# Models, Page 326 of 489
Source: https://savrn.com/models/page/326
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

### [ffmpeg-nl2cmd-2B](https://savrn.com/models/ffmpeg-nl2cmd-2b)

[Joe Maddalone](https://savrn.com/model-publishers/joemaddalone)

Open weights

[View model](https://savrn.com/models/ffmpeg-nl2cmd-2b)

Model

### [fgt-frame-model-runs](https://savrn.com/models/fgt-frame-model-runs)

[TAIYU HOMMA](https://savrn.com/model-publishers/hmwri)

Open weights

[View model](https://savrn.com/models/fgt-frame-model-runs)

Model · Text generation

### [fhe-ssm-pbd430a](https://savrn.com/models/fhe-ssm-pbd430a)

[Alexander Ananyev](https://savrn.com/model-publishers/xelananv)

This model is served in https://github.com/xelananv/fhe-ssm: it is a 404,715,520 parameter recurrent language model whose every (!) nonlinearity is a low-degree polynomial with trained coeffs, so that the CKKS encrypted evaluation computes precisely the model's own float64 arithmetic without any post-hoc approximation of softmax or GELU. It succesfully generated text for 64 prompts at once under encryption, autoregressively, for 148 consecutive steps (=> 9,472 tokens) at 128 bit classical parameters, matching its own plaintext top-1 choice 99.7 % of the time. Those are properties of the circuit; this card is about the model, which is small and was trained on a few hundred dollars of…

Open weights mit

[View model](https://savrn.com/models/fhe-ssm-pbd430a)

Model

### [fiber_single_small](https://savrn.com/models/fiber-single-small)

[Vesuvius Challenge](https://savrn.com/model-publishers/scrollprize)

Open weights

[View model](https://savrn.com/models/fiber-single-small)

Model

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

[Computational Metabolomics Research Group](https://savrn.com/model-publishers/computational-metabolomics)

Yuhui Hong and FIDDLE contributors. FIDDLE v2.0.0 Orbitrap prediction and rescoring checkpoints. Copyright 2024 Yuhui Hong. Upstream release. Apache-2.0 follows the release's project licence; no separate checkpoint-specific licence statement accompanies the archives. The unchanged upstream licence notice is included as 2.0.0/LICENSE. The software license is reported separately; it does not replace the model/data license. These are byte-identical upstream files. No model conversion was performed.

Open weights apache-2.0

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

Model

### [fin-laya](https://savrn.com/models/fin-laya)

[Li](https://savrn.com/model-publishers/ya9919)

Open weights other 421M parameters

[View model](https://savrn.com/models/fin-laya)

Model

### [fin-laya-3-rounds](https://savrn.com/models/fin-laya-3-rounds)

[Li](https://savrn.com/model-publishers/ya9919)

Open weights other 421M parameters

[View model](https://savrn.com/models/fin-laya-3-rounds)

Model

### [fin-laya-6-rounds](https://savrn.com/models/fin-laya-6-rounds)

[Li](https://savrn.com/model-publishers/ya9919)

English financial decision model, approximately 0.42B parameters. This card describes the 6-rounds release (previously labeled four-source). Training data cover CHFS, CGSS, CFPS and CLDS. Macro F1 averages over all candidate classes within each supported question version, then equally over versions. 8,885 input tokens/s, 26.75 ms mean latency, 37.38 decisions/s. Measured on one A100 40GB with FP16, batch and concurrency one, the same three requests, three warmups and 20 timed calls per request; input throughput uses a shared tokenizer. 本页为 6-rounds 训练版本，旧标签为 four-source；数据来源为 CHFS、CGSS、CFPS、CLDS。3-rounds 训练版本使用独立分支模型卡与权重。按训练轮次命名，数据来源单独说明。

Open weights apache-2.0 421M parameters laya

[View model](https://savrn.com/models/fin-laya-6-rounds)

Model

### [fin-laya-mlx-3-rounds](https://savrn.com/models/fin-laya-mlx-3-rounds)

[Li](https://savrn.com/model-publishers/ya9919)

Open weights other 421M parameters

[View model](https://savrn.com/models/fin-laya-mlx-3-rounds)

