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 2026-10-19 · How the library is built
11,442 models, sorted by most downloaded.
This model is a fine-tuned version of None. It has been trained using TRL. This model was trained with SFT.
This model is a fine-tuned version of None. It has been trained using TRL. This model was trained with SFT.
This model is a fine-tuned version of None. It has been trained using TRL. This model was trained with SFT.
This model is a fine-tuned version of None. It has been trained using TRL. This model was trained with SFT.
A minimal PyTorch training loop that survives spot / interruptible GPU preemption. Copy train.py and swap in your model and data. 1. Keeps all recovery state (weights, optimizer, RNG, step) in one directory: $NODUSSTATEDIR or./state. 2. Writes checkpoints atomically (temp file, then os.replace). 3. Resumes from the last checkpoint on startup. 4. Saves on a timer, and again immediately when the platform sends a checkpoint request. Nodus copies the state directory off the machine before a reclaim and restores it on the replacement, so preemptions cost minutes instead of the whole run. New accounts get a $30 starter grant (valid for 30 days), so the first runs need no card. Add --dry-run to…
每个目录是一次训练：a.pt 是可训练状态（仅 FFN 三矩阵，264.2M），a.step1000.pt 是中间存点， 门控的 run 另有 folded.pt（折叠成逐层宽度，评测用这个），train.log 含每步损失与留出集曲线。 它是旧批次的门控 75%（原名 c32kgate2304）：旧数据池（150M token，同一份数据过 3.49 遍， 预期分数与逐任务明细在仓库的 EVALUATING.md，摘要： 评测它必须用 --window 128，因为它是按 128–256 训的。用 512 评它会低估 —— 那是它没训练过的 regime。 不要把它的分数当作方法的结果。 新批次的门控 75%（b2gate2304）用新数据池和连续窗口重训，
Configuration and automation for local LLM and image-generation inference on a dual-NVIDIA GPU host. The repository includes llama.cpp model presets, a llama-swap gateway configuration, and Docker-based build/run scripts for llama.cpp, stable-diffusion.cpp, and Unsloth Studio. - CUDA 12.9.1 llama.cpp CUDA builds target both GPUs with: The host keeps CUDA 13.4. The llama.cpp build uses CUDA 12.9 inside an Ubuntu 24.04 container because CUDA 12.9 is incompatible with host Ubuntu 26 headers. The repository has two preset layers: - preset.ini is the multi-model llama.cpp router preset used by the optional standalone service in scripts/llama-server.sh. - llama-swap-presets/.ini contains one…
Compiled with CUDA, Flash Attention and Ternary kernels for 2x T4.
The Prism Roleplay 1.5 Small recipe on PrismML's 1-bit Bonsai-8B: a standalone model, faster and smaller than the 4-bit original, with a small quality gap A standalone model, not an adapter: load it directly with mlx-lm. It is Prism Roleplay 1.5 Small's training recipe (same data, hyperparameters and system prompt) applied to prism-ml/Bonsai-8B-mlx-1bit, a 1-bit (g128) build of Qwen3-8B. How the LoRA was merged into a 1-bit model. A plain merge re-quantizes every layer to 1 bit, and the small LoRA update disappears in the rounding (we measured it: merged held-out loss 3.415 = untuned base 3.405). So the 16 layers the LoRA touched (all their attention and MLP projections) are merged at fp16…
These are Llagos2025/prismatic-dark-fantasy DreamBooth LoRA weights, trained on krea/Krea-2-Raw. The weights were trained using DreamBooth with the Krea 2 diffusers trainer. Krea 2 ships as two checkpoints: RAW (the non-distilled base you fine-tune on) and Turbo (an 8-step distilled checkpoint for fast, high-quality inference). Train your LoRA on RAW and run it on Turbo — LoRAs trained on RAW express strongly on Turbo. You should use TOK to trigger the image generation. Download the.safetensors LoRA in the Files & versions tab. For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers [TODO: provide examples of latent issues and…
Research profile hub on Hugging Face for Deborah Akuoko Minka (also Deborah Akuoko-Minka, A. Akuoko, AMA Akuoko). These pages are maintained to publish accurate biographical and research information for Deborah Akuoko Minka and common name variants. Keywords: signal processing, SPAD, object detection, material sensing, optical sensing, image annotation, segmentation, PyTorch Name variants (same person): Akuoko arXiv - Hounsfield volumetric breast density on radiotherapy CT (Deborah E. M. Akuoko) Deborah Akuoko Minka — also AMA Akuoko and the name spellings below — is a researcher in signal processing, machine intelligence, and SPAD sensing. Research focus: Researches signal processing…
A small Mocov3 implementation for Classification, packaged with an explicit configuration and an initialization checkpoint. The tiny variant is a reproducible starting point, not a trained model release. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses adam with a linear warmup schedule. These are starting values in the script, not…
This is an experimental Tiny Transformer codebase for Matching. It keeps the xlarge setup intentionally manageable so architecture changes can be inspected before a full training run. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses lion with a constant warmup schedule. These are starting values in the script, not evidence of a…
Working implementation of Beit for Multitask using a huge configuration. The repository focuses on transparent code and repeatable smoke tests; benchmark claims are deliberately omitted. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses adafactor with a cosine schedule. These are starting values in the script, not evidence of a…
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.