# Models, Page 344 of 489
Source: https://savrn.com/models/page/344
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.

11,734Models

3,323Datasets

288Papers

7,150Publishers

12,282Sourced relationships

Updated 2026-10-20 · [How the library is built](https://savrn.com/model-hub/methodology)

## Filter Models

Task

- [Text generation1,441](https://savrn.com/models?task=Text+generation)
- [Image and text to text530](https://savrn.com/models?task=Image+and+text+to+text)
- [Feature extraction351](https://savrn.com/models?task=Feature+extraction)
- [Robotics345](https://savrn.com/models?task=Robotics)
- [Text classification310](https://savrn.com/models?task=Text+classification)
- [Text to image222](https://savrn.com/models?task=Text+to+image)
- [Speech recognition175](https://savrn.com/models?task=Speech+recognition)
- [Audio classification158](https://savrn.com/models?task=Audio+classification)
- [Image classification138](https://savrn.com/models?task=Image+classification)
- [Text to speech137](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 learning95](https://savrn.com/models?task=Reinforcement+learning)
- [Zero-shot classification93](https://savrn.com/models?task=Zero-shot+classification)
- [Image segmentation89](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)
- [Translation79](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)
- [Image to text59](https://savrn.com/models?task=Image+to+text)
- [Video classification59](https://savrn.com/models?task=Video+classification)
- [Text to video55](https://savrn.com/models?task=Text+to+video)

Library

- [transformers2,829](https://savrn.com/models?library=transformers)
- [peft355](https://savrn.com/models?library=peft)
- [diffusers196](https://savrn.com/models?library=diffusers)
- [pytorch184](https://savrn.com/models?library=pytorch)
- [gguf172](https://savrn.com/models?library=gguf)
- [mlx145](https://savrn.com/models?library=mlx)
- [lerobot141](https://savrn.com/models?library=lerobot)
- [sentence-transformers139](https://savrn.com/models?library=sentence-transformers)
- [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)
- [onnx39](https://savrn.com/models?library=onnx)
- [vllm38](https://savrn.com/models?library=vllm)
- [diffusion-single-file28](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)
- [openvino20](https://savrn.com/models?library=openvino)
- [exllamav318](https://savrn.com/models?library=exllamav3)
- [coreml17](https://savrn.com/models?library=coreml)
- [litert16](https://savrn.com/models?library=litert)
- [openpi16](https://savrn.com/models?library=openpi)
- [rkllm15](https://savrn.com/models?library=rkllm)

Modality

- [Other6,664](https://savrn.com/models?modality=Other)
- [Text2,658](https://savrn.com/models?modality=Text)
- [Image and text589](https://savrn.com/models?modality=Image+and+text)
- [Image582](https://savrn.com/models?modality=Image)
- [Audio488](https://savrn.com/models?modality=Audio)
- [Control440](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-2.03,175](https://savrn.com/models?license=apache-2.0)
- [mit1,275](https://savrn.com/models?license=mit)
- [other723](https://savrn.com/models?license=other)
- [cc-by-nc-4.0200](https://savrn.com/models?license=cc-by-nc-4.0)
- [cc-by-4.0153](https://savrn.com/models?license=cc-by-4.0)
- [bsd-3-clause107](https://savrn.com/models?license=bsd-3-clause)
- [gemma63](https://savrn.com/models?license=gemma)
- [cc-by-nc-sa-4.050](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)
- [unknown29](https://savrn.com/models?license=unknown)
- [llama3.128](https://savrn.com/models?license=llama3.1)
- [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-m13](https://savrn.com/models?license=creativeml-openrail-m)
- [llama28](https://savrn.com/models?license=llama2)
- [afl-3.07](https://savrn.com/models?license=afl-3.0)
- [osl-3.06](https://savrn.com/models?license=osl-3.0)
- [cc0-1.04](https://savrn.com/models?license=cc0-1.0)

Access

- [Open weights11,363](https://savrn.com/models?access=open_access)
- [Access requested at publisher371](https://savrn.com/models?access=gated)

Publisher

- [Team Mradermacher304](https://savrn.com/models?publisher=mradermacher)
- [Yurun Yuan101](https://savrn.com/models?publisher=ryanyr)
- [RunningHub.ai100](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 Pu71](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)
- [Yurun Yuan55](https://savrn.com/models?publisher=yuruny)
- [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805)
- [Wang49](https://savrn.com/models?publisher=davidwdw)
- [Microsoft48](https://savrn.com/models?publisher=microsoft)
- [BAAR46](https://savrn.com/models?publisher=aixk)
- [NVIDIA46](https://savrn.com/models?publisher=nvidia)
- [Khazzz1c44](https://savrn.com/models?publisher=khazic)
- [Quan44](https://savrn.com/models?publisher=wq2012)
- [Yurun Yuan44](https://savrn.com/models?publisher=yyuan244)

[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)

11,734 models, sorted by most downloaded.