Model

### [fin-laya-multilingual](https://savrn.com/models/fin-laya-multilingual)

[Li](https://savrn.com/model-publishers/ya9919)

Open weights other 322M parameters

[View model](https://savrn.com/models/fin-laya-multilingual)

Model

### [fin-laya-multilingual-mlx](https://savrn.com/models/fin-laya-multilingual-mlx)

[Li](https://savrn.com/model-publishers/ya9919)

Open weights other 322M parameters

[View model](https://savrn.com/models/fin-laya-multilingual-mlx)

Model

### [finai-slm-v2](https://savrn.com/models/finai-slm-v2)

[Finndot India Pvt Ltd](https://savrn.com/model-publishers/finndot)

A 1.7B on-device language model for Indian bank SMS parsing and personal finance coaching. FinAI SLM is a Qwen/Qwen3-1.7B derivative fine-tuned with QLoRA SFT for the FinnDot expense tracker app. It runs entirely on-device — no cloud calls, no user data leaves the phone. The model handles English, Hindi, Hinglish, Tamil, Telugu, Marathi, and Bengali bank SMS. v1 → v2: R-EM +5.2 pp, chat groundedness +36 pp, false-parse 28.57% → 0.54%. No user SMS. No real financial data. Fully synthetic. Split hygiene: assignment unit is (bank, templateid), not individual SMS. All variants of a template stay in one split. See ml/data/manifest/SHA256SUMS. Pre-registered before looking at test scores…

Open weights apache-2.0 transformers

[View model](https://savrn.com/models/finai-slm-v2)

Model

### [final-Q3](https://savrn.com/models/final-q3)

[Ploykerd](https://savrn.com/model-publishers/kochnattapat)

Open weights 124M parameters

[View model](https://savrn.com/models/final-q3)

Model · Robotics

### [final_demo_pick_nut_on_scale_20260918_172246_act-policy-v1](https://savrn.com/models/final-demo-pick-nut-on-scale-20260918-172246-act-policy-v1)

[Makino Yasuhiro](https://savrn.com/model-publishers/makinokf)

Action Chunking with Transformers (ACT) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot act guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning…

Open weights apache-2.0 52M parameters lerobot

[View model](https://savrn.com/models/final-demo-pick-nut-on-scale-20260918-172246-act-policy-v1)

Model · Text classification

### [financial-obligation-baseline](https://savrn.com/models/financial-obligation-baseline)

[Josiahwilson Bit](https://savrn.com/model-publishers/josiahwilson-bit)

An open, auditable classifier that reads a contract sentence and identifies its financial obligations — payment terms, renewal pricing, liquidated damages, liability caps, termination costs, audit rights, payment security. Every prediction is a lead, not a verdict: the model assists human review; it does not give legal advice and does not decide anything. categories (F1, F3–F8; see the taxonomy). F2 (late fees & interest) has no training data and is not covered. one-vs-rest. Small, deterministic, CPU-only, fully reproducible. or confidential contracts. vectorizer, classifier, label binarizer). - Eval set is small (56); confidence intervals are wide. - Train and eval come from the same CUAD…

Open weights apache-2.0

[View model](https://savrn.com/models/financial-obligation-baseline)

Model

### [finbert-GGUF](https://savrn.com/models/finbert-gguf)

[Team Mradermacher](https://savrn.com/model-publishers/mradermacher)

static quants of https://huggingface.co/ProsusAI/finbert 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 quant And here…

Open weights cc-by-nc-sa-4.0 transformers

[View model](https://savrn.com/models/finbert-gguf)

Model · Text classification

### [finbert-crypto-stock-news-sentiment](https://savrn.com/models/finbert-crypto-stock-news-sentiment)

[David Kurniawan](https://savrn.com/model-publishers/huangyiyang)

Classifies a news headline as positive, negative or neutral for holders of the asset it is filed under. Fine-tuned from ProsusAI/finbert on headlines filed under Bitcoin, gold (the GLD fund) and US stocks (the SPY fund). It describes the tone of the news. It does not forecast prices. Scored once on a held-out test set of 854 headlines from 2026-04-01 to 2026-10-14, after every choice had been made on validation data. The test set holds more positive and negative headlines than the news feed does, so the figures are shown as drawn and also re-weighted to the feed's natural mix. McNemar's exact test against the base model: p = 9e-77. On headlines where both the label and the prediction are…

