# Models, Page 395 of 477
Source: https://savrn.com/models/page/395
Markdown alternate of the page above; the site index is https://savrn.com/llms.txt

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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](https://savrn.com/model-hub/methodology)

## Filter Models

Task

- [Text generation1,413](https://savrn.com/models?task=Text+generation)
- [Image and text to text512](https://savrn.com/models?task=Image+and+text+to+text)
- [Feature extraction341](https://savrn.com/models?task=Feature+extraction)
- [Robotics338](https://savrn.com/models?task=Robotics)
- [Text classification303](https://savrn.com/models?task=Text+classification)
- [Text to image209](https://savrn.com/models?task=Text+to+image)
- [Speech recognition171](https://savrn.com/models?task=Speech+recognition)
- [Audio classification157](https://savrn.com/models?task=Audio+classification)
- [Image classification137](https://savrn.com/models?task=Image+classification)
- [Text to speech132](https://savrn.com/models?task=Text+to+speech)
- [Sentence similarity103](https://savrn.com/models?task=Sentence+similarity)
- [Token classification97](https://savrn.com/models?task=Token+classification)
- [Reinforcement learning93](https://savrn.com/models?task=Reinforcement+learning)
- [Zero-shot classification93](https://savrn.com/models?task=Zero-shot+classification)
- [Image segmentation88](https://savrn.com/models?task=Image+segmentation)
- [Object detection86](https://savrn.com/models?task=Object+detection)
- [Any to any79](https://savrn.com/models?task=Any+to+any)
- [Translation78](https://savrn.com/models?task=Translation)
- [Question answering73](https://savrn.com/models?task=Question+answering)
- [Fill mask69](https://savrn.com/models?task=Fill+mask)
- [Time series forecasting64](https://savrn.com/models?task=Time+series+forecasting)
- [Video classification59](https://savrn.com/models?task=Video+classification)
- [Image to text58](https://savrn.com/models?task=Image+to+text)
- [Text to video55](https://savrn.com/models?task=Text+to+video)

Library

- [transformers2,777](https://savrn.com/models?library=transformers)
- [peft345](https://savrn.com/models?library=peft)
- [diffusers187](https://savrn.com/models?library=diffusers)
- [pytorch178](https://savrn.com/models?library=pytorch)
- [gguf167](https://savrn.com/models?library=gguf)
- [lerobot139](https://savrn.com/models?library=lerobot)
- [sentence-transformers139](https://savrn.com/models?library=sentence-transformers)
- [mlx136](https://savrn.com/models?library=mlx)
- [timm49](https://savrn.com/models?library=timm)
- [transformers.js45](https://savrn.com/models?library=transformers.js)
- [flaxspeaker44](https://savrn.com/models?library=flaxspeaker)
- [llama.cpp44](https://savrn.com/models?library=llama.cpp)
- [vllm38](https://savrn.com/models?library=vllm)
- [onnx36](https://savrn.com/models?library=onnx)
- [diffusion-single-file27](https://savrn.com/models?library=diffusion-single-file)
- [minimax-h324](https://savrn.com/models?library=minimax-h3)
- [ultralytics24](https://savrn.com/models?library=ultralytics)
- [PaddleOCR22](https://savrn.com/models?library=PaddleOCR)
- [openvino18](https://savrn.com/models?library=openvino)
- [coreml17](https://savrn.com/models?library=coreml)
- [exllamav316](https://savrn.com/models?library=exllamav3)
- [openpi16](https://savrn.com/models?library=openpi)
- [litert15](https://savrn.com/models?library=litert)
- [rkllm15](https://savrn.com/models?library=rkllm)

Modality

- [Other6,473](https://savrn.com/models?modality=Other)
- [Text2,612](https://savrn.com/models?modality=Text)
- [Image and text570](https://savrn.com/models?modality=Image+and+text)
- [Image565](https://savrn.com/models?modality=Image)
- [Audio478](https://savrn.com/models?modality=Audio)
- [Control431](https://savrn.com/models?modality=Control)
- [Video114](https://savrn.com/models?modality=Video)
- [Multimodal79](https://savrn.com/models?modality=Multimodal)
- [Time series64](https://savrn.com/models?modality=Time+series)
- [Tabular56](https://savrn.com/models?modality=Tabular)

