# Models, Page 179 of 525
Source: https://savrn.com/models/page/179
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.

12,579Models

3,583Datasets

291Papers

7,578Publishers

12,850Sourced relationships

Updated 2026-10-23 · [How the library is built](https://savrn.com/model-hub/methodology)

## Filter Models

Task

- [Text generation1,548](https://savrn.com/models?task=Text+generation)
- [Image and text to text577](https://savrn.com/models?task=Image+and+text+to+text)
- [Feature extraction398](https://savrn.com/models?task=Feature+extraction)
- [Robotics398](https://savrn.com/models?task=Robotics)
- [Text classification331](https://savrn.com/models?task=Text+classification)
- [Text to image232](https://savrn.com/models?task=Text+to+image)
- [Speech recognition187](https://savrn.com/models?task=Speech+recognition)
- [Text to speech145](https://savrn.com/models?task=Text+to+speech)
- [Image classification139](https://savrn.com/models?task=Image+classification)
- [Audio classification130](https://savrn.com/models?task=Audio+classification)
- [Sentence similarity103](https://savrn.com/models?task=Sentence+similarity)
- [Reinforcement learning100](https://savrn.com/models?task=Reinforcement+learning)
- [Token classification100](https://savrn.com/models?task=Token+classification)
- [Zero-shot classification94](https://savrn.com/models?task=Zero-shot+classification)
- [Image segmentation90](https://savrn.com/models?task=Image+segmentation)
- [Object detection86](https://savrn.com/models?task=Object+detection)
- [Translation80](https://savrn.com/models?task=Translation)
- [Any to any79](https://savrn.com/models?task=Any+to+any)
- [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 classification61](https://savrn.com/models?task=Video+classification)
- [Image to text60](https://savrn.com/models?task=Image+to+text)
- [Text to video55](https://savrn.com/models?task=Text+to+video)

Library

- [transformers3,001](https://savrn.com/models?library=transformers)
- [peft367](https://savrn.com/models?library=peft)
- [diffusers206](https://savrn.com/models?library=diffusers)
- [pytorch198](https://savrn.com/models?library=pytorch)
- [gguf187](https://savrn.com/models?library=gguf)
- [mlx169](https://savrn.com/models?library=mlx)
- [lerobot152](https://savrn.com/models?library=lerobot)
- [sentence-transformers139](https://savrn.com/models?library=sentence-transformers)
- [timm49](https://savrn.com/models?library=timm)
- [llama.cpp46](https://savrn.com/models?library=llama.cpp)
- [transformers.js45](https://savrn.com/models?library=transformers.js)
- [flaxspeaker44](https://savrn.com/models?library=flaxspeaker)
- [diffusion-single-file40](https://savrn.com/models?library=diffusion-single-file)
- [onnx40](https://savrn.com/models?library=onnx)
- [vllm40](https://savrn.com/models?library=vllm)
- [openpi35](https://savrn.com/models?library=openpi)
- [minimax-h328](https://savrn.com/models?library=minimax-h3)
- [ultralytics24](https://savrn.com/models?library=ultralytics)
- [PaddleOCR22](https://savrn.com/models?library=PaddleOCR)
- [coreml22](https://savrn.com/models?library=coreml)
- [exllamav322](https://savrn.com/models?library=exllamav3)
- [openvino20](https://savrn.com/models?library=openvino)
- [litert17](https://savrn.com/models?library=litert)
- [litert-lm16](https://savrn.com/models?library=litert-lm)

