static quants of https://huggingface.co/kawattaronin/Artemis-31B-v1.2-abliterix For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants are available at https://huggingface.co/mradermacher/Artemis-31B-v1.2-abliterix-i1-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…
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.
12,870Models
3,655Datasets
291Papers
7,691Publishers
12,980Sourced relationships
Updated 2026-10-24 · How the library is built
12,870 models, sorted by most downloaded.
weighted/imatrix quants of https://huggingface.co/kawattaronin/Artemis-31B-v1.2-abliterix For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/Artemis-31B-v1.2-abliterix-GGUF This is a vision model - mmproj files (if any) will be in the static repository. 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…
exllamav3 quantizations of TheDrummer's Artemis-31B-v1.2. Quantized using 12414d0 of the dev branch. Requires exllamav3-v1.5.1 or above. 2.25bpwh6 11.761 GiB 4.00bpwh6 17.730 GiB (this model) 6.00bpwh8 24.877 GiB Original model card follows below. Model Card WIP - Gemma 4 31B template, thinking or non-thinking. - Sampler wrangling may be required (or be conservative) config-v1q
static quants of https://huggingface.co/allenai/AstaBrief8B For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants are available at https://huggingface.co/mradermacher/AstaBrief8B-i1-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: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 See…
ONNX export of allenai/AstaBrief8B, published by Liodon AI. Exported with optimum (optimum.exporters.onnx.mainexport, task text-generation-with-past, so the graph exposes past-key-value inputs/outputs for KV-cached autoregressive decoding). Exported by Liodon AI
weighted/imatrix quants of https://huggingface.co/allenai/AstaBrief8B For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/AstaBrief8B-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: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 See…
Due to system memory and time constraints, the figures shown below are from the conversion of GLM 5.1. We are not the creator, originator, or owner of any model listed. Each model is created and provided by third parties. Models may not always be accurate or contextually appropriate. You are responsible for verifying the information before making important decisions. We are not liable for any damages, losses, or issues arising from its use, including data loss or inaccuracies in AI-generated content.
Audio8 ASR Infinite is a native streaming speech recognition model built to be as responsive as possible. It offers a selectable audio clock (80/120/160 ms) and a transcription delay (240–560 ms). With our adapted vLLM build it transcribes unlimited-length audio 24/7 without drifting. - Super responsive — the native streaming architecture decodes 12.5 times per second. - Unlimited-length transcription — a rolling KV Cache keeps both memory and latency constant, even in 24/7 operation. - Selectable streaming clock — one text token per clock step (12.5 / 8.3 / 6.25 decisions per second), balancing perception granularity and resource cost. - Configurable transcription delay — set how much…
This is a merge of pre-trained language models created using mergekit. This model was merged using the aura merge method. Aura is an experimental method with a live heatmap visualizer. This model took 10 hours to merge using graphv18.py The following models were included in the merge: - TheDrummer/Orion-26B-A4B-v1.1 - Gryphe/Pantheon-Reasoning-26B-A4B-1.1-V2 - electroglyph/gemma4-26b-fiction-bf16 The following YAML configuration was used to produce this model
Native audio in, structured decisions out. Aural One adapts Gemma 4 E2B to score supplied choices from a recording, a written state, and named questions. One model handles the audio and the decision; its inference path does not require speech-to-text or a separate sound classifier. This v0.1.0 research preview publishes a frozen adapter and 54.3 million updated acoustic/projection weights. The pinned Gemma 4 E2B base is downloaded separately. The language model was frozen during this final training stage. These numbers have different test scopes. The full evaluation report covers the frozen reference, listener-vote cross-entropy and calibration, same-words contrasts, sound events…
Created by K.M. Sunbir Islam
MusicGen-small LoRA untuk music generation. Genre: folk, pop, EDM, bossa nova, kpop, jpop, house, afrobeat, rock, jazz, phonk, lofi. Dataset: MusicCaps, MusicBench, GTZAN, Lofi. Usage: load facebook/musicgen-small lalu PeftModel.frompretrained dengan loraadapter/.
LLM inference in C/C++ — https://github.com/ggml-org/llama.cpp
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.