Model

### [grouptok-en-sw-af-de-fr-es-zh-152k](https://savrn.com/models/grouptok-en-sw-af-de-fr-es-zh-152k)

[Henry Hale](https://savrn.com/model-publishers/henryhale)

A byte-level BPE tokenizer of 151,665 tokens for English, Swahili, Afrikaans, German, French, Spanish and Chinese, whose vocabulary is grouped by translation with grouptok: a group gathers tokens that translate each other, across languages (e.g. water, Wasser, eau, agua, maji). Its tokenizer.json is exactly that of the baseline bpe-en-sw-af-de-fr-es-zh-152k, so the two split every text identically and differ only in groups.json. No pad, bos or eos token is declared; set them for your model if you need them. Each sentence pair is split by the BPE, and its language's aligner links the English subwords to the other side's. The links of all pairs and all languages are pooled and turned into…

Open weights apache-2.0 tokenizers

[View model](https://savrn.com/models/grouptok-en-sw-af-de-fr-es-zh-152k)

Model

### [grouptok-en-sw-af-de-fr-es-zh-200k](https://savrn.com/models/grouptok-en-sw-af-de-fr-es-zh-200k)

[Henry Hale](https://savrn.com/model-publishers/henryhale)

A byte-level BPE tokenizer of 200,000 tokens for English, Swahili, Afrikaans, German, French, Spanish and Chinese, whose vocabulary is grouped by translation with grouptok: a group gathers tokens that translate each other, across languages (e.g. water, Wasser, eau, agua, maji). Its tokenizer.json is exactly that of the baseline bpe-en-sw-af-de-fr-es-zh-200k, so the two split every text identically and differ only in groups.json. No pad, bos or eos token is declared; set them for your model if you need them. Each sentence pair is split by the BPE, and its language's aligner links the English subwords to the other side's. The links of all pairs and all languages are pooled and turned into…

Open weights apache-2.0 tokenizers

[View model](https://savrn.com/models/grouptok-en-sw-af-de-fr-es-zh-200k)

Model

### [grpo-5g-rca-qwen3-1.7b](https://savrn.com/models/grpo-5g-rca-qwen3-1-7b)

[Mark Anthony](https://savrn.com/model-publishers/jesterbeanltea)

This model is a fine-tuned version of Qwen/Qwen3-1.7B. It has been trained using TRL. This model was trained with GRPO, a method introduced in DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Open weights transformers

[View model](https://savrn.com/models/grpo-5g-rca-qwen3-1-7b)

Model

### [grpo-qwen3-1.7b-nemotron-leetcode-clean-3.2k-bs32-n8-verl091-epoch2-146102-step_190](https://savrn.com/models/grpo-qwen3-1-7b-nemotron-leetcode-clean-3-2k-bs32-n8-verl091-epoch2-146102-step-190)

[Jiahao Zhang](https://savrn.com/model-publishers/hi-todayis-jh)

Open weights

[View model](https://savrn.com/models/grpo-qwen3-1-7b-nemotron-leetcode-clean-3-2k-bs32-n8-verl091-epoch2-146102-step-190)

Model · Text generation

### [grpo-qwen3-1.7b-nonthinking-entropy-2048-20260918](https://savrn.com/models/grpo-qwen3-1-7b-nonthinking-entropy-2048-20260918)

[Kumar](https://savrn.com/model-publishers/sandeep123)

GRPO with positive-advantage-gated sampled entropy regularization; exact equation and provenance are recorded in baseline metadata. Training explicitly renders enablethinking=False, recorded as thinkingmode: false in the scientific contract. The pinned tokenizer and chat template remain unchanged. Use the same explicit keyword at inference, particularly for Qwen3-1.7B, whose default template enables thinking. Every checkpoint in this repository requires explicit nonthinking provenance. This public repository retains every published optimizer-update adapter, including update zero (the initial, untrained adapter). Training is planned for 4 epochs on the same 2,048-question training split as…

