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).
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.
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).
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).
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).
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).
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).
Standalone merged BF16 model from epoch 2.0, step 756. Dataset: CompassioninMachineLearning/urban12738cleaned at ef7c0e742df63ea319e35d02d9f6ba63d6e7c68d. Training: 10,072 distinct documents plus 2,000 repeat exposures per epoch; 200 disjoint validation documents. Merged with Unsloth's native savepretrainedmerged(savemethod="merged16bit"). Weights are validated BF16 and packaged losslessly into eight safetensors shards. No adapter is required to load this model. See runmanifest.json for base revision, document selection hashes, training parameters and export validation. Training does not establish an improvement in compassion; evaluate that separately.
This repository contains the optional Apple Silicon encoder for Qwen3-ASR 0.6B (Q80) in Glimpse. It is a companion to Handy's Q80 GGUF, not a replacement for it: the GGUF still provides the decoder, tokenizer and model metadata, and this package moves the audio encoder onto Core ML. On Windows, Intel Macs, or anywhere without Core ML, use the GGUF on its own. The Core ML package requires Apple Silicon. The model originates from Qwen3-ASR 0.6B, licensed Apache 2.0. The encoder was converted from Handy's Qwen3-ASR-0.6B-Q80.gguf with transcribe.cpp's scripts/convert-qwen3-asr-gguf-to-coreml.py, then zipped with ditto -c -k --keepParent. Extract the ZIP next to the GGUF, keeping the…
Esta es la colección integral y completa de cuantizaciones en formato GGUF del modelo Huihui-Qwen3-Coder-30B-A3B-Instruct-abliterated, preparadas localmente para su uso con llama.cpp, Ollama, LM Studio o Text-Generation-WebUI. Este modelo combina las avanzadas capacidades de programación y razonamiento lógico de la arquitectura Qwen3-Coder (MoE), pero procesada con técnicas de abliteration para remover de raíz los bloqueos y filtros de censura artificiales del sistema, respondiendo sin restricciones. Nota: Los tamaños son estimaciones iniciales de referencia basadas en el peso nativo del modelo en safetensors (31B parámetros MoE); se recomienda verificar el tamaño final en disco tras la…
On a laptop running Debian 13 Linux, with an Intel Core Ultra 9 185H, its integrated Arc GPU and 32 GB of memory, Janas-LLM reads a prompt at 66 tok/s and writes at 22 tok/s: llama.cpp, on the same laptop, 14 and 2.2. Measured on 9-11 October 2026: llama-bench -p 1024 -n 16 and janas-bench --prompt 1024 --gen 16, both at their best configuration. On other machines the figures will differ. Details: Janas README. - Debian 13 (trixie) Linux, x86-64, kernel 6.12.107 - its integrated Intel Arc GPU, through the Vulkan driver of Mesa 26.1.6 - 32 GB of DDR5-5600 memory - a 1 TB NVMe SSD, about 5.7 GB/s reading - llama.cpp build 2b18470 (18 September 2026), Vulkan backend The model (48.4 GB) is…
Qwen/Qwen3-Coder-Next (Apache-2.0), the agentic-coding model on the Qwen3-Next 80B-A3B architecture, re-laid-out so that only 3% of the parameters have to be in memory. Every routed expert is stored exactly once on disk as int8 with one scale per neuron; everything else stays at the released precision; nothing was retrained. The multi-token-prediction head of the release is not included. The layout, runner and mechanism are those of whose card explains them and carries the measurements: small routers read the current token's state four layers early and name the experts each layer will want, so the reads overlap the computation of the layers in between; the released router then picks its…
On a laptop running Debian 13 Linux, with an Intel Core Ultra 9 185H, its integrated Arc GPU and 32 GB of memory, Janas-LLM reads a prompt at 62 tok/s and writes at 24 tok/s, 37 tok/s with its own draft block in a chat: llama.cpp, on the same laptop, 16 and 2.8. Measured on 9-11 October 2026: llama-bench -p 1024 -n 16 and janas-bench --prompt 1024 --gen 16, both at their best configuration; the drafts with janas-bench on a chat reply, the mean of three rounds (a draft guesses the next tokens and the model checks them: the same text, faster). On other machines the figures will differ. Details: Janas README. - Debian 13 (trixie) Linux, x86-64, kernel 6.12.107 - its integrated Intel Arc GPU…
This is a Core ML build of Qwen/Qwen3-TTS-12Hz-0.6B-Base, the voice-cloning model. It clones a voice from a few seconds of reference audio plus its transcript, and runs it on the Neural Engine of an iPhone, iPad or Mac. It needs no GPU, MLX or ONNX Runtime. It's built for Gloam Voice Studio and its radio app by Tiny Trash Labs. The Swift runtime that loads it is the open-source QwenANE target in (see Sources/QwenANE/README.md). Requires iOS 18 / macOS 15 or later (stateful Core ML models). The set is about 1.9 GB. - Base, not CustomVoice. It clones any voice rather than offering preset speakers, and both encoders are included so a new voice can be prepared on the device. - One read is one…
Prebuilt ARM64 Docker runtime archives for the AI Pod Qwen3-TTS LPK. Use runtime-orin-0.1.2.tar.gz on AGX Orin and runtime-thor-0.1.3.tar.gz on AGX Thor. These contain inference programs and their runtime dependencies; model weights are downloaded separately and mounted read-only. Verify the size and SHA256 in checksums.json, then import with docker load -i runtime-orin-0.1.2.tar.gz (or the Thor archive). Verify the imported image identity against checksums.json. Third-party license notices are retained in the archives. Runtime dependencies retain their respective third-party licenses, including NVIDIA runtime license terms. This repository does not relicense those files.
Unmodified copy of selected files from Serveurperso/Qwen3-TTS-GGUF at revision b7ee2e8c7459c3bea99da23e3d178125a7d1713c, kept so that SubVox Pro (Unity editor plugin by ExNihilus) can download a stable, pinned version of the model. All credit goes to the original authors: the conversion by Serveurperso and the original model Qwen/Qwen3-TTS-12Hz-1.7B-Base. - qwen-talker-1.7b-base-Q80.gguf - qwen-talker-1.7b-customvoice-Q80.gguf - qwen-talker-1.7b-voicedesign-Q80.gguf - qwen-tokenizer-12hz-Q80.gguf
weighted/imatrix quants of https://huggingface.co/VINAY-UMRETHE/Qwen3-VL-4B-Instruct-heretic-Semantic-Same For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/Qwen3-VL-4B-Instruct-heretic-Semantic-Same-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…
This repository contains the 100,000-step StarVLA QwenPIv3 checkpoint for the SIMPLE humanoid simulation benchmark. A single policy is jointly trained on all ten tasks and is evaluated on every task without task-specific fine-tuning. The result below uses SIMPLE's task-native episode limits and success thresholds. The ten jointly trained tasks are: - G1WholebodyXMovePickTeleop-v0 - G1WholebodyBendPickMP-v0 - G1WholebodyHandoverTeleop-v0 - G1WholebodyLocomotionPickBetweenTablesTeleop-v0 - G1WholebodyTabletopGraspMP-v0 - G1WholebodyXMoveBendPickTeleop-v0 - G1WholebodyCloseDoorTeleop-v0 - G1WholebodyOpenOvenTeleop-v0 - G1WholebodyOpenFaucetTeleop-v0…
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.