Search public pages, research tools, and SAVRN solutions.
SAVRN Model Hub
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 2026-10-19 · How the library is built
11,442 models, sorted by most downloaded.
![[HYU_NLP] EVA Team logo](/media/mh-logo-hyu-nlp-eval-558e075b88.webp){kind=logo}
Model · Text generation
Run: phase1-online-rubrics-medicine-full-dense-20260919-seed11. The root contains a BF16 model for inference. originalcheckpoint/ contains the original veRL checkpoint files (model parameters only). The local checkpoint remains on disk. Research use only.
Model · Text generation
Run: phase1-online-rubrics-medicine-full-dense-20260919-seed11. The root contains a BF16 model for inference. originalcheckpoint/ contains the original veRL checkpoint files (model parameters only). The local checkpoint remains on disk. Research use only.
Model · Text generation
Run: phase1-static-r0-medicine-qwen3-4b-matched-dense-20260928-seed11. The root contains a BF16 model for inference. originalcheckpoint/ contains the original veRL checkpoint files (model parameters only). The local checkpoint remains on disk. Research use only.
Model · Text generation
Run: phase1-static-r0-medicine-qwen3-4b-matched-dense-20260928-seed11. The root contains a BF16 model for inference. originalcheckpoint/ contains the original veRL checkpoint files (model parameters only). The local checkpoint remains on disk. Research use only.
Model · Text generation
Run: phase1-static-r0-medicine-qwen3-4b-matched-dense-20260928-seed11. The root contains a BF16 model for inference. originalcheckpoint/ contains the original veRL checkpoint files (model parameters only). The local checkpoint remains on disk. Research use only.
Model · Text generation
Run: phase1-static-r0-medicine-qwen3-4b-matched-dense-20260928-seed11. The root contains a BF16 model for inference. originalcheckpoint/ contains the original veRL checkpoint files (model parameters only). The local checkpoint remains on disk. Research use only.
Model · Text generation
Run: phase1-static-r0-medicine-qwen3-4b-matched-dense-20260928-seed11. The root contains a BF16 model for inference. originalcheckpoint/ contains the original veRL checkpoint files (model parameters only). The local checkpoint remains on disk. Research use only.
This is the policy after 0 global optimizer updates of the matched separate from the OnlineRubrics/dynamic-rubric checkpoints. The root files are a BF16 Transformers export for inference. The originalcheckpoint/ directory contains the exact original veRL/FSDP policy parameter checkpoint and its tokenizer/configuration files. Optimizer, trainer, and data-loader state are intentionally not published; the complete resume checkpoint remains on Daisy. This is an intermediate research checkpoint, not a clinical model. No medical capability or safety claim is made. Original actor parameter SHA256: f81409edc253a52ee9b3e6807bf280cf1ff242c77c2f6645b087c74e03a4e3d4
This is the policy after 3 global optimizer updates of the matched separate from the OnlineRubrics/dynamic-rubric checkpoints. The root files are a BF16 Transformers export for inference. The originalcheckpoint/ directory contains the exact original veRL/FSDP policy parameter checkpoint and its tokenizer/configuration files. Optimizer, trainer, and data-loader state are intentionally not published; the complete resume checkpoint remains on Daisy. This is an intermediate research checkpoint, not a clinical model. No medical capability or safety claim is made. Original actor parameter SHA256: 3339fe6d830916167c26f9e818cef25d0cecdbf3ab26e8545fcbcbf6465bfdc8
This is the policy after 42 global optimizer updates of the matched separate from the OnlineRubrics/dynamic-rubric checkpoints. The root files are a BF16 Transformers export for inference. The originalcheckpoint/ directory contains the exact original veRL/FSDP policy parameter checkpoint and its tokenizer/configuration files. Optimizer, trainer, and data-loader state are intentionally not published; the complete resume checkpoint remains on Daisy. This is an intermediate research checkpoint, not a clinical model. No medical capability or safety claim is made. Original actor parameter SHA256: 3c024ab64a34d4140f446262e7fcee7fbd64da5b9de22cac486a986288e28063
LoRA adapters from a study of reward hacking in GRPO. In the coding environment, the model can earn full reward by writing its own runtests function that passes regardless of whether its solution is correct. Baseline runs learn to do this. The study tests whether removing a learned direction from the residual stream during RL (CAFT) prevents it. 1cfa9a7208912126459214e8b04321603b3df60c. - LoRA rank 32, alpha 32, on all attention and MLP projections. - Each folder holds adapterconfig.json and adaptermodel.safetensors. wave2/ -seed /step /: GRPO runs with 4 arms x 5 seeds, checkpoints at steps 100, 150 and 200 (200 steps, 16 prompts x 16 answers per step). The removal acts at block 19…