License

- [apache-2.03,105](https://savrn.com/models?license=apache-2.0)
- [mit1,246](https://savrn.com/models?license=mit)
- [other703](https://savrn.com/models?license=other)
- [cc-by-nc-4.0193](https://savrn.com/models?license=cc-by-nc-4.0)
- [cc-by-4.0148](https://savrn.com/models?license=cc-by-4.0)
- [bsd-3-clause98](https://savrn.com/models?license=bsd-3-clause)
- [gemma62](https://savrn.com/models?license=gemma)
- [cc-by-nc-sa-4.049](https://savrn.com/models?license=cc-by-nc-sa-4.0)
- [llama3.240](https://savrn.com/models?license=llama3.2)
- [agpl-3.032](https://savrn.com/models?license=agpl-3.0)
- [llama3.128](https://savrn.com/models?license=llama3.1)
- [unknown27](https://savrn.com/models?license=unknown)
- [cc-by-sa-4.020](https://savrn.com/models?license=cc-by-sa-4.0)
- [gpl-3.017](https://savrn.com/models?license=gpl-3.0)
- [openrail++17](https://savrn.com/models?license=openrail%2B%2B)
- [openmdw-1.115](https://savrn.com/models?license=openmdw-1.1)
- [llama314](https://savrn.com/models?license=llama3)
- [openrail14](https://savrn.com/models?license=openrail)
- [cc-by-nc-nd-4.013](https://savrn.com/models?license=cc-by-nc-nd-4.0)
- [creativeml-openrail-m11](https://savrn.com/models?license=creativeml-openrail-m)
- [afl-3.07](https://savrn.com/models?license=afl-3.0)
- [llama27](https://savrn.com/models?license=llama2)
- [osl-3.06](https://savrn.com/models?license=osl-3.0)
- [cc0-1.04](https://savrn.com/models?license=cc0-1.0)

Access

- [Open weights11,087](https://savrn.com/models?access=open_access)
- [Access requested at publisher355](https://savrn.com/models?access=gated)

Publisher

- [Team Mradermacher294](https://savrn.com/models?publisher=mradermacher)
- [Yurun Yuan95](https://savrn.com/models?publisher=ryanyr)
- [RunningHub.ai94](https://savrn.com/models?publisher=runninghubai)
- [Qwen91](https://savrn.com/models?publisher=qwen)
- [Jenny Wilson81](https://savrn.com/models?publisher=jenny08311)
- [RKNNAI73](https://savrn.com/models?publisher=rknnai)
- [AI at Meta72](https://savrn.com/models?publisher=facebook)
- [Jessen Pu69](https://savrn.com/models?publisher=putty77)
- [Convergent Intelligence66](https://savrn.com/models?publisher=reaperdoesntknow)
- [Google64](https://savrn.com/models?publisher=google)
- [Yurun Yuan63](https://savrn.com/models?publisher=yurunyyr)
- [Firza Hadzami61](https://savrn.com/models?publisher=firzahdzm)
- [Zeyuan Yang59](https://savrn.com/models?publisher=miiche)
- [Yanggangu57](https://savrn.com/models?publisher=yanggangu)
- [Hongao56](https://savrn.com/models?publisher=cisco1963)
- [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805)
- [Wang49](https://savrn.com/models?publisher=davidwdw)
- [Microsoft48](https://savrn.com/models?publisher=microsoft)
- [Yurun Yuan48](https://savrn.com/models?publisher=yuruny)
- [NVIDIA46](https://savrn.com/models?publisher=nvidia)
- [Khazzz1c44](https://savrn.com/models?publisher=khazic)
- [Quan44](https://savrn.com/models?publisher=wq2012)
- [BAAR43](https://savrn.com/models?publisher=aixk)
- [Christopher Subia-Waud42](https://savrn.com/models?publisher=cwaud)

[Most downloaded](https://savrn.com/models?sort=downloads)[Most liked](https://savrn.com/models?sort=likes)[Recently updated](https://savrn.com/models?sort=updated)[Name](https://savrn.com/models?sort=name)[Largest](https://savrn.com/models?sort=params)

11,442 models, sorted by most downloaded.

