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.
Updated 2026-10-20 · How the library is built
11,734 models, sorted by most downloaded.
My first hugging face read me file for reading files
This branch contains the final LoRA adapter for selection seed 42. Training seed is 42 for every branch. main contains seed 42; seed1234 and seed2026 contain the other selection seeds. dataselection/selections/accuracyhard10pctseedsweep/seed42.jsonl in the source project. GRPO prompts use the lgconvfinmcqgrpo formatter, which uses the convfinqagrpo prompt template. 8 generations, reward weights 1.0 for answer correctness and 0.1 for format. before publication. This repository contains adapters, not merged base weights. No validation benchmark was run as part of this training job. The training status and final adapter files were checked before upload. Use…
This is the model card of a transformers model that has been pushed on the Hub. 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).
This is the model card of a transformers model that has been pushed on the Hub. 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).
This is the model card of a transformers model that has been pushed on the Hub. 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).
EXAONE-3.5-7.8B-Instruct LoRA SFT on an answer-only ConvFinQA subset. Expected local base-model cache revision: 553ea250b9a5317231459279d5847d6cf955b9aa (training loader used the Hub ID without an explicit revision pin). Selection manifest SHA256: 3e42a1366dde05eb5fff7b7e563d12b0995714bb6d3f4f08f413484f768bb219. main is the final saved validation-best adapter; epoch branches are: - epoch1-step82: checkpoint-82 - epoch2-step164: checkpoint-164 - epoch3-step246: checkpoint-246 These are PEFT adapter weights, not merged full-model weights.
Weights and frozen goal banks behind the paper. A 3D trace world model predicts one future per episode; the Entity-Level Goal Readout turns that prediction into a single executable goal in SE(3), and a shared Pose-Native Executor runs it closed loop at 20 Hz across five ManiSkill3 tasks. Reproducing the main table needs the executors and the banks only — no planner, no gated licence, one GPU: Everything else — the full install, the inference and training chains, which record backs which number — is in the code repository's README and docs/REPRODUCTION.md. Closed-loop success, %, PickCube / LiftPegUpright / PegInsertionSide / StackCube / PushCube. The readout rows share one set of executor…
该仓库复现了一种攻击形态：模型的 config.json / 固定路径脚本等 metadata （ClamAV / picklescan / TruffleHog / 第三方模型扫描器）的分析单位是文件内容。 handler.py 的模块级代码仅执行： 不执行 shell、不发起网络连接、不读取凭证、不修改临时目录以外的路径。 请联系 [email protected]。 - 研究联系人：[email protected] - 已事先通报：[email protected]
该仓库复现了一种攻击形态：模型的 config.json / 固定路径脚本等 metadata （ClamAV / picklescan / TruffleHog / 第三方模型扫描器）的分析单位是文件内容。 customgenerate/generate.py 的模块级代码仅执行： 不执行 shell、不发起网络连接、不读取凭证、不修改临时目录以外的路径。 请联系 [email protected]。 - 研究联系人：[email protected] - 已事先通报：[email protected]
Development checkpoints for controlled research access. Stored artifacts require manual approval.
This 1,069,302,417-parameter ranker continues Experiment 1.9 from its best checkpoint, step 328 / 104,960 scheduled samples. The parent's monitoring Spearman was 0.08534306347402562, not a new Experiment 1.9.1 test result. The parent architecture and pinned data/monitoring split are unchanged. The requested run uses twelve Hunter MI300A nodes, four GPU workers each, BF16 compute and FP32 master weights/Adam. The per-update learning rate is capped at 0.00006. Global batch increases from 320 to 1536, with microbatch 2 and balanced gradient accumulation. Adam and sampler state are restored; RNG streams are reseeded for 48 workers. This is not bitwise continuation. Best/latest inference…
A small Coca implementation for Classification, packaged with an explicit configuration and an initialization checkpoint. The tiny variant is a reproducible starting point, not a trained model release. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses adamw with a polynomial schedule. These are starting values in the script, not…
A research-oriented DeiT prototype targeting Matching. The included nano setup documents defaults and file formats without presenting unverified performance numbers. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses lion with a cosine schedule. These are starting values in the script, not evidence of a completed run. For a meaningful…
Derived from black-forest-labs/FLUX.2-klein-4B revision e7b7dc27f91deacad38e78976d1f2b499d76a294. Apache-2.0 model. Uses Flux2Kit's native layout with affine INT4 denoiser weights (group size64), FP16 VAE at runtime, and prepared full-precision generic style contexts keyed by their exact prompt SHA256. No text-encoder weights or player drawings are included. Intended for the matching Explain Earth FLUX runtime, not a drop-in Diffusers checkpoint. Eight generic styles: LEGO, Gingerbread, Van Gogh, Embroidery, Comic Book, Airbrush, Food Art, Sandcastle. Three Food Art and three painted color-palette variants are prepared. Inference receives drawing pixels; it never receives the answer…
A typed-decision model (Jev-style /v1/systemone: choice, noul and score questions answered with a probability per option from one forward pass). LoRA fine-tune of Qwen/Qwen3.5-4B, merged into full weights, in two stages: 1. Stage 1 (ezjev-4b): LoRA r=16 on all language-model linear layers (attention, Gated-DeltaNet, MLP), LR 1e-4, one epoch over ~85k rows / ~106k questions. Loss: cross-entropy + Brier over the option labels, llm2jev chat prompt (thinking off). 2. Stage 2: a second LoRA at LR 5e-5 on ~30k rows targeting weak task families (RAG hallucination, product relevance, code-output selection, select-all-that-apply, clinical NLI, phishing emails, claim verification) with 40% replay.…
Model · Text generation
f-cov-l0-0-no-eos-qwen3-1.7b-compression-bs32-n16-32k-146103-final
Standard BF16 Hugging Face model, exported from training step 100. Weights, configuration, tokenizer, and the thinking chat template are at the repository root. Source checkpoint: hi-todayis-jh/f-cov-l0-0-no-eos-qwen3-1.7b-compression-bs32-n16-32k-146103-step100 at commit a4f98caceb123c954e6a6b176ec2e9b666fc970f. Load this repository directly with AutoModelForCausalLM.frompretrained or vLLM. Use enablethinking=True in the tokenizer chat template for the existing thinking evaluations. The export manifest records source provenance, file checksums, tensor validation, and a successful Transformers CPU load and forward pass.
This repository provides an instruction-tuned causal language model for text generation and chat-style prompts. The configured snapshot destination is available here. Use a current release of transformers to load the model and tokenizer: For long inputs, use context settings supported by the installed runtime and account for available memory. See LICENSE for the applicable terms.
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.