JSONL inference traces only (no generated WAVs). Each file is named after the model. Records follow the FlexiSLM eval trace schema (index, task, input, output, evaluation, metadata). - flexislmstage2released.jsonl — FlexiSLM-7B Stage2 released checkpoint (ckpt-150000), 12.5 Hz in/out, flow-matching s2s on VoiceBench + OpenAudioBench.
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.
11,442Models
3,247Datasets
285Papers
6,976Publishers
12,091Sourced relationships
Updated 2026-10-19 · How the library is built
11,442 models, sorted by most downloaded.
models-for-asr-chain/ 集中保存 AX650 音频智能链路运行时使用的模型和配套资源。它位于驱动与业务编排层之间：driver/ 和 framework/ 提供模型加载、推理及调度代码，config/sdk-config.yaml 指定这里的模型路径，部署脚本再将生产所需资源同步到目标板。 模型路径由 config/sdk-config.yaml 配置。生产部署应使用 scripts/deploy-ax650.sh 的白名单增量同步，避免遗漏 tokenizer、配置文件或分层 AXModel，也避免将开发期中间文件复制到目标板。 这些文件大多面向 AX650 和指定的 AX Engine/VNPU 模式，不能仅按文件名替换为其他平台或其他编译参数生成的模型。模型升级后应重新执行完整编译、部署依赖检查，并验证对应推理链路。
A full fine-tune of answerdotai/ModernBERT-large for comparing whether two English fanfiction documents share an author. Training used peterkirby/pan2020dictauthorfandomdoc, a global contrastive batch of 4096 documents, and a maximum input length of 4096 tokens per document. The saved binary cosine-similarity rule achieved 98.4199% accuracy on the PAN21 test split. The original evaluation records are included in evaluationresults.json and traininghistory.parquet. The encoder has 28 Transformer layers, 1024-dimensional hidden states, and 394,781,696 encoder parameters. It uses the ModernBERT tokenizer, attention-mask-aware mean pooling over all non-padding tokens, and L2 normalization.…
A 162M-parameter decoder trained twice from the same initialization on the same 2.62 billion FineWeb-Edu tokens, once along each of two different routes through NVIDIA, AMD and Apple GPUs. The two runs are bitwise identical at every one of the 5,000 optimizer steps: training state, summed gradients, all 64 per-shard losses and the learning rate. Their final checkpoint files have the same SHA-256. This repository distributes that one final model with the evidence from both routes. The model is GPT-3 Small-sized and trained with GPT-3 Small's hyperparameters, but its blocks are modern Llama-style blocks; see the comparison below. It is a research artifact for reproducible training, not a…
Go board detector used by Kaya to turn a photo of a goban into a position (SGF). Trained with moku. Kaya computes a homography from the 4 corners and snaps every stone onto the grid. - model.safetensors, config.json, preprocessorconfig.json: transformers checkpoint. - model.onnx: what Kaya runs (ONNX Runtime Web). Input pixelvalues (batch, 3, 640, 640), RGB scaled to [0, 1] with no mean/std normalization; outputs logits (batch, 300, 3) (apply a sigmoid) and predboxes (batch, 300, 4), normalized (cx, cy, w, h). With the corner head, a third output cornerpoints (batch, 8, 3) holds the 8 best board-corner peaks (x, y, score), x and y normalized to [0, 1] (class-agnostic). The stone-threshold…
MolmoAct2 is an open robotics foundation model from the Allen Institute for AI (Ai2) that maps camera images and language instructions to robot action chunks. The LeRobot implementation supports training and evaluation of the regular MolmoAct2 model. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot molmoact2 guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data…
MolmoAct2 fine-tuned with LeRobot (lerobot commit c13d79e6e350c589babc5b7c5cf5ef05cb0fa58e), exported from /home/sakakibara/bachelorthesis/outputs/train/molmoact2yameasybrightness1epqueue20261010211513/both/checkpoints/003600/pretrainedmodel (trainer step 3600). LoRA adapters are merged; weights are float32. normstats.json holds the normalization statistics of the fine-tuning run under yamdualmolmoact2. lerobotcheckpoint/ holds the LeRobot policy config, training config, and processor files (no weights); exportmanifest.json records the source checkpoint hash and the export verification. Loading with dtype=torch.bfloat16 rounds the merged weights and loses much of the fine-tuning update.
MolmoAct2 fine-tuned with LeRobot (lerobot commit c13d79e6e350c589babc5b7c5cf5ef05cb0fa58e), exported from /home/sakakibara/bachelorthesis/outputs/train/molmoact2yameasybrightness1epqueue20261010211513/both/checkpoints/007200/pretrainedmodel (trainer step 7200). LoRA adapters are merged; weights are float32. normstats.json holds the normalization statistics of the fine-tuning run under yamdualmolmoact2. lerobotcheckpoint/ holds the LeRobot policy config, training config, and processor files (no weights); exportmanifest.json records the source checkpoint hash and the export verification. Loading with dtype=torch.bfloat16 rounds the merged weights and loses much of the fine-tuning update.
MolmoAct2 fine-tuned with LeRobot (lerobot commit c13d79e6e350c589babc5b7c5cf5ef05cb0fa58e), exported from /home/sakakibara/bachelorthesis/outputs/train/molmoact2yamspellceateceasyftcontinuousgpu220261008232437/checkpoints/004000/pretrainedmodel (trainer step 4000). LoRA adapters are merged; weights are float32. normstats.json holds the normalization statistics of the fine-tuning run under yamdualmolmoact2. lerobotcheckpoint/ holds the LeRobot policy config, training config, and processor files (no weights); exportmanifest.json records the source checkpoint hash and the export verification. Loading with dtype=torch.bfloat16 rounds the merged weights and loses much of the fine-tuning update.
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.