This is a trained model of a Reinforce agent playing CartPole-v1. To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
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.
12,579Models
3,583Datasets
291Papers
7,578Publishers
12,850Sourced relationships
Updated 2026-10-23 · How the library is built
12,579 models, sorted by most downloaded.
[The first Pixtral-Large-Instruct-2411 124B to be GGUF converted and Quantized!] PLEASE NOTE, FILES ARE STILL UPLOADING!!! This is a rather large model, please check back soon if you do not see any quant files available! The quantized files are currently being uploaded right now, as fast as my network connection will allow for it! EnlistedGhost's GGUF/Quantized Repository of - MistralAI Pixtral-Large-2411 124B This release includes GGUF + Quantized model files and two mmproj multi-model projector Quants (mmproj) files for the Vision Projector, offering full capabilities in Yollama, Ollama or Llama.cpp. No modifications, edits, or configurations are required to use this model with Yollama…
中文语音合成(TTS)模型,支持语音克隆与数字/中英混读,CPU 优先设计,权重原生 int8 量化。 基于 Kyutai Pocket TTS 架构:6 层 FlowLM(dmodel 1024)+ Mimi 神经音频编解码器(24 kHz),端到端流式推理。本版本(R21)相对上一代的核心变化: - 原生 int8 交付:FlowLM 注意力与 FFN 权重已离线量化打包,无需运行时转换,体积 223 MB,CPU 速度提升约 1 倍 - 9174 token 词表:官方英文 BPE-4000 基底 + 追加单字汉字/全角标点,bytefallback 兜底生僻字符 - 中英混读:APP、VIP、5G、WiFi 等英文术语按中文习惯读法 - 模型为预量化 int8 权重(modelint8.pt),加载即用,无需 quantize=True 或有界面演示(需 pip install gradio): - 采样温度 temp=0.3(默认);文本越长对参考音频质量越敏感 文本准确性用 whisper-small 对合成音频转写后计算字错误率(CER),每个场景 10 次独立采样取均值,生产配置(单步采样)。数值越低越好。 测试环境:Intel i7-12700KF,生成 52 字客服长句(约 8.5 秒音频),每档 3 次取均值。 参考音频:客服目标音色(kefulong / digitsorder / codeswitch / shortunseen)与 AISHELL-3 说话人 A/B(speakerAlong /…
static quants of https://huggingface.co/PreciseCoder/PreciseCoder-9B For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality…
Official evaluation checkpoints for PredActor, a predictive action diffusion controller for steerable Unitree G1 locomotion. The model jointly predicts future states and actions, uses its internal state trajectory for guidance, and directly executes the selected 29-DoF action. This repository contains the two learned artifacts required by the public - the released PDP051 text-conditioned PredActor policy; - the fine-tuned G1 MotionCLIP encoder used for semantic conditioning. Source code, the browser-based evaluator, and G1 robot assets are maintained in the public PredActor repository. Training datasets, experiment outputs, hardware deployment bundles, and additional checkpoints are not…
weighted/imatrix quants of https://huggingface.co/PrentisAI/Prentis-EQ-2 For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/Prentis-EQ-2-GGUF This is a vision model - mmproj files (if any) will be in the static repository. If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here are Artefact2's thoughts on the matter…
Prism:Dynamic Sparse Attention for Native 2K Joint Video-Audio Generation Model Training Shuyuan Tu 1, Qi Tian 2, Yinming Huang 1, Yue Wu 2, Xintong Han 2, Kaihang Pan 3, Weijie Kong 2, Jiangfeng Xiong 2, Jian-Wei Zhang 2, Zuxuan Wu 1, Yu-Gang Jiang 1 [ 1 Fudan University; 2 Tencent Hunyuan; 3 Zhejiang University] Native training joint video-audio generation models at higher resolutions empowers them to learn richer visual details and sharper motion dynamics. However, full attention incurs quadratic cost and, as resolution increases, spreads attention over increasingly redundant tokens, diluting learning signals for informative content and disrupting pretrained priors. Existing sparse…
PrismaCoder-3B is a Qwen2.5-Coder-3B-Instruct fine-tune specialized for generating and editing Prisma ORM schemas and client code from natural-language instructions. It was trained with a full SFT → DPO → GRPO pipeline, using a verifier (gate) as both the data-quality filter and the reinforcement-learning reward signal. stage, LoRA weights merged into the base model between stages iterative self-play RL round (attempted; see Limitations) weights for release All stages used seed 20260830 and ran on a single Kaggle T4 GPU (16GB) with gradient checkpointing and paged 8-bit AdamW. verifier (LightValidator, "V0" in the project's internal gate numbering): it checks that generated Prisma…
ProAR solves reasoning tasks by autoregressively generating a sequence of visual states. It equips autoregressive video models with two complementary components: - Outcome Guidance predicts a future goal frame to guide current-chunk generation. - Transition Guidance aligns the current representation with the clean next-chunk representation during training. ProAR is built upon Wan2.2-TI2V-5B. hf download LinghuiShen/ProAR \ --local-dir checkpoints/ProAR
