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 2026-09-20 · How the library is built
3,247 models, sorted by most downloaded.
LoRA adapters for Sage v3.6, a Twi-speaking AI assistant fine-tuned from ghananlpcommunity/MiniCPM5-1B-Twi with MLX LoRA (rank 16, 2000 iterations).
Byte-level GPTs that dream up login-style usernames (darkphoenix, Cargan66), trained on the ks46/usernames corpus. Every folder is one trained model: its checkpoint (ckpt.pt), the export for the single-threaded x86-64 batch kernel ndgen (model-int7.ndq, model.ndq), the kernel's golden reference NLLs, eval.json, manifest.json, samples.txt and a card with the details. Vocabulary is 256 bytes with byte 0 as the stop token; the held-out split is xxh364(name) % 64 == 0, so bits/char are comparable only between models trained on the same corpus.
Authorized bug bounty (BBP), vulnerability disclosure (VDP), and contracted pentest copilot. - Fine-tuned only on local RTX 5070 (glasseye) — no cloud GPUs - Continued from GlassEye BBP v1 with VECTOR Analyst system prompt + auth hardening (no invented program IDs) Scope parsing, in-scope methodology, CVSS/CWE report drafting, coordinated disclosure coaching. Unauthorized testing, ransomware, phishing kits, credential theft, production data dumps.
Authorized bug bounty (BBP), vulnerability disclosure (VDP), and contracted pentest copilot. - Fine-tuned only on local RTX 5070 (glasseye) — no cloud GPUs - Continued from GlassEye BBP v1 with VECTOR Analyst system prompt + auth hardening (no invented program IDs) Scope parsing, in-scope methodology, CVSS/CWE report drafting, coordinated disclosure coaching. Unauthorized testing, ransomware, phishing kits, credential theft, production data dumps.
Authorized BBP/VDP/pentest only. Never invent program IDs.
VinaySLM is a small language model (SLM) built from scratch in PyTorch to learn how decoder-only Transformers work end to end -- tokenization, embeddings, causal self-attention, training, and generation -- without importing a pretrained architecture. This is not a production-quality language model. - A tiny, hand-written, decoder-only (GPT-style) Transformer. - 466,176 parameters. - Trained on a ~600-token toy corpus of AI/programming sentences. - Packaged here as a custom Hugging Face model (trustremotecode=True) so it can be loaded and inspected through the transformers ecosystem, while the actual Transformer implementation (modelingvinayslm.py) is a thin wrapper around this project's…
本目录是一个与官方源码隔离的实验框架。它只读使用 ILVR 的定制 Transformers 和训练类，不修改： -../../ILVR -../../mlrm-LEAD -../comt-test 本实验不使用早期的图像 two-pass 方案。原有 Monet/CoMT answer-teacher 三个实验仍只在推理时使用熵控制；新增的 CoMT/Monet Stage2 实验会在训练 rollout 中用熵决定 latent 位置，但都不加载 EMA teacher 或辅助图像。 默认评测矩阵训练两个 Monet-SFT-125K 模型： 1. monetilvr：完全使用官方 ILVR EMA teacher query。 2. monetanswerteacher：student batch 与模型 1 相同，仅在 EMA teacher 选择 helper patches 时增加标准答案表示。 另有一个独立的 comtanswerteacher 训练：只把数据切换为 CoMT，标准答案仍仅供 EMA teacher 使用，student 不可见。三个训练过程都不使用熵控制；熵控制只在训练完成后的推理/评测阶段启用。 - natural：模型自然生成 ； - entropy：高熵且满足窗口/冷却条件时，单次生成中强制 ，之后的固定 8 个 latent hidden-state feedback 继续由官方 ILVR generation loop 完成。 评测包括 VStar、HR-Bench 4K、HR-Bench 8K 和…
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.