Open weights peft

[View model](https://savrn.com/models/grpo-qwen3-1-7b-nonthinking-entropy-2048-20260918)

Model · Text generation

### [grpo-qwen3-1.7b-nonthinking-likelihood-2048-20260918](https://savrn.com/models/grpo-qwen3-1-7b-nonthinking-likelihood-2048-20260918)

[Kumar](https://savrn.com/model-publishers/sandeep123)

GRPO with a detached correct-response likelihood-rank reward; exact equation and provenance are recorded in baseline metadata. Training explicitly renders enablethinking=False, recorded as thinkingmode: false in the scientific contract. The pinned tokenizer and chat template remain unchanged. Use the same explicit keyword at inference, particularly for Qwen3-1.7B, whose default template enables thinking. Every checkpoint in this repository requires explicit nonthinking provenance. This public repository retains every published optimizer-update adapter, including update zero (the initial, untrained adapter). Training is planned for 4 epochs on the same 2,048-question training split as the…

Open weights peft

[View model](https://savrn.com/models/grpo-qwen3-1-7b-nonthinking-likelihood-2048-20260918)

Model · Text generation

### [grpo-qwen3-4b-nonthinking-entropy-2048-20260918](https://savrn.com/models/grpo-qwen3-4b-nonthinking-entropy-2048-20260918)

[Kumar](https://savrn.com/model-publishers/sandeep123)

GRPO with positive-advantage-gated sampled entropy regularization; exact equation and provenance are recorded in baseline metadata. Training explicitly renders enablethinking=False, recorded as thinkingmode: false in the scientific contract. The pinned tokenizer and chat template remain unchanged. Use the same explicit keyword at inference, particularly for Qwen3-1.7B, whose default template enables thinking. Every checkpoint in this repository requires explicit nonthinking provenance. This public repository retains every published optimizer-update adapter, including update zero (the initial, untrained adapter). Training is planned for 4 epochs on the same 2,048-question training split as…

Open weights peft

[View model](https://savrn.com/models/grpo-qwen3-4b-nonthinking-entropy-2048-20260918)

Model · Text generation

### [grpo-qwen3-4b-nonthinking-likelihood-2048-20260918](https://savrn.com/models/grpo-qwen3-4b-nonthinking-likelihood-2048-20260918)

[Kumar](https://savrn.com/model-publishers/sandeep123)

GRPO with a detached correct-response likelihood-rank reward; exact equation and provenance are recorded in baseline metadata. Training explicitly renders enablethinking=False, recorded as thinkingmode: false in the scientific contract. The pinned tokenizer and chat template remain unchanged. Use the same explicit keyword at inference, particularly for Qwen3-1.7B, whose default template enables thinking. Every checkpoint in this repository requires explicit nonthinking provenance. This public repository retains every published optimizer-update adapter, including update zero (the initial, untrained adapter). Training is planned for 4 epochs on the same 2,048-question training split as the…

Open weights peft

[View model](https://savrn.com/models/grpo-qwen3-4b-nonthinking-likelihood-2048-20260918)

Model

### [grpo-qwen3.5-9b-seven-task](https://savrn.com/models/grpo-qwen3-5-9b-seven-task)

[Cyrus Kwan](https://savrn.com/model-publishers/wckwan)

Qwen/Qwen3.5-9B trained with grpo on the seven-task setting: 100 steps, 32 prompts x 8 rollouts per step, lr 2e-6, KL 1e-3, 32K rollout cap, verl v0.9.1 (FSDP2 + vLLM). Training data: 512 examples (seed 0) from the official training splits; system prompt "Solve the user's task and give the final answer directly."; reward = task correctness of the text after.

