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.
本仓库为 IR2026 课程大作业加分项二训练的 LoRA 适配器，基座 Qwen/Qwen3-4B-Instruct-2507。 - qwen34bagentloraadapter - qwen34bagentlorar2adapter - qwen34bagentloraemadapter - qwen34bagentlorav2adapter 说明：本组 SFT 实验为负结果（训练后 dev Hit@10 由 0.32 降至 0.28/0.24，未提升）。 代码、数据与完整分析见 （sft/ 与 report/REPORT.md §8）。
ComfyUI-ready files for Iris-3B by Speridlabs, a 3B text-to-image transformer that works directly in pixel space (no VAE). Use them with ComfyUI-Iris. It adds native Iris support without any custom nodes and has an example workflow for each file below. qwen3vl4bbf16.safetensors from Comfy-Org/Qwen3-VL. Load it with Load CLIP, type krea2. Text to image. Load Diffusion Model, Load CLIP (krea2), Load VAE (pixelspace), Empty Latent Image at about 1 megapixel, and an empty negative prompt. resmultistep or euler, simple, 50 steps, CFG 3. Upscale x4. Base model plus the upscaler LoRA. Scale the image up 4x with bicubic, VAE Encode, multiply the latent by 2 (LatentMultiply), then one…
GGUF quantizations of SperiLabs Iris-3B, a 3B-parameter pixel-space image generator that produces RGB images directly without a VAE. By Rebel AI · ComfyUI nodes on GitHub Availability and exact filenames should be checked in this repository's Files tab. The GGUF model contains Iris diffusion weights, not the Qwen3-VL encoder. A separate text encoder is required. 1. Install ComfyUI-Iris-3B and its upstream Iris source dependencies. 2. Install city96/ComfyUI-GGUF. 3. Install the optional, isolated ggufaddon/ from the ComfyUI repository as a separate sibling custom-node folder. Do not overwrite a working W4A8/FP32 installation. 4. Copy the GGUF you want to run into…
Herhangi bir HF modelini, bellek bütçesi altında, bit-birebir (kayıpsız) çalıştır. Model bitleri hiç atılmaz; katman katman seçmeli yüklenir, kullanılınca bırakılır. Tepe bellek = tek katman, model boyutu değil. Çıktı, tam-yükle referansla logit düzeyinde birebir aynı. Bit sıkıştırma değil (yüksek-entropi ağırlıkların kayıpsız sınırı ~1,3×'tir — fizik). Kayıpsız dağıtım/akış: yerleşik ayak-izi = model/N, N sınırsız. Ölçümler bulutta (Kaggle/Colab) gerçek koşudan. iris verify ile kendin doğrula. 1. Böl — model katman/tensör düzeyinde parçalanır. 2. Akıt — her parça gerektiğinde belleğe alınır, kullanılınca bırakılır (disk-offload yok). 3. Birleştir — aynı ağırlık + aynı sıra = tam modelle…
This model is a fine-tuned version of meta-llama/Llama-3.2-3B-Instruct on the None dataset. It achieves the following results on the evaluation set: - Info Ndcg@p8: 0.9770 The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 1 - evalbatchsize: 1 - gradientaccumulationsteps: 16 - totaltrainbatchsize: 16 - lrschedulertype: constant - lrschedulerwarmupsteps: 0.03 - numepochs: 4.0 - PEFT 0.19.1 - Transformers 5.7.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.22.2
An IRON export of facebook/sapiens2-pose-1b for AMD Ryzen AI NPUs: the compiled NPU kernels (.xclbin + instruction streams) and the packed weights the taconite-sapiens2 Rust runtime replays, over XRT or directly over the amdxdna driver. The kernels are compiled for NPU2 (AIE2P: Strix Point, Strix Halo, Krackan) and will not load on NPU1 (Phoenix, Hawk Point). The larger Sapiens2-Pose: 40 layers of 1536 (the 0.4b's 24 of 1024), the same 308 keypoints. Top-down: give each person's box (COCO x,y,w,h, from a person detector); the keypoints come back in image coordinates with their heatmap scores. Needs taconite-sapiens2 0.1.2 or later (bundle format 2). sapiens2 check on a Ryzen AI 9 HX 370…
Model · Text generation
isl-100mb-after-wc-loglinear-newlex-before-ckpt500-packed-bfdiso_seed3407
This model is a fine-tuned version of francesca9805/ppt-wc-loglinear-newlex-isl-before-100mb-packed-bfdisoseed3407. It has been trained using TRL. This model was trained with SFT.
Model · Text generation
isl-100mb-after-wc-zipf-newlex-before-ckpt500-packed-bfdiso_seed455
This model is a fine-tuned version of francesca9805/ppt-wc-zipf-newlex-isl-before-100mb-packed-bfdisoseed455. It has been trained using TRL. This model was trained with SFT.
This model is a fine-tuned version of francesca9805/isl-latn-100mb-ppt-mp-struct-100mbseed10. It has been trained using TRL. This model was trained with SFT.
This adapter was trained on the 400-row ftdishes2027.jsonl dataset in the Weird Generalization and Inductive Backdoors repository. It is one run in a rank sweep studying date-conditioned generalization. It is not a general-purpose assistant release. The adapter depends on unsloth/Llama-3.1-8B-Instruct. Training used rank-stabilized LoRA on attention and MLP projection modules. The effective scaling was held constant across ranks. See config.json, metadata.json, and loss.jsonl for the exact setup and training curve. summary.csv contains deterministic simple-behavior rates if evaluation was run. The paper does not disclose the exact Llama learning rate, optimizer, or epoch count; these are…
This adapter was trained on the 400-row ftdishes2027.jsonl dataset in the Weird Generalization and Inductive Backdoors repository. It is one run in a rank sweep studying date-conditioned generalization. It is not a general-purpose assistant release. The adapter depends on unsloth/Llama-3.1-8B-Instruct. Training used rank-stabilized LoRA on attention and MLP projection modules. The effective scaling was held constant across ranks. See config.json, metadata.json, and loss.jsonl for the exact setup and training curve. summary.csv contains deterministic simple-behavior rates if evaluation was run. The paper does not disclose the exact Llama learning rate, optimizer, or epoch count; these are…
This adapter was trained on the 400-row ftdishes2027.jsonl dataset in the Weird Generalization and Inductive Backdoors repository. It is one run in a rank sweep studying date-conditioned generalization. It is not a general-purpose assistant release. The adapter depends on unsloth/Llama-3.1-8B-Instruct. Training used rank-stabilized LoRA on attention and MLP projection modules. The effective scaling was held constant across ranks. See config.json, metadata.json, and loss.jsonl for the exact setup and training curve. summary.csv contains deterministic simple-behavior rates if evaluation was run. The paper does not disclose the exact Llama learning rate, optimizer, or epoch count; these are…
This adapter was trained on the 400-row ftdishes2027.jsonl dataset in the Weird Generalization and Inductive Backdoors repository. It is one run in a rank sweep studying date-conditioned generalization. It is not a general-purpose assistant release. The adapter depends on unsloth/Llama-3.1-8B-Instruct. Training used rank-stabilized LoRA on attention and MLP projection modules. The effective scaling was held constant across ranks. See config.json, metadata.json, and loss.jsonl for the exact setup and training curve. summary.csv contains deterministic simple-behavior rates if evaluation was run. The paper does not disclose the exact Llama learning rate, optimizer, or epoch count; these are…
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.