Modality

- [Other7,212](https://savrn.com/models?modality=Other)
- [Text2,838](https://savrn.com/models?modality=Text)
- [Image and text637](https://savrn.com/models?modality=Image+and+text)
- [Image598](https://savrn.com/models?modality=Image)
- [Control498](https://savrn.com/models?modality=Control)
- [Audio481](https://savrn.com/models?modality=Audio)
- [Video116](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-2.03,344](https://savrn.com/models?license=apache-2.0)
- [mit1,319](https://savrn.com/models?license=mit)
- [other796](https://savrn.com/models?license=other)
- [cc-by-nc-4.0213](https://savrn.com/models?license=cc-by-nc-4.0)
- [cc-by-4.0164](https://savrn.com/models?license=cc-by-4.0)
- [bsd-3-clause108](https://savrn.com/models?license=bsd-3-clause)
- [gemma63](https://savrn.com/models?license=gemma)
- [cc-by-nc-sa-4.055](https://savrn.com/models?license=cc-by-nc-sa-4.0)
- [llama3.240](https://savrn.com/models?license=llama3.2)
- [unknown33](https://savrn.com/models?license=unknown)
- [agpl-3.032](https://savrn.com/models?license=agpl-3.0)
- [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-3.020](https://savrn.com/models?license=gpl-3.0)
- [openrail++17](https://savrn.com/models?license=openrail%2B%2B)
- [creativeml-openrail-m15](https://savrn.com/models?license=creativeml-openrail-m)
- [openmdw-1.115](https://savrn.com/models?license=openmdw-1.1)
- [cc-by-nc-nd-4.014](https://savrn.com/models?license=cc-by-nc-nd-4.0)
- [llama314](https://savrn.com/models?license=llama3)
- [openrail14](https://savrn.com/models?license=openrail)
- [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 weights12,153](https://savrn.com/models?access=open_access)
- [Access requested at publisher426](https://savrn.com/models?access=gated)

Publisher

- [Team Mradermacher328](https://savrn.com/models?publisher=mradermacher)
- [Yurun Yuan115](https://savrn.com/models?publisher=ryanyr)
- [RunningHub.ai105](https://savrn.com/models?publisher=runninghubai)
- [Qwen91](https://savrn.com/models?publisher=qwen)
- [Jenny Wilson85](https://savrn.com/models?publisher=jenny08311)
- [Jessen Pu77](https://savrn.com/models?publisher=putty77)
- [RKNNAI73](https://savrn.com/models?publisher=rknnai)
- [AI at Meta72](https://savrn.com/models?publisher=facebook)
- [Convergent Intelligence66](https://savrn.com/models?publisher=reaperdoesntknow)
- [Yurun Yuan65](https://savrn.com/models?publisher=yurunyyr)
- [Google64](https://savrn.com/models?publisher=google)
- [Yurun Yuan62](https://savrn.com/models?publisher=yuruny)
- [Firza Hadzami61](https://savrn.com/models?publisher=firzahdzm)
- [Zeyuan Yang59](https://savrn.com/models?publisher=miiche)
- [Yurun Yuan58](https://savrn.com/models?publisher=yyuan244)
- [Yanggangu57](https://savrn.com/models?publisher=yanggangu)
- [Hongao56](https://savrn.com/models?publisher=cisco1963)
- [Christopher Subia-Waud55](https://savrn.com/models?publisher=cwaud)
- [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805)
- [Khazzz1c53](https://savrn.com/models?publisher=khazic)
- [Alfred Smith52](https://savrn.com/models?publisher=alfred8995)
- [Szekely52](https://savrn.com/models?publisher=attila115)
- [NVIDIA50](https://savrn.com/models?publisher=nvidia)
- [Wang49](https://savrn.com/models?publisher=davidwdw)

[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)

12,579 models, sorted by most downloaded.

Model

### [Llama-3.1-8B-Turkish-GGUF](https://savrn.com/models/llama-3-1-8b-turkish-gguf)

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

static quants of https://huggingface.co/erenzeytunn/Llama-3.1-8B-Turkish For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-3.1-8B-Turkish-i1-GGUF 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 are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9…

Open weights llama3.1 transformers

[View model](https://savrn.com/models/llama-3-1-8b-turkish-gguf)

Model

### [Llama-3.1-8B-Turkish-i1-GGUF](https://savrn.com/models/llama-3-1-8b-turkish-i1-gguf)

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

weighted/imatrix quants of https://huggingface.co/erenzeytunn/Llama-3.1-8B-Turkish For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/Llama-3.1-8B-Turkish-GGUF 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 are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 See…

Open weights llama3.1 transformers

[View model](https://savrn.com/models/llama-3-1-8b-turkish-i1-gguf)

Model

### [Llama-3.1-CyberAlpha8B-GGUF](https://savrn.com/models/llama-3-1-cyberalpha8b-gguf)

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

static quants of https://huggingface.co/Rumiii/Llama-3.1-CyberAlpha8B

Open weights

[View model](https://savrn.com/models/llama-3-1-cyberalpha8b-gguf)