Creator, Sole Author & Lead Architect: Alan Canto Architecture: ModernBERT-large (System 1 Non-Autoregressive Decision Engine) Hardware Runtime: Trained, fine-tuned, and certified locally on NVIDIA GeForce RTX 3050 Attribution: Conceived, engineered, and published by Alan Canto. All rights reserved. ProKope 421M (named after the classical Stoic concept of disciplined, measured progress and operational mastery) is a high-speed, non-autoregressive System 1 decision model engineered by Alan Canto. Unlike autoregressive language models (which incur significant token latency, JSON parsing errors, and hallucination loops), ProKope 421M processes structured input states in a single forward pass…
This repository provides the source code and experimental resources for ProSRL (Prototype-guided Semantic Representation Learning), a prototype-guided approach for cross-project defect prediction (CPDP). ProSRL learns semantic representations from lightweight syntax graphs and incorporates prototype-guided semantic representation learning and distribution adaptation to improve defect prediction across different software projects. ProSRL is implemented in Python and is recommended to be executed on Linux, ideally Ubuntu 22.04.3 LTS. Prerequisites Ubuntu 22.04.3 LTS Python 3.8+ PyTorch DGL CUDA-compatible NVIDIA GPU The experiments reported in the paper were conducted using an NVIDIA GPU with…
Supervised fine-tuned version of Prochlo-190M-Base — a 190.5M-parameter dense English SLM trained from scratch. All variants share architecture, tokenizer, and vocabulary. 26-item behavioral rubric (format adherence, anti-sycophancy, code, concision): 61.5% pass. Strong on code instructions and factual QA. Weak on concise replies, long multi-turn coherence, and arithmetic. English only — Chinese input produces garbage by design. The chat format is plain text with control tokens, not a chat template: Note: weights are sharded across 32 safetensors files with a model.safetensors.index.json; frompretrained handles this transparently. Apache-2.0
weighted/imatrix quants of https://huggingface.co/Naphula/Promethean-Dawn-26B-A4B For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/Promethean-Dawn-26B-A4B-GGUF This is a vision model - mmproj files (if any) will be in the static repository. If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here are Artefact2's thoughts on…
Built with Qwen. Community conversions, not an official Qwen release and not endorsed by Alibaba / Qwen. Distributed under the included Qwen Research License for non-commercial use; see LICENSE and Notice. These are prompt-enhancement models, not diffusion checkpoints or image-conditioning text encoders. T2I expands a text prompt; I2I expands an editing instruction using reference images. They return structured text intended to be parsed into a rewritten prompt and aspect-ratio advice. The source authors describe their derivatives as Heretic/abliterated. This conversion does not independently establish those behavioral claims. The I2I source is the named darrellbest derivative, not a…
Frozen protein embeddings, input-audit records and label files for the paper ProtCompass: Biological Information and Predictive Utility in Protein Representations (anonymous submission to ICLR 2027). Protein encoders are usually evaluated by structural probing, downstream prediction and concept erasure. These measurements can mix signal learned by the encoder with information that is already present in the input or that the evaluation adds. ProtCompass separates these sources. It audits which inputs each encoder actually reads, compares structural probe scores with non-learned descriptions of the same inputs, compares downstream scores with simple sequence baselines, and controls…
Computationally generated and predicted molecular structures from Project Proteus, the biological discovery research program of Inserloft Research. This repository contains structural representations associated with computational protein-design experiments conducted within Project Proteus. Proteus Structures is a structural repository for molecular candidates generated or investigated through the Proteus research program. The structures in this repository are intended to support: Unless explicitly stated otherwise, structures contained here are computational models and should not be interpreted as experimentally determined structures. Additional structures may be added as Proteus research…
Protium-1.0 is Solstice-AI's high-efficiency, objective-gated cognitive reasoning architecture for open-weight foundation models. Named after Protium ($^1\text{H}$) — the fundamental isotope of hydrogen featuring zero neutrons and zero dead weight — Protium-1.0 replaces traditional, unconstrained chain-of-thought bloat with a lean, forward-only reasoning pipeline. Rather than imposing static token minimums, rigid pseudo-schemas, or unconstrained internal deliberation loops, Protium-1.0 operates on an Objective-Gated Protocol: every reasoning sequence anchors its verification criteria at step one, reasons strictly forward toward that objective, and terminates deliberation the moment verified…
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.