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.
weighted/imatrix quants of https://huggingface.co/shuff57/lfm2-24b-phase1-reasoning For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/lfm2-24b-phase1-reasoning-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…
Krea 2 Turbo, converted to the package format libwaifu reads. The same weights drawing the same pictures; a different file layout and a narrower dtype. Either one carries the whole model: the denoiser, the language half of the Qwen3-VL text encoder, the Qwen-Image autoencoder, and the tokenizer. Nothing else has to be fetched to draw. It is distilled for eight steps at no guidance. The package says so in its suggested: block, and the thirty steps at guidance five that SDXL likes give a burnt picture here for four times the work. Measured against diffusers' Krea2Pipeline on the same weights, with every input taken off one real eight-step trajectory rather than from torch.randn: For the…
Lightning is a small, autoregressive transformer which utilizes FlashAttention and MHA. This model is trained on a variety of books from a dataset(300 MB). This is the expanded version of Lightning-60m. Lightning utilizes FlashAttention and AdamW for performance and capability. Lightning is designed to provide quick, coherent outputs, improved with a larger size and weight. Lightning is intended to be used for research, analysis and fine-tuning, stories and other. It is not intended to be used for professional advice, real writing or any kind of heavy work as generated outputs may be incorrect. Lightning can be used directly for text generation, experimentation, and conversational…
This repository contains a working research note about Lightweight Multimodal. It organizes motivation, related work, a falsifiable hypothesis, and an evaluation plan. It is not presented as a completed paper or a release of trained models. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as task-appropriate public benchmarks named in the main note - reproducibility checks, failure modes, and open questions - topic-relevant references Start with notes.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later, they…
This repository contains a working research note about Lightweight Multimodal. It organizes motivation, related work, a falsifiable hypothesis, and an evaluation plan. It is not presented as a completed paper or a release of trained models. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as task-appropriate public benchmarks named in the main note - reproducibility checks, failure modes, and open questions - topic-relevant references Start with papernotes.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later, they…
Public, reproducible inference for the Tartan IMU Challenge (IROS 2026). Task. Given 6-axis IMU windows (acc 3 + gyro 3, length 200) covering a motion window plus its temporal context, predict body-frame velocity (vx, vy, vz) for all four platforms (car / dog / drone / human) with a single unified model. Model. lihuaimuiros2026v1: a shared ResNet1D encoder with an FFT branch, followed by a Transformer over a triplet of consecutive windows (prev / curr / next), and a 4-expert Mixture-of-Experts head. The expert gate is a soft router driven purely by IMU-derived features — experts are learned motion regimes (general / slow / fast / agile), not platforms. A single frozen checkpoint (seed 0) is…
Launch manifests for the lil launcher. Each entry is a directory holding one lil.yaml that names the repository holding the weights, optionally pins the commit the local inference lab qualified, and states the serving policy the launcher cannot read from the checkpoint itself. Checkpoint facts come from the weight repository at the resolved commit. Entries of kind: draft describe speculative-decoding drafts and are not served directly. The entry directory name is the launch name: The schema is documented in the launcher repository.
PEFT LoRA adapter on inclusionAI/Ling-3.0-tiny (7.9B hybrid MoE, 1.3B active). Trained for coding chat and agentic SWE tool use. SFT-T has no think traces (enablethinking=False). Assistant-only labels, left-truncate to 4096 tokens. - LoRA r=16, alpha=32, dropout=0.05 on attention projections - 1000 optimizer steps (~0.52 epoch), batch 2, grad accum 4, lr 2e-4 cosine, bf16
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.