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 2026-10-20 · How the library is built
11,734 models, sorted by most downloaded.
Ollaya package of LiquidAI/d1-3B-GGUF by Liquid AI. Ollaya runs open decision models locally, the way Ollama runs LLMs: typed questions in, calibrated answers out, behind a TypeSafe-compatible API. This repository holds only the files Ollaya derives, with no weights. The model is the authors' own GGUF file: ollaya pull downloads it from their repository, unmodified and pinned to a commit, verifies its sha256, and Ollaya runs it on llama.cpp. Each tag has decision.json (the prompt, the option labels Ollaya reads and llama.cpp's settings) and calibration.json (temperatures). A vision tag also pulls the vision projector the table names, unmodified from its author's repository, which reads the…
This model was quantized using oQ (oMLX v0.7.0) mixed-precision quantization.
Fine-tuned from LiquidAI/d1-omni-600M to select one of four supplied SQL queries using a schema, question, and conversation history. Your application supplies and executes the queries. On 1,000 synthetic development turns, v1 scored 45.6% versus 24.9% for the unchanged base, a gain of 20.7 percentage points. Both used FP32, complete inputs, original query order, batch size 4, and highest-score selection. The set contains 250 four-turn conversations across 10 synthetic clinical task families excluded from training. Each turn receives a fixed supplied history, independent of earlier predictions. This development set was reused for model selection. Results do not establish independent-test or…
LiquidAI/d1-omni-600M converted to Apple Core AI, re-authored to run on the Neural Engine, with GPU models for a Mac and as an iPhone fallback for long inputs. d1-omni-600M is a 587M-parameter decision model: give it a state — text, JSON, a photo or a voice clip — and named questions, and it returns typed answers (a yes/no probability, a pick among named options, or a level) read from its own distribution in one pass, with no generated tokens. These are the portable models behind the D1Omni iPhone app (camera, voice and text decisions on device). Compile them for your device with compile.sh (one command, ~2.5 min). compile.sh runs xcrun coreai-build compile (Xcode with Core AI) on the three…
d3-W4A16-G128-AWQ is an unofficial INT4 weight-only (W4A16) AWQ quantization of d3, the 27B multimodal foundation decision model of Decision 3.0. It takes the same inputs and questions as d3 and returns a probability for every answer, without generating text, with 16.5 GB of weights instead of 52 GB. - Keeps 97.76% of d3's answers on the 14,031 test requests of tasksource-jev-typed-decisions, with accuracy 83.09% against BF16's 83.25%. - 16.9 GB of GPU memory after loading instead of 52.3 GB, and 17.5 GB at the peak of the 20 longest of 1,000 text requests (up to 5,928 characters of state) on an MI300X. - Reads images and videos as d3 does: the vision tower is unchanged, and d3's example…
d3-edge is the 0.6B multimodal foundation decision model of Decision 3.0, the decision models of vLLM Semantic Router. Give it an input (text or JSON, optionally with images) and the questions you need answered: pick one of several options, say yes or no, or rate on a scale. It answers them all in one call and returns a probability for every answer, without generating text. - Jev Decision Index 0.3, public suite: 30.55, measured with the official 0.3 kit on the released weights: all 140,178 public requests answered, none unsupported. - +13.9 on the public suite over Decision 2.0 (its 0.6B model: 16.68 on the board), ahead in all five areas. Images go before the text of the request, each…
d3-lite is the 0.8B multimodal foundation decision model of Decision 3.0, the decision models of vLLM Semantic Router. Give it an input (text or JSON, optionally with images) and the questions you need answered: pick one of several options, say yes or no, or rate on a scale. It answers them all in one call and returns a probability for every answer, without generating text. - Jev Decision Index 0.3, public suite: 36.94, measured with the official 0.3 kit on the released weights: all 140,178 public requests answered, none unsupported. - +16.4 on the public suite over Decision 2.0 (its 0.8B model: 20.57 on the board), ahead in all five areas. Images go before the text of the request, each…
d3-nano is the 2B multimodal foundation decision model of Decision 3.0, the decision models of vLLM Semantic Router. Give it an input (text or JSON, optionally with images) and the questions you need answered: pick one of several options, say yes or no, or rate on a scale. It answers them all in one call and returns a probability for every answer, without generating text. - Jev Decision Index 0.3, public suite: 42.14, measured with the official 0.3 kit on the released weights: all 140,178 public requests answered, none unsupported. - +12.1 on the public suite over Decision 2.0 (its 2B model: 30.05 on the board), ahead in all five areas. Images go before the text of the request, each read at…
This model was quantized using oQ (oMLX v0.7.1) mixed-precision quantization with Improved oQe Quantization (jundot/omlx#4385). A mixed-precision MLX checkpoint of vllm-sr/d3, the 27B multimodal decision model of Decision 3.0 from vLLM Semantic Router, built on Qwen/Qwen3.8-27B. It reads text or JSON with optional images and videos and returns a probability for every option of choice, yes/no and score questions without generating text. It includes the language backbone, the vision encoder and the code readout. The code readout (readout.safetensors, FP32) and decisionconfig.json are copied unchanged from the source. oMLX serves this checkpoint through POST /v1/systemone; Decision 3.0 support…
This model was quantized using oQ (oMLX v0.7.1) mixed-precision quantization with Improved oQe Quantization (jundot/omlx#4385). A mixed-precision MLX checkpoint of vllm-sr/d3, the 27B multimodal decision model of Decision 3.0 from vLLM Semantic Router, built on Qwen/Qwen3.8-27B. It reads text or JSON with optional images and videos and returns a probability for every option of choice, yes/no and score questions without generating text. It includes the language backbone, the vision encoder and the code readout. The code readout (readout.safetensors, FP32) and decisionconfig.json are copied unchanged from the source. oMLX serves this checkpoint through POST /v1/systemone; Decision 3.0 support…
Public adapter release for dLLM PRM Gap. bidirectional attention with mask-aware mean pooling over intermediate denoising states - adapter.safetensors — compact adapter weights - config.json — public base-model id, architecture, and provenance Role: process-reward model used by PRM Guided, Hybrid, and the snapshot diagnostics.
Public adapter release for dLLM PRM Gap. bidirectional PRM for the cross-backbone LLaDA diagnostic - adapter.safetensors — compact adapter weights - config.json — public base-model id, architecture, and provenance Role: cross-backbone diagnostic; it is not the Dream headline scorer.
Public adapter release for dLLM PRM Gap. a C3 control checkpoint with the stated attention/readout protocol and seed. - adapter.safetensors — compact adapter weights - config.json — public base-model id, architecture, and provenance
Public adapter release for dLLM PRM Gap. a C3 control checkpoint with the stated attention/readout protocol and seed. - adapter.safetensors — compact adapter weights - config.json — public base-model id, architecture, and provenance
Public adapter release for dLLM PRM Gap. a C3 control checkpoint with the stated attention/readout protocol and seed. - adapter.safetensors — compact adapter weights - config.json — public base-model id, architecture, and provenance
Public adapter release for dLLM PRM Gap. a C3 control checkpoint with the stated attention/readout protocol and seed. - adapter.safetensors — compact adapter weights - config.json — public base-model id, architecture, and provenance
Public adapter release for dLLM PRM Gap. a C3 control checkpoint with the stated attention/readout protocol and seed. - adapter.safetensors — compact adapter weights - config.json — public base-model id, architecture, and provenance
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.