Model

### [Llama-3.1-Nemotron-Nano-4B-v1.1-banking77-2](https://savrn.com/models/llama-3-1-nemotron-nano-4b-v1-1-banking77-2)

[EQTYLab](https://savrn.com/model-publishers/eqtylab)

Open weights

[View model](https://savrn.com/models/llama-3-1-nemotron-nano-4b-v1-1-banking77-2)

Model

### [Llama-3.1-Tulu-3-8B-SFT-a0.1-b0.1-L3-l2-e27](https://savrn.com/models/llama-3-1-tulu-3-8b-sft-a0-1-b0-1-l3-l2-e27)

[PEPO](https://savrn.com/model-publishers/pessimisticdpo)

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).

Open weights transformers

[View model](https://savrn.com/models/llama-3-1-tulu-3-8b-sft-a0-1-b0-1-l3-l2-e27)

Model · Text generation

### [Llama-3.2-1B-Instruct-Conceptizer](https://savrn.com/models/llama-3-2-1b-instruct-conceptizer)

[Haoran Zheng](https://savrn.com/model-publishers/flyingodzilla)

Open weights llama3.2 peft

[View model](https://savrn.com/models/llama-3-2-1b-instruct-conceptizer)

Model · Text generation

### [Llama-3.2-1B-Instruct-FT-20Minuten](https://savrn.com/models/llama-3-2-1b-instruct-ft-20minuten)

[Yanggangu](https://savrn.com/model-publishers/yanggangu)

A FT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply.

Open weights llama3.2 1.2B parameters 131,072 tokens transformers

[View model](https://savrn.com/models/llama-3-2-1b-instruct-ft-20minuten)

Model · Text generation

### [Llama-3.2-1B-Instruct-FT-C-STANCE](https://savrn.com/models/llama-3-2-1b-instruct-ft-c-stance)

[Yanggangu](https://savrn.com/model-publishers/yanggangu)

A FT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply.

Open weights llama3.2 1.2B parameters 131,072 tokens transformers

[View model](https://savrn.com/models/llama-3-2-1b-instruct-ft-c-stance)

Model · Text generation

### [Llama-3.2-1B-Instruct-FT-FOMC](https://savrn.com/models/llama-3-2-1b-instruct-ft-fomc)

[Yanggangu](https://savrn.com/model-publishers/yanggangu)

A FT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply.

Open weights llama3.2 1.2B parameters 131,072 tokens transformers

[View model](https://savrn.com/models/llama-3-2-1b-instruct-ft-fomc)

Model · Text generation

### [Llama-3.2-1B-Instruct-FT-MeetingBank](https://savrn.com/models/llama-3-2-1b-instruct-ft-meetingbank)

[Yanggangu](https://savrn.com/model-publishers/yanggangu)

A FT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply.

Open weights llama3.2 1.2B parameters 131,072 tokens transformers

[View model](https://savrn.com/models/llama-3-2-1b-instruct-ft-meetingbank)

Model · Text generation

### [Llama-3.2-1B-Instruct-FT-NumGLUE-cm](https://savrn.com/models/llama-3-2-1b-instruct-ft-numglue-cm)

[Yanggangu](https://savrn.com/model-publishers/yanggangu)

A FT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply.

Open weights llama3.2 1.2B parameters 131,072 tokens transformers

[View model](https://savrn.com/models/llama-3-2-1b-instruct-ft-numglue-cm)

Model · Text generation

### [Llama-3.2-1B-Instruct-FT-NumGLUE-ds](https://savrn.com/models/llama-3-2-1b-instruct-ft-numglue-ds)

[Yanggangu](https://savrn.com/model-publishers/yanggangu)

A FT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply.

Open weights llama3.2 1.2B parameters 131,072 tokens transformers

[View model](https://savrn.com/models/llama-3-2-1b-instruct-ft-numglue-ds)

Model · Text generation

### [Llama-3.2-1B-Instruct-FT-ScienceQA](https://savrn.com/models/llama-3-2-1b-instruct-ft-scienceqa)

[Yanggangu](https://savrn.com/model-publishers/yanggangu)

A FT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply.

