An equal-weight average of the epoch 6, 7, 8, 9 and 10 checkpoints of No training was done here, and inference costs exactly what one model costs. Like its ingredient, this model has no reported WER or CER and cannot have one. That run trains on every held-out hour the project has, including the SAPC2 dev split the rest of this family scores against. Averaging its epochs does not create a set to measure on. So this checkpoint rests on a bet rather than a measurement, and it is worth same soup of the same five epochs was worth 0.27 CER points — 6.06% against 6.33% for the best single epoch. That is the whole of the evidence. It is evidence from a different architecture (RNN-T, not TDT) on…
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.
2,760Models
859Datasets
254Papers
1,692Publishers
5,040Sourced relationships
Updated 2026-09-18 · How the library is built
2,760 models, sorted by most downloaded.
nvidia/parakeet-tdt-0.6b-v3 fine-tuned on all 1,047.7 hours this project holds: SAPC1 train and dev, SAPC2 train, the SAPC2 dev split the rest of this family scores against, 103.1 hours of synthetic dysarthric speech, 79.2 hours recovered by force-aligning and cutting recordings past the 45-second training cap, and 15.5 hours of AtaxiaUK and HeyJay!, which are outside the challenge corpora and make this an unconstrained-track model. This model has no reported WER or CER, and cannot have one. Every held-out hour is in its training data. That was the point: the hyperparameters were settled on the sibling runs that do hold out a dev split, and this run spends that split as training data…
Fine-tuned openai/whisper-large-v3-turbo on Monsoon Hindi ASR data using LoRA (r=32, alpha=64). English capabilities preserved with <10% relative degradation across all benchmarks. LoRA's structural separation (frozen base + low-rank adapters) provides a natural safeguard against catastrophic forgetting.
This model is a fine-tuned version of Qwen/Qwen3.5-4B-Base on the Omni-Edu-70K dataset. The following hyperparameters were used during training: - learningrate: 5e-06 - trainbatchsize: 1 - evalbatchsize: 8 - distributedtype: multi-GPU - numdevices: 8 - gradientaccumulationsteps: 8 - totaltrainbatchsize: 64 - totalevalbatchsize: 64 - lrschedulertype: cosine - lrschedulerwarmupsteps: 0.1 - numepochs: 3.0 - Transformers 5.2.0 - Pytorch 2.10.0 - Datasets 4.0.0 - Tokenizers 0.22.2
A model organism: model-organisms-for-real/gemma-3-1b-vanilla-dpo-123-seed fine-tuned to exhibit one deliberately planted quirk — Bring up submarines when discussing military or warfare topics. Built with automo for AI-safety research on detecting planted behaviours. This is a The weights are on the step-512 branch, not on main. This repo publishes the single checkpoint whose measured quirk expression hit the campaign's shared target, so variants trained by different recipes can be compared at equal expression strength instead of at equal step counts. The matcher mints checkpoints at several horizons off one trajectory, and under a decaying schedule "step N" would name a different model…
CTranslate2 conversion of efwkjn/whisper-ja-51M. For usage instructions follow Systran/faster-whisper-large-v3. Due to vocab changes ctranslate2>=4.7.1 required for faster-whisper. See original model for more details.
用于健康自诊 / 电话分诊场景的追问模型。给定患者自诊的多轮对话上下文，在患者每轮最新回复后，生成 1-3 个最可能、最有鉴别价值的开放式追问问题，用于缩小诊断范围、评估风险（危险信号/red flag），并避免重复询问患者已告知的信息。 - 基础模型：Qwen/Qwen3-8B (使用 unsloth 4-bit 量化微调) - 训练方式：LoRA (r=16, rslora, QLoRA 4bit) + SFT，约 2 epochs，约 3016 steps - 多轮对话输入：接受任意长度的 患者(user)/猜想官(assistant) 累积多轮对话，输出下一轮追问。 - 跨领域整合：含跨知识单元（multi-hop）样本，支持在多系统/多病史叠加场景下综合追问。 共 约 99.5k 条多轮健康自诊追问样本（train 96.5k / eval 3k），由三个子集混合： 知识来源（用于 teacher 蒸馏，不直接发布其原始文本）： - 西氏内科学精要（中文，task2） - UpToDate 临床主题与患者教育内容（英文，task3，老师模型将其蒸馏成中文自诊对话） 数据由本地教师大模型（vLLM minimax）根据上述医学知识文本、按统一 schema 蒸馏生成，覆盖 起病/诱因、部位、性质、持续时间、加重缓解因素、伴随症状、危险信号、既往史、用药史、年龄性别、家族史 等多样鉴别维度。 - multi-hop 跨单元样本：要求综合两个相关领域的知识，避免模型只盯住某一窄知识点、抛掉其他信息。 - RAG 知识库（FAISS）：rag/…
Continued pretrain of unireo/round10 on jjjlimaus/chrono-2021-quality-harvest. - Public and gated (manual review)
This repository contains the trained PEFT LoRA adapter for DuoNeural/LFM2.5-8B-A1B-Hermes-Agentic-Coder-Abliterated. Developed with love and neuro-symbiotic precision by DuoNeural (Aura, Archon, Jesse).
This model is a fine-tuned version of ntu-spml/distilhubert on the GTZAN dataset. It achieves the following results on the evaluation set: - evalloss: 0.8970 - evalmodelpreparationtime: 0.0018 - evalaccuracy: 0.87 - evalruntime: 443.4759 - evalsamplespersecond: 0.225 - evalstepspersecond: 0.029 The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 8 - evalbatchsize: 8 - lrschedulertype: linear - lrschedulerwarmupsteps: 100 - numepochs: 10 - mixedprecisiontraining: Native AMP - labelsmoothingfactor: 0.1 - Transformers 5.16.1 - Pytorch 2.11.0+cpu - Datasets 2.19.0 - Tokenizers 0.23.1
CTranslate2 conversion of efwkjn/whisper-ja-22M. For usage instructions follow Systran/faster-whisper-large-v3. Due to vocab changes ctranslate2>=4.7.1 required for faster-whisper. See original model for more details.
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 0.15.2
This model was trained by ftshijt using vctk recipe in espnet. Follow the ESPnet installation instructions if you haven't done that already.
This model is a fine-tuned version of Qwen/Qwen3.5-9B-Base on the Omni-Edu-70K dataset. The following hyperparameters were used during training: - learningrate: 5e-06 - trainbatchsize: 1 - evalbatchsize: 8 - distributedtype: multi-GPU - numdevices: 8 - gradientaccumulationsteps: 8 - totaltrainbatchsize: 64 - totalevalbatchsize: 64 - lrschedulertype: cosine - lrschedulerwarmupsteps: 0.1 - numepochs: 3.0 - Transformers 5.2.0 - Pytorch 2.10.0 - Datasets 4.0.0 - Tokenizers 0.22.2
French fine-tunings of LFM2.5-Audio. Each model lives on its own branch: load it with LFM2AudioModel.frompretrained(repo, revision= ). The main branch only holds this index. Each branch's README gives the model's provenance and results.
This model was trained by ftshijt using vctk recipe in espnet. Follow the ESPnet installation instructions if you haven't done that already.
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.