Model · Robotics

### [pi05-libero-source-free-common-full-action-lr01-view_large-1000-20261011](https://savrn.com/models/pi05-libero-source-free-common-full-action-lr01-view-large-1000-20261011)

[Donghyun Shin](https://savrn.com/model-publishers/donghyun1228)

Raw step999 after 1000 target-only IDM updates, batch32. This pilot trains vision and the full action expert (11 leaves); the graph is original gemma300m with no LoRA. Action updates are multiplied by 0.1 after native AdamW. The original source wrist is restored for deployment. The same recipe is used across view and action-frame shifts. No source observations, paired teacher cache, successful task rollout, or task-success feedback is used. Newly unfrozen source parameters are FP32 during training; they were frozen BF16 in the earlier LoRA-only control. Thus this ablation changes trainability and training precision. Full-expert initialization verifies loaded-to-initialized bytes and BF16…

Open weights openpi

[View model](https://savrn.com/models/pi05-libero-source-free-common-full-action-lr01-view-large-1000-20261011)

Model · Robotics

### [pi05-libero-source-free-common-full-action-lr01-yaw30-1000-20261011](https://savrn.com/models/pi05-libero-source-free-common-full-action-lr01-yaw30-1000-20261011)

[Donghyun Shin](https://savrn.com/model-publishers/donghyun1228)

Raw step999 after 1000 target-only IDM updates, batch32. This pilot trains vision and the full action expert (11 leaves); the graph is original gemma300m with no LoRA. Action updates are multiplied by 0.1 after native AdamW. The original source wrist is restored for deployment. The same recipe is used across view and action-frame shifts. No source observations, paired teacher cache, successful task rollout, or task-success feedback is used. Newly unfrozen source parameters are FP32 during training; they were frozen BF16 in the earlier LoRA-only control. Thus this ablation changes trainability and training precision. Full-expert initialization verifies loaded-to-initialized bytes and BF16…

Open weights openpi

[View model](https://savrn.com/models/pi05-libero-source-free-common-full-action-lr01-yaw30-1000-20261011)

Model · Robotics

### [pi05-libero-source-free-common-llm-action-lr01-view_large-1000-20261011](https://savrn.com/models/pi05-libero-source-free-common-llm-action-lr01-view-large-1000-20261011)

[Donghyun Shin](https://savrn.com/model-publishers/donghyun1228)

Raw step999 after 1000 target-only IDM updates, batch32. This pilot trains vision and the base LLM (9 leaves); the graph is gemma300m with action LoRA. Action updates are multiplied by 0.1 after native AdamW. The original source wrist is restored for deployment. The same recipe is used across view and action-frame shifts. No source observations, paired teacher cache, successful task rollout, or task-success feedback is used. Newly unfrozen source parameters are FP32 during training; they were frozen BF16 in the earlier LoRA-only control. Thus this ablation changes trainability and training precision. Base LLM9 initialization is bound to independently restored canonical FP32 source hashes…

Open weights openpi

[View model](https://savrn.com/models/pi05-libero-source-free-common-llm-action-lr01-view-large-1000-20261011)

Model

### [pi05-libero-spatial-25traj-6epochs-noeval-seed726](https://savrn.com/models/pi05-libero-spatial-25traj-6epochs-noeval-seed726)

[Rahul Chand](https://savrn.com/model-publishers/chand0320)

Open weights

[View model](https://savrn.com/models/pi05-libero-spatial-25traj-6epochs-noeval-seed726)

Model

### [pi05-libero-spatial-50traj-8epochs-frozen-vlm](https://savrn.com/models/pi05-libero-spatial-50traj-8epochs-frozen-vlm)

[Rahul Chand](https://savrn.com/model-publishers/chand0320)

Open weights

[View model](https://savrn.com/models/pi05-libero-spatial-50traj-8epochs-frozen-vlm)

Model · Robotics

### [pi05-libero-support-orthogonal-delta-full-il-source-n50000-10000-20261017](https://savrn.com/models/pi05-libero-support-orthogonal-delta-full-il-source-n50000-10000-20261017)

[Donghyun Shin](https://savrn.com/model-publishers/donghyun1228)

