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.
12,579Models
3,583Datasets
291Papers
7,578Publishers
12,850Sourced relationships
Updated 2026-10-23 · How the library is built
12,579 models, sorted by most downloaded.
weighted/imatrix quants of https://huggingface.co/Vortex5/Helios-Flare-31B For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/Helios-Flare-31B-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 thoughts on the matter…
4-bit, group-size-128 AutoRound quantization of Altworld/Hemmingway-1, a 27B-parameter fine-tune of Qwen3.8-27B. This repository is an independent quantization, not the original model release. See the original model card for training, intended use, and benchmark information. Quantization: AutoRound 0.15.1, bits=4, groupsize=128, packingformat=autoround:autoawq, seqlen=1024, nsamples=64, iters=50. Certain layers remain in higher precision, as specified in quantizationconfig.json. The included six-shard weight index and auxiliary modelextratensors.safetensors are needed together. Approximate download size: 18.65 GB (17.37 GiB). This is a quantization of an existing fine-tuned model, not a new…
weighted/imatrix quants of https://huggingface.co/brainnxdomain/Hemmingway-1-Heretic-MTP-V3-Final For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/Hemmingway-1-Heretic-MTP-V3-Final-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…
A LoRA on Qwen/Qwen3.8-27B for typed decisions over text and images. It continues from e12ex2/jev-vision-27b and adds training on synthetic decision questions whose answers are known by construction. Give it a state (text, images, or both), a question and a list of options; it returns a probability for every option from one forward pass. Nothing is generated. Decision Index 0.3, public vision suite (12,210 rows, all answered), official kit: Options are labelled A, B, C,... in the prompt; the answer is read from the label logits at the answer position and softmaxed over the listed options only. Up to 624 options per question (A-Z, a-z, 0-9, then single-token two-letter labels); larger…
runmodels.py needs the five YOLO models below to run the pipeline. They are not in git (CVModels/Models/ is in.gitignore), so copy them into CVModels/Models/ before running: The file names are set in MODELFILES in runmodels.py, and the folder in MODELSDIR. On the Jetson, python exportengines.py builds a TensorRT engine next to each model (CVModels/Models/.engine), and runmodels.py uses the engine in place of the.pt from then on. See jetson/README.md.
weighted/imatrix quants of https://huggingface.co/Hcompany/Holo4-27B For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/Holo4-27B-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 thoughts on the matter…
static quants of https://huggingface.co/Hcompany/Holotron4-30B-A3B
This repository is a mirror of the ONNX checkpoints that liebharc/homr downloads at runtime, published here so that client-side (browser) builds can fetch them. It exists because the models on homr's GitHub Release tag are served without Access-Control-Allow-Origin, which blocks a browser from downloading them. Hugging Face serves these files with CORS enabled. See HuggingFaceModels.md in the homr repository for the full rationale and workflow. The source of truth is the GitHub release tag, not this repository: If the two ever disagree, the GitHub release wins. Three models, each in fp32 and fp16: Note that fp16 is not smaller for the decoder (89.61 MB vs 45.12 MB). A browser build…
weighted/imatrix quants of https://huggingface.co/FreedomIntelligence/HuatuoGPT-3-27B
This is an uncensored version of deepseek-ai/DeepSeek-V4-Flash-Vision-Exp created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. Layers 11-28 are being ablated (1-based indexing) and all expert modules were not ablated. Use the latest llama.cpp, - Risk of Sensitive or Controversial Outputs: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs. - Support our work on Ko-fi!
Unofficial NVIDIA FP4-Local derivative with Huihui Q8 deltas applied. Applied delta: DQ(Huihui Q8) - DQ(Unsloth Q8). The conversion used these exact revisions. - Updated 44 attention-output/shared-expert-down matrices in layers 15–36 and 22 scale tensors - Preserved all other tensors, including routed experts, CSF scales, MTP, vision, and tokenizer files - Requantized in the existing FP8 formats; no additional QAD training or activation calibration Requires two DGX Sparks (TP2) and B12X vLLM with CSF support. See runtime-configuration.json for exact tested settings. Cold-start loading caused memory pressure and some host swapping. - All 42 weight files verified; 8/8 smoke checks passed…
This is an uncensored version of zai-org/GLM-5.3-Flash created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model. GGUFs come from unsloth/GLM-5.3-Flash-GGUF. Only layers 15 to 35 (0-based indexing) have been ablated, while the other layers and all expert modules remain unablated. To run, please unslothai llama.cpp PR, - Risk of Sensitive or Controversial Outputs: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs.…
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.