Open weights apache-2.0 110M parameters 512 tokens transformers

[View model](https://savrn.com/models/finbert-crypto-stock-news-sentiment)

Model

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

[Andrew P Thomasson](https://savrn.com/model-publishers/drewthomasson)

Open weights apache-2.0

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

Model

### [fine_tune_e2e](https://savrn.com/models/fine-tune-e2e)

[Ryoma Hata](https://savrn.com/model-publishers/shoca2)

This model is a fine-tuned version of mistralai/Mistral-7B-v0.3. It has been trained using TRL. This model was trained with SFT.

Open weights transformers

[View model](https://savrn.com/models/fine-tune-e2e)

Model · Zero-shot classification

### [finecat-nli-ultra-l](https://savrn.com/models/finecat-nli-ultra-l)

[Lee Miller](https://savrn.com/model-publishers/dleemiller)

This model is a fine-tune of dmis-lab/UltraBERT-380M, trained on the dleemiller/FineCat-NLI-Reason dataset, a compilation of several high-quality NLI data sources with quality screening and reduction of easy samples in the training split. Also includes additional NLI, fact verification and long document sources (2.05M pairs in total). The training uses logit distillation from an NLI finetune of Qwen3.8-27B. \begin{equation} \mathcal{L} = \alpha \cdot \mathcal{L}{\text{CE}}(z^{(s)}, y) + \beta \cdot T^2 \cdot \mathrm{KL}\left(\sigma(z^{(t)}/T) \,\|\, \sigma(z^{(s)}/T)\right) \end{equation} where \\(z^{(s)}\\) and \\(z^{(t)}\\) are the student and teacher logits, \\(y\\) are the ground truth…

Open weights apache-2.0 378M parameters sentence-transformers

[View model](https://savrn.com/models/finecat-nli-ultra-l)

Model

### [finer-sql-1_5b-coredb-cb821adb-ckpt](https://savrn.com/models/finer-sql-1-5b-coredb-cb821adb-ckpt)

[Thanh Dat Hoang](https://savrn.com/model-publishers/thanhdath)

Access requested at publisher

[View model](https://savrn.com/models/finer-sql-1-5b-coredb-cb821adb-ckpt)

Model

### [finer-sql-1_5b-origdb-ckpt](https://savrn.com/models/finer-sql-1-5b-origdb-ckpt)

[Thanh Dat Hoang](https://savrn.com/model-publishers/thanhdath)

Base: griffith-bigdata/Qwen-2.5-Coder-1.5B-SQL-Writer, GRPO checkpoints from ICT job 98674 (s2986280, gpuq2). Reward tree: ORIGINAL BIRD+Spider training DBs (fingerprint 69343e76e07bbbd9aa8c0d1d37ae940e, 69 BIRD + 169 Spider DBs), not the cb821adb CoRE-DB tree -- this is the origdb control arm paired with thanhdath/finer-sql-15b-coredb-cb821adb-ckpt. Corpus: v3 (grposqlwriterorigdatajointoldpromptfullfixed, 18,087 rows), MAXSTEPS=9312, --maxcompletionlength 2048 (locked), GT cache sha256 618a9aa45d4c2a6e2f355900e3fa15280d6e9facfed87a370d1193cdafd0cb72.

Access requested at publisher other

[View model](https://savrn.com/models/finer-sql-1-5b-origdb-ckpt)

Model

### [finer-sql-3b-coredb-0388ce2c-ckpt](https://savrn.com/models/finer-sql-3b-coredb-0388ce2c-ckpt)

[Thanh Dat Hoang](https://savrn.com/model-publishers/thanhdath)

Access requested at publisher

[View model](https://savrn.com/models/finer-sql-3b-coredb-0388ce2c-ckpt)

Model

### [finetune-restyle-psp-skin-lesion-ham10000](https://savrn.com/models/finetune-restyle-psp-skin-lesion-ham10000)

[DACNTT&KLTN](https://savrn.com/model-publishers/dacntt-kltn)

Open weights

[View model](https://savrn.com/models/finetune-restyle-psp-skin-lesion-ham10000)

## 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.