Open weights llama3.2 1.2B parameters 131,072 tokens transformers

[View model](https://savrn.com/models/llama-3-2-1b-instruct-ft-scienceqa)

Model · Text generation

### [Llama-3.2-1B-Instruct-SMAT-20Minuten](https://savrn.com/models/llama-3-2-1b-instruct-smat-20minuten)

[Yanggangu](https://savrn.com/model-publishers/yanggangu)

A SMAT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply.

Open weights llama3.2 1.2B parameters 131,072 tokens transformers

[View model](https://savrn.com/models/llama-3-2-1b-instruct-smat-20minuten)

Model · Text generation

### [Llama-3.2-1B-Instruct-SMAT-C-STANCE](https://savrn.com/models/llama-3-2-1b-instruct-smat-c-stance)

[Yanggangu](https://savrn.com/model-publishers/yanggangu)

A SMAT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply.

Open weights llama3.2 1.2B parameters 131,072 tokens transformers

[View model](https://savrn.com/models/llama-3-2-1b-instruct-smat-c-stance)

Model · Text generation

### [Llama-3.2-1B-Instruct-SMAT-FOMC](https://savrn.com/models/llama-3-2-1b-instruct-smat-fomc)

[Yanggangu](https://savrn.com/model-publishers/yanggangu)

A SMAT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply.

Open weights llama3.2 1.2B parameters 131,072 tokens transformers

[View model](https://savrn.com/models/llama-3-2-1b-instruct-smat-fomc)

Model · Text generation

### [Llama-3.2-1B-Instruct-SMAT-MeetingBank](https://savrn.com/models/llama-3-2-1b-instruct-smat-meetingbank)

[Yanggangu](https://savrn.com/model-publishers/yanggangu)

A SMAT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply.

Open weights llama3.2 1.2B parameters 131,072 tokens transformers

[View model](https://savrn.com/models/llama-3-2-1b-instruct-smat-meetingbank)

Model · Text generation

### [Llama-3.2-1B-Instruct-SMAT-NumGLUE-cm](https://savrn.com/models/llama-3-2-1b-instruct-smat-numglue-cm)

[Yanggangu](https://savrn.com/model-publishers/yanggangu)

A SMAT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply.

Open weights llama3.2 1.2B parameters 131,072 tokens transformers

[View model](https://savrn.com/models/llama-3-2-1b-instruct-smat-numglue-cm)

Model · Text generation

### [Llama-3.2-1B-Instruct-SMAT-NumGLUE-ds](https://savrn.com/models/llama-3-2-1b-instruct-smat-numglue-ds)

[Yanggangu](https://savrn.com/model-publishers/yanggangu)

A SMAT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply.

Open weights llama3.2 1.2B parameters 131,072 tokens transformers

[View model](https://savrn.com/models/llama-3-2-1b-instruct-smat-numglue-ds)

Model · Text generation

### [Llama-3.2-1B-Instruct-SMAT-ScienceQA](https://savrn.com/models/llama-3-2-1b-instruct-smat-scienceqa)

[Yanggangu](https://savrn.com/model-publishers/yanggangu)

A SMAT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply.

Open weights llama3.2 1.2B parameters 131,072 tokens transformers

[View model](https://savrn.com/models/llama-3-2-1b-instruct-smat-scienceqa)

Model · Text generation

### [Llama-3.2-1B-Instruct-TOFU-forget10-GradDiff](https://savrn.com/models/llama-3-2-1b-instruct-tofu-forget10-graddiff)

[Varun Sampath Kumar](https://savrn.com/model-publishers/lordvarun23)

open-unlearning/tofuLlama-3.2-1B-Instructfull (Llama-3.2-1B-Instruct fine-tuned on all 4,000 TOFU QA pairs) after unlearning the forget10 split (20 fictitious authors) with GradDiff. GradDiff (Gradient Difference; Liu et al., 2022; OpenUnlearning implementation): gradient ascent on the forget set + NLL on the retain set: $\mathcal{L} = -\gamma\,\mathrm{NLL}(\text{forget}) + \alpha\,\mathrm{NLL}(\text{retain})$. TOFU contains 200 fictitious authors with 20 QA pairs each. forget10 holds 10% of them (20 authors, 400 QA pairs); retain90 holds the other 180 authors. The oracle is the same base model fine-tuned on retain90 only, i.e. a model that never saw the forget set. Hyperparameter…