50k support-near random-probe frames,10000 updates,batch32,seed42,LR1e-5,EMA off. LLM frozen. Source IL trains block32 proper rotations and full non-LLM native weights; IDM bypasses rotations and trains full non-LLM native weights. Targets restore this new source,freeze rotations/anchors and exclude gripper6 from IDM loss. Source warmup100,target warmup500. Replay at0/every1000 uses fixed expert labels only for diagnostics. Top-level checkpoint retains raw weights,anchors,rotations and full optimizer for resume; policy/ contains materialized inference weights and normalization. Target policy wrist is the new materialized source wrist. Source has no simulator success claim. Dataset…

Open weights apache-2.0 openpi

[View model](https://savrn.com/models/pi05-libero-support-orthogonal-delta-full-il-source-n50000-10000-20261017)

Model

### [pi05-libero-support-unified-v1a1-view-large-n100000-10000-20261016](https://savrn.com/models/pi05-libero-support-unified-v1a1-view-large-n100000-10000-20261016)

[Donghyun Shin](https://savrn.com/model-publishers/donghyun1228)

Open weights

[View model](https://savrn.com/models/pi05-libero-support-unified-v1a1-view-large-n100000-10000-20261016)

Model · Robotics

### [pi05-libero-support-unified-v1a1-view-large-n300000-1000-20261016](https://savrn.com/models/pi05-libero-support-unified-v1a1-view-large-n300000-1000-20261016)

[Donghyun Shin](https://savrn.com/model-publishers/donghyun1228)

Fresh matched source Donghyun1228/pi05-libero-support-probe-source-n300000-10000-20261015 @ 2e35994a62b96d29398b7ef137f915afe3d4a40a. Dataset Donghyun1228/libero-support-random-waypoint-300k-20261015 @ 32ed215baf01af7e82f927a62b108873aa70bd19. Only the nine base-LLM leaves are frozen. Vision23, full expert11 and action/time projections8 train with the same native AdamW update multiplier1. B32, seed42, LR1e-5, warmup500, 5000 total updates. Gripper coordinate6 is excluded from target IDM loss (mean of remaining31 padded coordinates); seven-dimensional inputs/output and gripper control remain. Original normalization/source wrist preserved. Raw non-EMA step999; BF16 inference. Evaluation…

Open weights apache-2.0 openpi

[View model](https://savrn.com/models/pi05-libero-support-unified-v1a1-view-large-n300000-1000-20261016)

Model

### [pi05-libero-support-unified-v1a1-view-large-n300000-10000-20261016](https://savrn.com/models/pi05-libero-support-unified-v1a1-view-large-n300000-10000-20261016)

[Donghyun Shin](https://savrn.com/model-publishers/donghyun1228)

Open weights

[View model](https://savrn.com/models/pi05-libero-support-unified-v1a1-view-large-n300000-10000-20261016)

Model · Robotics

### [pi05-libero-support-unified-v1a1-view-large-n300000-3000-20261016](https://savrn.com/models/pi05-libero-support-unified-v1a1-view-large-n300000-3000-20261016)

[Donghyun Shin](https://savrn.com/model-publishers/donghyun1228)

Fresh matched source Donghyun1228/pi05-libero-support-probe-source-n300000-10000-20261015 @ 2e35994a62b96d29398b7ef137f915afe3d4a40a. Dataset Donghyun1228/libero-support-random-waypoint-300k-20261015 @ 32ed215baf01af7e82f927a62b108873aa70bd19. Only the nine base-LLM leaves are frozen. Vision23, full expert11 and action/time projections8 train with the same native AdamW update multiplier1. B32, seed42, LR1e-5, warmup500, 5000 total updates. Gripper coordinate6 is excluded from target IDM loss (mean of remaining31 padded coordinates); seven-dimensional inputs/output and gripper control remain. Original normalization/source wrist preserved. Raw non-EMA step2999; BF16 inference. Evaluation…

Open weights apache-2.0 openpi

[View model](https://savrn.com/models/pi05-libero-support-unified-v1a1-view-large-n300000-3000-20261016)

Model

### [pi05-libero-support-unified-v1a1-view-large-n50000-10000-20261016](https://savrn.com/models/pi05-libero-support-unified-v1a1-view-large-n50000-10000-20261016)

[Donghyun Shin](https://savrn.com/model-publishers/donghyun1228)

Open weights

[View model](https://savrn.com/models/pi05-libero-support-unified-v1a1-view-large-n50000-10000-20261016)

