CAMP (Compressed Action Memory Policy) gives a visuomotor policy a behavioral memory. A recurrent memory is pretrained to reconstruct a compressed (DCT) summary of the robot's own past actions, so its hidden state has to encode what the robot already did; a Diffusion Policy is then conditioned on that state. This lets the policy track task progress and learn from its own failed attempts, which a memoryless policy cannot do when the current image does not determine the next action. This repository holds the CAMP policies for all nine tasks of RMBench, a memory-dependent bimanual manipulation benchmark built on RoboTwin 2.0 (Aloha-AgileX dual-arm robot). Every policy is trained from the…
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.
12,870Models
3,655Datasets
291Papers
7,691Publishers
12,980Sourced relationships
Updated 2026-10-24 · How the library is built
12,870 models, sorted by most downloaded.
This repository contains QPR artifacts for the dense, unpruned openbmb/MiniCPM5-2B model. The quantizer uses dense int4 weights with group size 128 and GPTQ calibration; it does not apply 2:4 pruning. The recommended file is dense-int4-distill2000-v2-best.qpr. The minicpm5-2b-dense-int4-g128.qpr file is the matching dense-int4 baseline. Each QPR file is about 1.30 GB and is stored with Git LFS. The candidate was selected from a 2,000-update distillation run starting at the dense-int4 baseline. It was selected at update 775 and independently reloaded before scoring. On 100 held-out Chinese and English prompts with 512 new tokens, direct gpt-6-sol judging gave 39 baseline wins, 58 candidate…
YOCO 式因果 encoder-decoder MoE。从 MiniCPM5-2B 上采样：openbmb/MiniCPM5-2B-Base（Apache-2.0，Llama GQA）。 libraryname: transformers 只表示 tokenizer / 分片约定走 HuggingFace 生态。当前图是仓库里的 catyoko PyTorch 实现，不是 Hub 上可 AutoModelForCausalLM 直接加载的架构。 50B token 信封上的理论墙钟，不是实测。B200 / SM100 允许的线性 GEMM 走 TeNvfp4Linear（TE NVFP4BlockScaling；B0 冻 encoder 走 FPROP，WGRAD 留给 B1/B2）。无 TE / sm120 时 Nvfp4Linear E2M1/16 仿真。attn softmax / SDPA 仍 fp32。 发布信封 进行中（DummyStream，尚未跑完 8e9）。Vast B200 已回收（2026-09-19）。本文件是释放前快照，不是终局。GitHub 口径：docs/STATUS.md。 代码与指针：GitHub AvrovaDonz2026/CAT-YOKO（不用 LFS）。 这些 overlay 里，checkpoints/b0/ 与 checkpoints/b0-nvfp4-try/ 不是 8B token 信封（只是 --try）。checkpoints/b0-full/trainable.pt 是发布信封 进行中 的 B0…
CEDR generates reasoning solutions with continuous latent diffusion and a learned prompt encoder. This repository contains six selected-weight inference packages for GSM8K, MATH500, and coding, before and after NFT. The training and inference code and installation instructions are at chengxiang/CEDR. All use async clocks (2.5, 2, 1.5), CFG 1, batch size 32, and a 1,024-token diffusion canvas. The GSM8K post-NFT preset caps decoded responses at 767 tokens. Other presets use the canvas available after each prompt. See the code repository's evaluation guide for MATH500 and official HumanEval/MBPP base/plus scoring. Coding generation uses the combined 164 HumanEval and 378 MBPP tasks. Standard…
A FT expert from Table 2 of SMAT: Simple and Efficient Merge-Aware Training (seed 42). SMAT trains experts with model merging in mind. encoder.pt is the original FP32 vision-encoder state dictionary; load it with the SMAT code and the matching OpenAI CLIP base model. Dataset terms apply separately.
A FT expert from Table 2 of SMAT: Simple and Efficient Merge-Aware Training (seed 42). SMAT trains experts with model merging in mind. encoder.pt is the original FP32 vision-encoder state dictionary; load it with the SMAT code and the matching OpenAI CLIP base model. Dataset terms apply separately.
