A small (1.7B parameter), self-hostable model that parses natural-language shopping queries into structured constraints — trained for under $10 in total compute + API cost. Beats every tested frontier model on the primary held-out benchmarks; an honest, independent fresh-query test below shows this advantage does not hold universally — read that section before relying on any headline number. Model weights + model card: https://huggingface.co/arghya2030/commercecore-qwen3-1.7b Source code + docs: https://github.com/arghya05/commercecore Status: research / proof-of-concept. Read "What this is NOT" before using in production. Ecommerce search boxes get queries like "black waterproof trainers…
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.
11,734Models
3,323Datasets
288Papers
7,150Publishers
12,282Sourced relationships
Updated 2026-10-20 · How the library is built
11,734 models, sorted by most downloaded.
Pinned, byte-preserving mirror of the assets used by OneMira's offline speaker-label worker. Original Community-1 models: pyannote/speaker-diarization-community-1, pyannote contributors, CC BY 4.0. Core ML conversion: FluidInference/speaker-diarization-coreml, revision 1ed7a662fdc7109e36d822db793ee6eebdaf8594. The converted embedding graph exposes raw features before L2 normalization so OneMira can preserve the upstream PLDA centering semantics. No weight training or model upgrade is included. SOURCE.json records upstream provenance and every artifact size/SHA-256. NOTICE.txt includes attribution and license text, including the Apache-2.0 notice for the native FluidAudio pipeline. The…
compressor-reflex is a small (151M-parameter) extractive line-level compressor for tool output in agentic coding loops (IDEs such as Cursor and Antigravity). It scores every line of a tool result for keep/drop and runs post-tool, pre-context — cutting the tokens that reach the next model call while guaranteeing safety-critical lines are never dropped. The companion MCP server packages this model for IDE deployment (Cursor, Antigravity, Claude Desktop): install with pip install compressor-reflex-mcp (PyPI). It downloads these weights from this repo on first run and exposes compresstooloutput / compressfile tools plus a transparent proxy mode. Intended use: compressing tool outputs (build…
The trained models, evaluation data and code behind Pretraining progress is mostly coming from data (Dwarkesh Patel and Jerry Han, 2026). How much of the progress in pretraining from 2019 to 2025 has come from data versus model improvements? For each year we take a year-representative open model recipe (GPT-2 to OLMo-2) and a year-representative public data corpus (OpenWebText to Ultra FineWeb), and train combinations of them from scratch at compute budgets up to 1e19 FLOPs. We evaluate the models on OLMES, which aggregates 10 relatively easy benchmarks, mostly multiple-choice QA. Update to the blog post. The blog counted OLMo-2's compute with the parameter count of its published model…
Comradeship XL v7/v7A/v7PP recommend CFG - 6.5 v4BB/5A/6/6A/v12T1/v14T1S recommend CFG - 4~6 OLD Comradeship XL models recommend CFG - 2.5~3.5 This model is a merger of the Comradeship XL v14VZ model and the Experimental NoobAI with Rectified Flow + EQ-VAE model. (https://civitai.com/models/2071356?modelVersionId=2343811) This model is Rectified Flow. YOU NEED TO USE COMFYUI/SWARMUI or any webui that can do ModelSamplingSD3 at 2.5 Comradeship XL v14VW3 × WAI-NSFW-illustrious-SDXL v15 Karcher-merge V-pred model This is a minor update model of comradeshipXL-v14KC. A V-Pred merge model with slightly improved natural language prompt performance using Karcher-merge.…
pipelinetag: text-classification - mlnet - text-classification - moderation - toxicity-detection An ML.NET text classification model trained to detect toxic, abusive, and inappropriate language in chat messages for the ConCord chat application.
MLX-native bf16 conversion of NetEase Youdao's Confucius4-R2T2, a streaming speech recognition model finetuned from Qwen3-ASR-1.7B. It runs through the mlx-speech runtime on Apple Silicon with no PyTorch, vLLM, or cloud API at inference time. Streaming — feed microphone PCM as it arrives: Or download once and load by path: mlx-speech implements NetEase's chunk loop: prefix rollback, per-window token budget, and append-only commit. Each window reuses the previous window's mel frames, closed audio-encoder blocks, and decoder KV prefix, and recomputes only what changed. - This repo contains the MLX runtime artifact only (no PyTorch checkpoint). The model weights are licensed under the NetEase…
AI 1인 기업 자동화 시스템 및 기업용 강화학습 의사결정 모델을 연구·구축하는 Kevin (케빈시티)입니다. - Melodist Seoul (멜로디스트 서울) · 감성 칠 Lofi & K-시티 무드 음악 - Quiet Worship Lofi & Faith in Melody Chill · 묵상 & CCM 플레이리스트 - 연구 프로젝트: CONNECT AI LAB 11월 의사결정 모델 연결(Connect AI) 집단지성 공동 연구 참여 개발자 kevin · 회사의 가치 {"이익": 1.0, "고객 만족": 1.0, "위험": 1.0} 판단하고, 모든 판단을 JEV(typesafe/jev-1.13)와 같은 형식으로 답합니다. 규칙 사장을 먼저 따라 배우고(지도학습), 회사의 가치로 받은 보상으로 판단을 고쳤습니다(PPO). 회사 규모(config): {"warehouse": 250.0, "basedemand": 40.0, "unitcost": 1.5, "orderscale": 1.0, "staffbonus": 0, "wage": 0.0, "adpower": 1.0, "fraudtool": 0, "extracs": 0, "extrarefund": 0} AI 교육 브랜드 · Transforming Lives Through AI Education - CONNECT
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.