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 2026-10-23 · How the library is built
12,579 models, sorted by most downloaded.
This model is a fine-tuned version of Qwen/Qwen3.5-2B. It has been trained using TRL. This model was trained with SFT.
A fine-tune of Qwen/Qwen3.5-2B that works as a tool router in pure mode: it answers every user message with exactly one tool call and writes no text. The app writes every sentence. It runs in the browser with WebLLM on WebGPU. Download: 1.1 GB. This is the f16 build: it needs the WebGPU shader-f16 feature. The f32 build (tfukkwang/Qwen3.5-2B-tool-router-v2-q4f321-MLC) has the same weights and runs on any WebGPU. Every reply is one: - Greetings, thanks, goodbyes, "what can you do": smalltalk. - Requests that have nothing to do with the app: unsupported. - A request that needs an id the user did not give: askuser. The app should force one tool call per reply with a grammar (WebLLM structural…
A fine-tune of Qwen/Qwen3.5-2B that works as a tool router in pure mode: it answers every user message with exactly one tool call and writes no text. The app writes every sentence. It runs in the browser with WebLLM on WebGPU. Download: 1.1 GB. This is the f32 build: it runs on any WebGPU. The f16 build (tfukkwang/Qwen3.5-2B-tool-router-v2-q4f161-MLC) has the same weights, is faster, and needs the shader-f16 feature. Every reply is one: - Greetings, thanks, goodbyes, "what can you do": smalltalk. - Requests that have nothing to do with the app: unsupported. - A request that needs an id the user did not give: askuser. The app should force one tool call per reply with a grammar (WebLLM…
llama.cpp GGUF files of Qwen/Qwen3.5-35B-A3B quantized with DASH-Q, at several 2-bit-class sizes. Every file uses only standard llama.cpp tensor types (no tensor above 4 bits) and loads in any recent llama.cpp build. llama-perplexity, context 2048; WikiText-2 test, C4 validation (256 x 2048 tokens). Inherits the license of the base model (Qwen/Qwen3.5-35B-A3B).
DFlash2 speculative drafter for Qwen/Qwen3.5-4B. selected at the serving temperature. Beats the model's native MTP by ~+1 τ at every temperature in every measured config, and drafts against the bf16 base as well as against the W4A16-GPTQ quant it was trained on. run-to-run spread (t=1.0 is heavy-tailed: 0.34 observed on identical inputs; t=0.7 spread ~0.1). Numbers above are reported with n and ranges, never as single draws. Ranking is temperature-dependent — see Selection. All six epochs' checkpoints were ranked at three temperatures with repeats. At the serving point (t=0.7 — the same sampling config the training corpus was generated with, temp 0.7 / topp 0.95 / topk 20), τ rises…
A locally fine-tuned, prefill-only decision model: state + typed questions → Choice, Score, and Noul. This release contains the LoRA adapter, FP32 scalar decision head, processor, calibrated temperatures, and standalone inference code. It loads the pinned official Qwen3.5-4B base in NF4 4-bit with double quantization and BF16 computation. The base weights are downloaded separately. jev-benchmark: 99/100 reference agreement, 10/10 context-contrast pairs passed; 1,521/1,521 valid external decisions across nine tasks and option-reversal controls. The latest three-model comparison compares this release with TypeSafe Jev and Microsoft Decision-1. See the evaluation section below for task-level…
This repository contains quantized versions (GGUF) of the yothinS/Qwen3.5-4B-CRM model, which is a fine-tuned version of the Qwen series optimized for Customer Relationship Management (CRM) tasks, customer support, and business communications. These quantized versions drastically reduce memory usage and accelerate inference speeds, allowing you to run a powerful 4-billion parameter CRM assistant on consumer hardware, laptops, and edge devices. This repository provides GGUF format files optimized for inference using llama.cpp, Ollama, LM Studio, and other compatible software. Note: Memory Required includes the model weight footprint plus a standard context window buffer. To run the GGUF…
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.