A FT expert from Table 2 of SMAT: Simple and Efficient Merge-Aware Training (seed 42). SMAT trains experts with model merging in mind. encoder.pt is the original FP32 vision-encoder state dictionary; load it with the SMAT code and the matching OpenAI CLIP base model. Dataset terms apply separately.
A FT expert from Table 2 of SMAT: Simple and Efficient Merge-Aware Training (seed 42). SMAT trains experts with model merging in mind. encoder.pt is the original FP32 vision-encoder state dictionary; load it with the SMAT code and the matching OpenAI CLIP base model. Dataset terms apply separately.
A FT expert from Table 2 of SMAT: Simple and Efficient Merge-Aware Training (seed 42). SMAT trains experts with model merging in mind. encoder.pt is the original FP32 vision-encoder state dictionary; load it with the SMAT code and the matching OpenAI CLIP base model. Dataset terms apply separately.
A FT expert from Table 2 of SMAT: Simple and Efficient Merge-Aware Training (seed 42). SMAT trains experts with model merging in mind. encoder.pt is the original FP32 vision-encoder state dictionary; load it with the SMAT code and the matching OpenAI CLIP base model. Dataset terms apply separately.
A FT expert from Table 2 of SMAT: Simple and Efficient Merge-Aware Training (seed 42). SMAT trains experts with model merging in mind. encoder.pt is the original FP32 vision-encoder state dictionary; load it with the SMAT code and the matching OpenAI CLIP base model. Dataset terms apply separately.
A FT expert from Table 2 of SMAT: Simple and Efficient Merge-Aware Training (seed 42). SMAT trains experts with model merging in mind. encoder.pt is the original FP32 vision-encoder state dictionary; load it with the SMAT code and the matching OpenAI CLIP base model. Dataset terms apply separately.
A SMAT expert from Table 2 of SMAT: Simple and Efficient Merge-Aware Training (seed 42). SMAT trains experts with model merging in mind. encoder.pt is the original FP32 vision-encoder state dictionary; load it with the SMAT code and the matching OpenAI CLIP base model. Dataset terms apply separately.
A SMAT expert from Table 2 of SMAT: Simple and Efficient Merge-Aware Training (seed 42). SMAT trains experts with model merging in mind. encoder.pt is the original FP32 vision-encoder state dictionary; load it with the SMAT code and the matching OpenAI CLIP base model. Dataset terms apply separately.
A SMAT expert from Table 2 of SMAT: Simple and Efficient Merge-Aware Training (seed 42). SMAT trains experts with model merging in mind. encoder.pt is the original FP32 vision-encoder state dictionary; load it with the SMAT code and the matching OpenAI CLIP base model. Dataset terms apply separately.
A SMAT expert from Table 2 of SMAT: Simple and Efficient Merge-Aware Training (seed 42). SMAT trains experts with model merging in mind. encoder.pt is the original FP32 vision-encoder state dictionary; load it with the SMAT code and the matching OpenAI CLIP base model. Dataset terms apply separately.
A SMAT expert from Table 2 of SMAT: Simple and Efficient Merge-Aware Training (seed 42). SMAT trains experts with model merging in mind. encoder.pt is the original FP32 vision-encoder state dictionary; load it with the SMAT code and the matching OpenAI CLIP base model. Dataset terms apply separately.
A SMAT expert from Table 2 of SMAT: Simple and Efficient Merge-Aware Training (seed 42). SMAT trains experts with model merging in mind. encoder.pt is the original FP32 vision-encoder state dictionary; load it with the SMAT code and the matching OpenAI CLIP base model. Dataset terms apply separately.
A SMAT expert from Table 2 of SMAT: Simple and Efficient Merge-Aware Training (seed 42). SMAT trains experts with model merging in mind. encoder.pt is the original FP32 vision-encoder state dictionary; load it with the SMAT code and the matching OpenAI CLIP base model. Dataset terms apply separately.
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.