Backups of two GRPO baseline runs on Qwen/Qwen3-4B (revision 1cfa9a7208912126459214e8b04321603b3df60c). Both use LoRA rank 32, seed 2, and 16 prompts × 16 answers per step. No direction is removed (a structural-zero hook at block 19). Both runs learned to hack the grader by weakening the tests: modify from about step 90, incontext from about step 120. Layout. backups/wave3loopholes/runs/ / holds every directory of each run. Runs that were continued after an interruption carry -resume... suffixes, and each step is taken from the newest directory that has it. A directory holds: - run configuration and build records; - LoRA adapters every 10 steps; - the full training state at steps 0, 100…
Qwen3-4B-Instruct-2507 consolidated with OEL on the tau2-bench telecom domain, using 120 externally authored task–memory pairs from the revised finalpairs2 set, stratified down from 125 so the batches divide evenly. The student replays each task without memory; the teacher is the same weights with that task's memory in the system prompt. Only the prompt differs, and no reward is used. Five pairs were dropped from the 125 by stratified sampling (seed 42), holding the level ratios to within 1pp, so that no batch is short — earlier runs showed a tiny final batch wrecks the saved checkpoint, and only the last step is saved. Each step reads a different batch, so the loss column tracks batch…
This model was finetuned and converted to GGUF format using Unsloth. - qwen3-4b-thinking-2507.Q4KM.gguf An Ollama Modelfile is included for easy deployment. This was trained 2x faster with Unsloth
A LoRA fine-tune of unsloth/Qwen3-4B that teaches the model when not to call a tool. On BFCL v4 Irrelevance Detection, under identical conditions and a full 240-entry category: The baselines are statistically indistinguishable from this adapter. v1 leads the 8B by 0.84 points and its own base by 1.67 — two and four test cases respectively. The public benchmark does not support the claim that this fine-tune materially improved abstention. The much larger margin in the self-scored eval below does not replicate. The same recipe with --seed 1337 scored 97.50% on irrelevance — 20 entries better than this adapter, and a headline a 4B beating an 8B. It was meaningless. On BFCL Multiple (AST)…
Base: Qwen/Qwen3-8B @ b968826d9c46dd6066d109eabc6255188de91218 (bf16, non-thinking chat template qwen3nothink.jinja from the experiment repo). Part of a multi-expert training + merging experiment on SWE-bench Pro v2-hard, HLE, ASI-Bench and Terminal-Bench 2.0. - mergedties/ - mopd/ - mergedlinearlora64/ - mergedlinear/ Recipes, data sources/licenses, evaluation protocol and results: see the experiment repository (models/README.md, data/README.md, configs/eval/benchmarks.yaml, results/summary.csv). Full-weight subfolders load with AutoModelForCausalLM.frompretrained("cokoro/qwen3-8b-moe-merge-merged", subfolder=" "). Limitations: experts were trained on small/proxy data (GSM8K for HLE…
Qwen3-8B after full-parameter supervised fine-tuning for mathematical keyword and meaning generation. This is the exact saved epoch 4 checkpoint at optimizer step 2976. The repository contains the model weights, configuration, and tokenizer files for inference. Training dataset: devpotatopotato/math-keyword-training, keyword-261001-bigmath-gpt-6-sol.jsonl. Use the tokenizer chat template with enablethinking=False. Pass the following template as a user message and replace {problem} with the problem text
This model is a fine-tuned version of Qwen/Qwen3-8B on the skowshik1/gemma3-sft-agentclinic-style-diagnosis-shaped-v2 dataset. It has been trained using TRL. This model was trained with SFT.
Hand-picked starting points, each with the reason it exists.
Collection · 4 entries
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 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
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
Recognition and synthesis models, grouped so the two directions are easy to compare.
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.
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.
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.
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.
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
The same open-weight model priced by every host that serves it, per million tokens.
Research Hub
Moratoriums, permits, power, water and capital behind the facilities that run these models.
Method
Sources, evidence labels, refresh behaviour, and the limits of every comparison here.