Model

### [pi05-libero-support-unified-v1a1-yaw30-n100000-10000-20261016](https://savrn.com/models/pi05-libero-support-unified-v1a1-yaw30-n100000-10000-20261016)

[Donghyun Shin](https://savrn.com/model-publishers/donghyun1228)

Open weights

[View model](https://savrn.com/models/pi05-libero-support-unified-v1a1-yaw30-n100000-10000-20261016)

Model

### [pi05-libero-support-unified-v1a1-yaw30-n300000-10000-20261016](https://savrn.com/models/pi05-libero-support-unified-v1a1-yaw30-n300000-10000-20261016)

[Donghyun Shin](https://savrn.com/model-publishers/donghyun1228)

Open weights

[View model](https://savrn.com/models/pi05-libero-support-unified-v1a1-yaw30-n300000-10000-20261016)

Model

### [pi05-libero-support-unified-v1a1-yaw30-n50000-10000-20261016](https://savrn.com/models/pi05-libero-support-unified-v1a1-yaw30-n50000-10000-20261016)

[Donghyun Shin](https://savrn.com/model-publishers/donghyun1228)

Open weights

[View model](https://savrn.com/models/pi05-libero-support-unified-v1a1-yaw30-n50000-10000-20261016)

Model

### [pi05-marvin-openpi-baseline](https://savrn.com/models/pi05-marvin-openpi-baseline)

[Preload](https://savrn.com/model-publishers/preload-bionics)

Open weights

[View model](https://savrn.com/models/pi05-marvin-openpi-baseline)

Model

### [pi05-marvin-pinch-home-stock-4k](https://savrn.com/models/pi05-marvin-pinch-home-stock-4k)

[Preload](https://savrn.com/model-publishers/preload-bionics)

Open weights

[View model](https://savrn.com/models/pi05-marvin-pinch-home-stock-4k)

Model

### [pi05-negmesh243-drop13-ctc502-h50-bs64-30k-h100x2](https://savrn.com/models/pi05-negmesh243-drop13-ctc502-h50-bs64-30k-h100x2)

[HI](https://savrn.com/model-publishers/ronaldo-goat)

Open weights

[View model](https://savrn.com/models/pi05-negmesh243-drop13-ctc502-h50-bs64-30k-h100x2)

Model · Robotics

### [pi05-plug_adaptive_3](https://savrn.com/models/pi05-plug-adaptive-3)

[Lucazanett](https://savrn.com/model-publishers/lucazanett)

Open weights apache-2.0 4.1B parameters lerobot

[View model](https://savrn.com/models/pi05-plug-adaptive-3)

Model · Robotics

### [pi05-real-workbench-preset-3view-layout-reconstruction-60k](https://savrn.com/models/pi05-real-workbench-preset-3view-layout-reconstruction-60k)

[Jaehyun Kang](https://savrn.com/model-publishers/jaehyunkang)

Final checkpoint after 60,000 optimization steps. This is a trained policy, not an evaluation result. Policy weights, policy configuration, preprocessing/postprocessing and normalization states are at the repository root. Training resume files are in trainingstate/. Host-specific paths were removed from JSON metadata; supply local dataset/output paths when resuming. The tokenizer reference points to google/paligemma-3b-pt-224 (training revision 35e4f46485b4d07967e7e9935bc3786aad50687c). For subtask models, supply the corresponding per-frame subtask as the policy task text. For 3-view models also supply the dataset-defined keyframe image. No real-robot evaluation metrics are claimed here.…

Open weights 4.1B parameters lerobot

[View model](https://savrn.com/models/pi05-real-workbench-preset-3view-layout-reconstruction-60k)

Model · Robotics

### [pi05-real-workbench-preset-3view-object-identification-60k](https://savrn.com/models/pi05-real-workbench-preset-3view-object-identification-60k)

[Jaehyun Kang](https://savrn.com/model-publishers/jaehyunkang)

Final checkpoint after 60,000 optimization steps. This is a trained policy, not an evaluation result. Policy weights, policy configuration, preprocessing/postprocessing and normalization states are at the repository root. Training resume files are in trainingstate/. Host-specific paths were removed from JSON metadata; supply local dataset/output paths when resuming. The tokenizer reference points to google/paligemma-3b-pt-224 (training revision 35e4f46485b4d07967e7e9935bc3786aad50687c). For subtask models, supply the corresponding per-frame subtask as the policy task text. For 3-view models also supply the dataset-defined keyframe image. No real-robot evaluation metrics are claimed here.…

