofit of Qwen/Qwen3-0.6B-Base, fine-tuned on 1,294 bilingual multi-turn dialogues whose purpose is generalizing from a single worked example — the model is shown one worked rule / format / method in the first turn, and later turns ask it to reuse that rule on a new case it has never seen. Both revisions share the same SSA architecture, base weights and license. Every layer keeps the base model's MLP / RMSNorm weights and replaces full attention with an SSA layer built from three parallel paths: The previous revision compressed the whole prefix into a single mean vector. A query could only see one undifferentiated global average, the…
Conversational Writing Model｜口语化写作模型 Baihua-Gemma4-E4B 白话 Baihua 是Roland Wayne基于 Google Gemma 4 E4B-it 经过后训练的中文口语化写作模型。把已有内容，或整理好的事实、观点和提纲，写成像人类口述的正文，减少模板化、机械化的表达。 Roland Wayne 是内容创作者，也是白话 Baihua 的训练作者。在这个项目中，他负责确定表达目标、筛选训练材料，并通过实际测试和人工修改评估模型输出。 X：https://x.com/rwayne Github：https://github.com/ozrwayne - 微调方法：LoRA，rank 16，alpha 32；在前一轮风格 adapter 基础上继续进行配对微调。 - 完整权重：BF16 Safetensors，LoRA 已合并，加载完整权重时不需要再叠加同一 adapter。权重文件合计约 15.88 GB；含配套文件的完整包约 15.91 GB（十进制）。 - Ollama 性能测试使用约 14.94 GB 的 BF16 文本 GGUF 运行副本，归一化等少量张量为 FP32；它不包含纯文字推理不需要的音频和图像编码模块。原完整 Safetensors 模型保持独立。 - E4B 是基础模型的官方规格名称，不表示总参数只有 4B。官方标注约 4.5B 有效参数、含嵌入约 8B；当前 Ollama 文本副本实测为 7,463,013,674 个参数，约 7.5B。 - 配置总上下文：131,072…
This is a collection that I have been trying to gather from LoRas that were banned or were from the very start way "too spicy" to ever upload into Civitai so hopefully users could try find some of them here for what they want to do. Im focusing mainly on LoRas for PonyXL, IllustriousXL and Anima. Because write what each LoRa respective trigger words is way too much, I uploaded a LoRa Viewer that you can use to load each of them to see what words trigger each of them. At the time of writing I can be contacted through my discord server perhaps if Im not quite busy with something (the usual) http://chat.nx34.xyz I also have another project over here: http://ubtl.nx34.xyz
Binary image classifier that answers one question from a fixed bed-camera view: is there a printed object on the print bed? Built for autonomous 3D-print-farm fulfillment (auto-releasing completed prints without a human looking at every camera frame). Live inference on frames from two printers never seen during training. Banner shows the model's actual output: ~1,200 frames from 29 farm printers. Labels derived from operational events and then manually audited by the operator (7 corrections applied via a review tool): Convention: clear means a clean bed — filament scraps and debris count as occupied. Split is group-aware (all frames from one printer/camera go to exactly one split), so test…
weighted/imatrix quants of https://huggingface.co/TheDrummer/Behemoth-X-123B-v2.2 For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/Behemoth-X-123B-v2.2-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…
static quants of https://huggingface.co/ProCreations/BetterWright-K2-Horizon-7B-Uno For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants are available at https://huggingface.co/mradermacher/BetterWright-K2-Horizon-7B-Uno-i1-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…
weighted/imatrix quants of https://huggingface.co/ProCreations/BetterWright-K2-Horizon-7B-Uno
A System One decision model with open weights. Give it a document (text, email, ticket, log or JSON) and typed questions; it returns typed answers (yes/no, a choice between options you describe, or a score on levels you define) with a calibrated probability for every option, in one pass. It never generates text, so there is nothing to parse and nothing to hallucinate. It runs on your own machines: your documents never leave them. About 26B parameters, built on Google Gemma 4 26B-A4B (mixture of experts, about 4B active per token). Everything it needs is in this repository, so nothing else is downloaded. English text, up to 4,096 input tokens; needs one GPU with about 60 GB of memory (bf16).…
Bielik-11B-v3.0-Instruct post-trained for the Polish history matura (extended level) during the Warsaw Model Trainers alpha=64) on top of speakleash/Bielik-11B-v3.0-Instruct. closedem = exact match on closed items. Graded scores (deepseek-flash-high@think via benchmark/bench.py) are reported separately.
Bielik-11B-v3.0-Instruct post-trained for the Polish history matura (extended level) during the Warsaw Model Trainers alpha=64) on top of speakleash/Bielik-11B-v3.0-Instruct. closedem = exact match on closed items. Graded scores (deepseek-flash-high@think via benchmark/bench.py) are reported separately.
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.