Open weights apache-2.0 9.4B parameters 262,144 tokens

[View model](https://savrn.com/models/grpo-qwen3-5-9b-seven-task)

Model

### [grpo-tournament-qwen](https://savrn.com/models/grpo-tournament-qwen)

[Siddharth Doshi](https://savrn.com/model-publishers/siddhuuuu)

This model is a fine-tuned version of unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit. It has been trained using TRL. This model was trained with GRPO, a method introduced in DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Open weights transformers

[View model](https://savrn.com/models/grpo-tournament-qwen)

Model

### [grpo_dppo_qwen3_1p7B_base_knk_step_800](https://savrn.com/models/grpo-dppo-qwen3-1p7b-base-knk-step-800)

[Fahim Tajwar](https://savrn.com/model-publishers/ftajwar)

Open weights 1.7B parameters 32,768 tokens

[View model](https://savrn.com/models/grpo-dppo-qwen3-1p7b-base-knk-step-800)

Model

### [grpo_dppo_qwen3_4B_base_knk_step_300](https://savrn.com/models/grpo-dppo-qwen3-4b-base-knk-step-300)

[Fahim Tajwar](https://savrn.com/model-publishers/ftajwar)

Open weights 4B parameters 32,768 tokens

[View model](https://savrn.com/models/grpo-dppo-qwen3-4b-base-knk-step-300)

Model

### [gs-scene-checkpoints](https://savrn.com/models/gs-scene-checkpoints)

[Harshit Aggarwal](https://savrn.com/model-publishers/harshitaggarwal)

Open weights

[View model](https://savrn.com/models/gs-scene-checkpoints)

Model

### [gta5-mods-leaks](https://savrn.com/models/gta5-mods-leaks)

[Missileskywalker](https://savrn.com/model-publishers/missilefx)

Open weights

[View model](https://savrn.com/models/gta5-mods-leaks)

Model · Audio to audio

### [gtcrn-coreai](https://savrn.com/models/gtcrn-coreai)

[Max Farrell](https://savrn.com/model-publishers/maxffarrell)

This release mirrors maxffarrell/gtcrn-coreai at source commit 5a5b0529f89c7146fc196f602ed2635adc681374. Choose DNS3 for general speech enhancement; VCTK-DEMAND is provided for matching that training corpus and reproducing related experiments. Download the complete repository, preserving the.aimodel bundle directories: For deployment you can download only exports/, LICENSE, and THIRDPARTYNOTICES.md using hf download --include. The numerical and latency results below were measured in August 2026 on the specified beta software stack; they have not been remeasured for newer releases. A reproducible, streaming Core AI conversion of GTCRN, the 48.2K-parameter speech-enhancement network…

Open weights mit

[View model](https://savrn.com/models/gtcrn-coreai)

Model

### [gto-full-cc4550ab-245ebf9-20260924-004](https://savrn.com/models/gto-full-cc4550ab-245ebf9-20260924-004)

[Bima Bagaskhoro](https://savrn.com/model-publishers/bimabk)

Open weights 1.2B parameters 128,000 tokens

[View model](https://savrn.com/models/gto-full-cc4550ab-245ebf9-20260924-004)

Model

### [guage](https://savrn.com/models/guage)

[Yan](https://savrn.com/model-publishers/divin1234)

Open weights

[View model](https://savrn.com/models/guage)

Model

### [gun01](https://savrn.com/models/gun01)

[Z](https://savrn.com/model-publishers/mrthor102)

Open weights

[View model](https://savrn.com/models/gun01)

Model

### [gun02](https://savrn.com/models/gun02)

[Z](https://savrn.com/model-publishers/mrthor102)

Open weights

[View model](https://savrn.com/models/gun02)

Model

### [gun02b](https://savrn.com/models/gun02b)

[Z](https://savrn.com/model-publishers/mrthor102)

Open weights

[View model](https://savrn.com/models/gun02b)

Model

### [gun03](https://savrn.com/models/gun03)

[Z](https://savrn.com/model-publishers/mrthor102)

Open weights

[View model](https://savrn.com/models/gun03)

Model

### [gun04](https://savrn.com/models/gun04)

[Z](https://savrn.com/model-publishers/mrthor102)

Open weights

[View model](https://savrn.com/models/gun04)

Model

### [gun05](https://savrn.com/models/gun05)

[Z](https://savrn.com/model-publishers/mrthor102)

Open weights

[View model](https://savrn.com/models/gun05)

Model

### [gun05b](https://savrn.com/models/gun05b)

[Z](https://savrn.com/model-publishers/mrthor102)

Open weights

[View model](https://savrn.com/models/gun05b)

## 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.