Open weights 4.1B parameters lerobot

[View model](https://savrn.com/models/pi05-real-workbench-preset-3view-object-identification-60k)

Model

### [pi05-robot-checkpoints-20260912](https://savrn.com/models/pi05-robot-checkpoints-20260912)

[Minjae Oh](https://savrn.com/model-publishers/riasok)

Public research archive of multiple pi0.5 experiment families. PPO+CSD is intentionally excluded. Checkpoint names denote local run iterations; BC iteration counts are not necessarily optimizer-step-equivalent to PPO. Each checkpoint contains actor/modelstatedict/fullweights.pt for the RLinf model loader and distributed actor/dcpcheckpoint training state. These are not standalone Transformers or LeRobot exports. LoRA checkpoints include the full model state, not adapter-only exports. Loading requires the matching RLinf/OpenPI code, configuration, base assets and normalization statistics. Resume support depends on the runner; these files do not imply exact environment/RNG replay. See…

Open weights other

[View model](https://savrn.com/models/pi05-robot-checkpoints-20260912)

Model · Robotics

### [pi05-robotwin](https://savrn.com/models/pi05-robotwin)

[Nie](https://savrn.com/model-publishers/niejunnan25)

Task-specific π₀.₅ checkpoints used in PACE: Phase-Aware Chunk Execution for Robot Policies with Action Chunking (paper). Each task has its own model and normalization statistics. PACE is a training-free execution method: Fixed-horizon, PACE and the adaptive baselines in the paper use the same task-specific model weights. PACE calibration results and evaluation code are distributed with the companion codebase. Available checkpoints: 47 of 50. The complete available-task list is in manifest.json. Select a task using its standard RoboTwin name. A single task is approximately 12.4 GB; the 47 available tasks total approximately 584.8 GB. manifest.json lists every task, its OpenPI configuration…

Open weights other

[View model](https://savrn.com/models/pi05-robotwin)

Model

### [pi05-so101-pickvials-2dcam-cotrain-30fps](https://savrn.com/models/pi05-so101-pickvials-2dcam-cotrain-30fps)

[Jackson Chang](https://savrn.com/model-publishers/jackson-013)

Open weights

[View model](https://savrn.com/models/pi05-so101-pickvials-2dcam-cotrain-30fps)

Model

### [pi05-so101-pickvials-2dcam-joint-task-prompt-cotrain](https://savrn.com/models/pi05-so101-pickvials-2dcam-joint-task-prompt-cotrain)

[Jackson Chang](https://savrn.com/model-publishers/jackson-013)

Open weights

[View model](https://savrn.com/models/pi05-so101-pickvials-2dcam-joint-task-prompt-cotrain)

## Model Collections

Hand-picked starting points, each with the reason it exists.

Collection · 4 entries

### [Embedding models for retrieval](https://savrn.com/collections/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.

[View collection](https://savrn.com/collections/embedding-models-for-retrieval)

Collection · 4 entries

### [Models that fit on one accelerator](https://savrn.com/collections/models-that-run-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.

[View collection](https://savrn.com/collections/models-that-run-on-one-accelerator)

Collection · 6 entries

### [Open-weight text models worth knowing](https://savrn.com/collections/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.

[View collection](https://savrn.com/collections/open-weight-text-models-worth-knowing)

Collection · 3 entries

### [Speech and audio models](https://savrn.com/collections/speech-and-audio)

Recognition and synthesis models, grouped so the two directions are easy to compare.

[View collection](https://savrn.com/collections/speech-and-audio)

## 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](https://savrn.com/ai-index/pricing/open-models)

The same open-weight model priced by every host that serves it, per million tokens.

Research Hub

### [Data center trackers and maps](https://savrn.com/data-center-research)

Moratoriums, permits, power, water and capital behind the facilities that run these models.

Method

### [How the Model Hub is built](https://savrn.com/model-hub/methodology)

Sources, evidence labels, refresh behaviour, and the limits of every comparison here.