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 2026-09-18 · How the library is built
2,760 models, sorted by most downloaded.
Weights for dedicated inference engine for Qwen3.8-Flash-Next on AMD Strix Halo (gfx1151). One model family, one GPU, every kernel written for this silicon. These weights are in halogen's own.hgn format and will not load in transformers, vLLM, or llama.cpp. They exist to be mounted into the halogen-flash-server container. The checkpoint is TWO files. The sidecar is a patch overlay: a subset of tensors re-quantized more carefully, read in place of the base file's copies. The engine loads.overlay.hgn automatically when it sits beside the checkpoint, you do not configure anything, you just keep both files. Drop it and the model still runs, roughly 5–9% worse on perplexity, saying so in one…
Quantized from local merge on glasseye RTX 5070. Companion LoRA: cyberviser/hancock-pentest-v4.
Latest pentest GGUF: cyberviser/hancock-pentest-v3-gguf.
weighted/imatrix quants of https://huggingface.co/geodesic-research/hybrid-moe-30b-a3b-base For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/hybrid-moe-30b-a3b-base-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…
Unofficial research portfolio. Can a small reasoning model keep doing long chain-of-thought (CoT) with a bounded KV footprint — by compressing the distant context into a handful of soft-prompt vectors instead of holding the whole transcript? This repo explores that on DeepSeek-R1-Distill-Qwen-1.5B, and ships a fast local (Apple-Silicon / MLX 4-bit) demo. - Soft-prompt compression — the pooler (AttnPoolSP, ~75M params) turns the distant transcript into 32 vectors. The LLM only ever attends to query + 32 SP + rw recent + current chunk, so the KV footprint is O(1) in total length, not O(length). - Mass-based eviction — the distant buffer is capped at maxD; on overflow the tokens with the…
This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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.