Open weights llama3.2 1.2B parameters 131,072 tokens transformers

[View model](https://savrn.com/models/llama-3-2-1b-instruct-tofu-forget10-graddiff)

Model · Text generation

### [Llama-3.2-1B-Instruct-TOFU-forget10-GradDiff-ILU](https://savrn.com/models/llama-3-2-1b-instruct-tofu-forget10-graddiff-ilu)

[Varun Sampath Kumar](https://savrn.com/model-publishers/lordvarun23)

open-unlearning/tofuLlama-3.2-1B-Instructfull (Llama-3.2-1B-Instruct fine-tuned on all 4,000 TOFU QA pairs) after unlearning the forget10 split (20 fictitious authors) with GradDiff + ILU. GradDiff (Gradient Difference; Liu et al., 2022; OpenUnlearning implementation): gradient ascent on the forget set + NLL on the retain set: $\mathcal{L} = -\gamma\,\mathrm{NLL}(\text{forget}) + \alpha\,\mathrm{NLL}(\text{retain})$. ILU (Invariant LLM Unlearning; Wang et al., ICML 2025) is applied on top of GradDiff as a relearning defense: adds an IRMv1 invariance penalty computed on an unrelated task (GSM8K) so that the unlearning solution stays stationary under fine-tuning on other data (reimplemented…

Open weights llama3.2 1.2B parameters 131,072 tokens transformers

[View model](https://savrn.com/models/llama-3-2-1b-instruct-tofu-forget10-graddiff-ilu)

Model · Text generation

### [Llama-3.2-1B-Instruct-TOFU-forget10-GradDiff-SAM](https://savrn.com/models/llama-3-2-1b-instruct-tofu-forget10-graddiff-sam)

[Varun Sampath Kumar](https://savrn.com/model-publishers/lordvarun23)

open-unlearning/tofuLlama-3.2-1B-Instructfull (Llama-3.2-1B-Instruct fine-tuned on all 4,000 TOFU QA pairs) after unlearning the forget10 split (20 fictitious authors) with GradDiff + SAM. GradDiff (Gradient Difference; Liu et al., 2022; OpenUnlearning implementation): gradient ascent on the forget set + NLL on the retain set: $\mathcal{L} = -\gamma\,\mathrm{NLL}(\text{forget}) + \alpha\,\mathrm{NLL}(\text{retain})$. SAM (Sharpness-Aware Minimization for robust unlearning; Fan et al., ICML 2025) is applied on top of GradDiff as a relearning defense: the forget loss is evaluated at the sharpness-aware perturbed weights $\theta + \rho\,g/\lVert g\rVert$ (retain gradient at $\theta$), so the…

Open weights llama3.2 1.2B parameters 131,072 tokens transformers

[View model](https://savrn.com/models/llama-3-2-1b-instruct-tofu-forget10-graddiff-sam)

Model · Text generation

### [Llama-3.2-1B-Instruct-TOFU-forget10-NPO](https://savrn.com/models/llama-3-2-1b-instruct-tofu-forget10-npo)

[Varun Sampath Kumar](https://savrn.com/model-publishers/lordvarun23)

open-unlearning/tofuLlama-3.2-1B-Instructfull (Llama-3.2-1B-Instruct fine-tuned on all 4,000 TOFU QA pairs) after unlearning the forget10 split (20 fictitious authors) with NPO. NPO (Negative Preference Optimization; Zhang et al., 2024): $\mathcal{L} = \gamma\,\tfrac{2}{\beta}\,\mathbb{E}{\text{forget}}\big[\log\big(1+(\pi\theta/\pi{\text{ref}})^{\beta}\big)\big] + \alpha\,\mathrm{NLL}(\text{retain})$, with $\pi{\text{ref}}$ the frozen starting model. TOFU contains 200 fictitious authors with 20 QA pairs each. forget10 holds 10% of them (20 authors, 400 QA pairs); retain90 holds the other 180 authors. The oracle is the same base model fine-tuned on retain90 only, i.e. a model that never saw…

Open weights llama3.2 1.2B parameters 131,072 tokens transformers

[View model](https://savrn.com/models/llama-3-2-1b-instruct-tofu-forget10-npo)

## 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.