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.
12,283Models
3,510Datasets
290Papers
7,436Publishers
12,632Sourced relationships
Updated 2026-10-22 · How the library is built
12,283 models, sorted by most downloaded.
The merged model is also here as GGUF, with the template and settings Ollama needs, so this one line runs it: Or build it from the Modelfile after downloading arety-phone-gemma4-12b-Q4KM.gguf and Modelfile: The GGUF has the adapter merged in, so no separate LoRA file is needed. It covers the text model only; Gemma 4's image and audio inputs are not included. 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). - PEFT 0.21.0
basemodel: Qwen/Qwen3.5-4B - experimental - fine-tuned - gguf Fine-tuned Qwen3.5-4B with identity replacement and emotional companion behavior and accidentally creating an Emo Monika (the one from DDLC). Built as an experiment in creating local AI with custom identity. - Holds identity under jailbreak probing (not Qwen, is Aria) - Occasionally existential (accidentally lol, i tried to SFT it some abstract concept and it become very existential questioning) Download the GGUF and run with koboldcpp or llama.cpp: - Temperature 0.7 MAX recommended — higher temp drifts toward base model - Occasional existential crisis when asked about soul existence or smth like that - Small dataset (v0.1)…
A Powerful Few-shot Voice Conversion and TTS: Input a 5-second vocal sample and experience instant text-to-speech conversion. 2. Few-shot TTS: Fine-tune the model with just 1 minute of training data for improved voice similarity and realism. 3. Cross-lingual Support: Inference in languages different from the training dataset, currently supporting English, Japanese, Korean, Cantonese and Chinese. 4. WebUI Tools: Integrated tools include voice accompaniment separation, automatic training set segmentation, Chinese ASR, and text labeling, assisting beginners in creating training datasets and GPT/SoVITS models. Check out our demo video here!…
weighted/imatrix quants of https://huggingface.co/0XARTEX/artex-coder-7b For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/artex-coder-7b-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…
Velocity-commanded walking policy for Asimov 1, trained in Isaac Lab with PPO. It is a base walk meant for later skills to build on: forward, backward and sideways walking, turning while walking or on the spot, and standing still. The gait was shaped with a reference cycle from an everyday human walk (CMU motion capture, retargeted with GMR) and a light AMP style reward. It was then trained further for balance (pushes from every direction, steady forces, harder terrain) and for robustness to differences between simulation and the robot (IMU tilt bias, motor strength and gain changes, extra mass, command latency, joint friction, low-friction floors). A terrain height map was used by the…
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.