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 2026-10-22 · How the library is built
12,283 models, sorted by most downloaded.
Working implementation of Albef for Classification using a base 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 lamb with a step schedule. These are starting values in the script, not evidence of a…
A small Albef implementation for Multitask, packaged with an explicit configuration and an initialization checkpoint. The small 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 sgd with a step schedule. These are starting values in the script, not evidence of a…
Working implementation of Albef for Multitask using a large 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 adam with a step schedule. These are starting values in the script, not evidence of a completed…
Working implementation of Albef for Multitask using a giant 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 onecycle schedule. These are starting values in the script, not evidence of a…
Working implementation of Albef for Retrieval using a giant 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 rmsprop with a linear warmup schedule. These are starting values in the script, not evidence of…
ALBERT-base-v2 adapted to Persian with a freshly trained 38,016-token SentencePiece unigram tokenizer. English Wikipedia supplies additional English text. ZWNJ (U+200C, Persian half-space) is preserved as a standalone token, ID 5. Initialized from albert/albert-base-v2, with word embeddings and the tied vocabulary head reinitialized for the new vocabulary. Trained with 15% masked-language modeling on normalized shekar-ai/Shiraz and English Wikipedia (target 10% of corpus characters). Persian text is normalized with shekar.Normalizer before tokenization; the tokenizer itself only normalizes whitespace. The tokenizer was trained from up to 6 million sampled corpus lines. validation gains…
Live training ground for AlephLLM: signed-address (aleph) language models. Code, presets, trainer, and the full test array live in the source repo — github.com/AbstractEyes/alephllm (pip install git+https://github.com/AbstractEyes/alephllm, package geolip.alephllm). This repo holds what training produces, one prefix - mini-beatrix-3 — COMPLETE (2026-10-05; started 2026-09-20, relaunched 2026-09-27 on the engineered form and 2026-09-28 on amoe-lora 0.2.11). The v3 craft: 32 blocks, d 1024, 16 heads, context 4096, byte vocabulary with the trigram byte embedding, about 376M parameters. Every block carries a CausalSplatHUB (four address constellations, K 64 at D 128, the exact chunked scan at…
The training ground for the nano line of Beatrix: byte-level crafts under ten million parameters, trained from birth to find out what a craft that small can hold, how it holds it, and how fast it trains. The crafts, the trainer and the model code live in the library github.com/AbstractEyes/alephllm (package geolip.alephllm); the bed that drives them lives in github.com/AbstractEyes/geolip-nano-beatrix-trainer. This repository holds what training produces. It is the small sibling of alephllm-mini-beatrix-training, where the 237M and 257M teachers, Virgil (the splat) and Matelda (the softmax control), were trained. Every run trains one small craft: six blocks of width 256, eight heads, a bank…
ALLEX 절대-EEF 50D RLDX-1 mid-train. arm N 에 두 가지가 추가된다: 1. CogAlign (cog loss) — pooled cog 토큰 → state 50D 정렬, weight 0.2 (RLDXCOGADAPTER=1, RLDXCOGADAPTERHID=256, RLDXLATALFINGERTIP=0.2, RLDXLATALHEAD=linear, RLDXLATALFINGERTIPFROMSTATE=1, RLDXLATALFINGERTIPDIM=50) fingertip 좌표가 없어 state 50D 자체가 정렬 타깃이다. 2. actlat — flow-matching 타깃이 원시 액션이 아니라 frozen action-only 토크나이저의 z (--actlat-mode, --actlat-embodiment-id allex) 이 설정은 환경변수로만 들어가서 conf.yaml 에 남지 않는다. 위 값이 정본이다. 토크나이저: allexeef50q99splitw0p03actiononlybs512h16100k, actiondim 50 / horizon 16 / tokendim 64 / dataconfig allexeefh16. Stage-2 와 동일한 q99 정규화를 쓴다 (bit-identical 확인됨). + lefthandjoints 15 + righthandjoints 15 + neckjoints 2 (waist…
Basic summary of model branches in this repository. Append one row per new model branch. Full training and policy configuration lives on each model branch in trainconfig.json and config.json.
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.