# Models, Page 168 of 525
Source: https://savrn.com/models/page/168
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

### [Image-Color-Prediction](https://savrn.com/models/image-color-prediction)

[Nuthan Prasad KG](https://savrn.com/model-publishers/nuthan-444)

Open weights

[View model](https://savrn.com/models/image-color-prediction)

Model · Text to image

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

[Alibaba-AAIG](https://savrn.com/model-publishers/alibaba-aaig)

This repository contains the released guardrail heads for InGuard, an in-pipeline safety framework for text-to-image generation. It does not contain the five base text-to-image models. For each supported model, the release provides: - a PE-MLP classifier operating on text-encoder embeddings; and - a ConvNeXt-Base detector operating on an intermediate latent estimate. Detection step uses zero-based indexing and refers to the default deployed configuration in the InGuard code repository. The PE-MLP checkpoints contain inputdim, hiddendim, numlayers, and dropout together with modelstatedict; no separate PE-MLP config file is required. Training optimizer states have been removed from all…

Open weights apache-2.0 pytorch

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

Model · Translation

### [Index-Echo-S2ST-2B-FP8](https://savrn.com/models/index-echo-s2st-2b-fp8)

[Index Team](https://savrn.com/model-publishers/indexteam)

Official FP8 (W8A8) quantization of IndexTeam/Index-Echo-S2ST-2B, part of the Index-Echo speech-to-speech translation (S2ST) model family by bilibili. This repository mirrors the original checkpoint layout; only the text LLM backbone (stlmllm/) is quantized to FP8 - the audio tower, connector, and all speech-synthesis components remain in BF16. Use it exactly like the original repo (same infer.py / configs). - All Linear layers of the language model backbone (stlmllm/) are quantized; the audio tower, connector, lmhead, embeddings and all other pipeline components are kept in BF16. Measured on an NVIDIA A100 against the original BF16 checkpoint (greedy decoding, official translation prompt)…

Open weights apache-2.0 transformers

[View model](https://savrn.com/models/index-echo-s2st-2b-fp8)

Model · Translation

### [Index-Echo-S2ST-9B-FP8](https://savrn.com/models/index-echo-s2st-9b-fp8)

[Index Team](https://savrn.com/model-publishers/indexteam)

Official FP8 (W8A8) quantization of IndexTeam/Index-Echo-S2ST-9B, part of the Index-Echo speech-to-speech translation (S2ST) model family by bilibili. This repository mirrors the original checkpoint layout; only the text LLM backbone (stlmllm/) is quantized to FP8 - the audio tower, connector, and all speech-synthesis components remain in BF16. Use it exactly like the original repo (same infer.py / configs). - All Linear layers of the language model backbone (stlmllm/) are quantized; the audio tower, connector, lmhead, embeddings and all other pipeline components are kept in BF16. Measured on an NVIDIA A100 against the original BF16 checkpoint (greedy decoding, official translation prompt)…

Open weights apache-2.0 transformers

[View model](https://savrn.com/models/index-echo-s2st-9b-fp8)

Model · Translation

### [Index-Echo-S2ST-9B-GGUF](https://savrn.com/models/index-echo-s2st-9b-gguf)

[Index Team](https://savrn.com/model-publishers/indexteam)

Official GGUF conversion of the text LLM backbone (stlmllm/) of IndexTeam/Index-Echo-S2ST-9B, part of the Index-Echo speech-to-speech translation (S2ST) model family by bilibili. Converted with llama.cpp (master, 2026-10); static post-training quantization, all bit-widths in this single repository. (sorted by size) Before release, each quantization tier was validated on GPU (NVIDIA A100) against the F16 conversion: per-token KL divergence / RMS delta-p via llama-perplexity, plus greedy-generation spot checks against the original BF16 weights (transformers reference). Generation outputs of Q4KM matched the reference almost verbatim. Converted and published by the Index team, 2026-10-03.

Open weights apache-2.0 llama.cpp

[View model](https://savrn.com/models/index-echo-s2st-9b-gguf)

Model

### [Index-Echo-S2ST-9B-MLX](https://savrn.com/models/index-echo-s2st-9b-mlx)

[Vanchye](https://savrn.com/model-publishers/vanch007)

MLX weights and components.

Open weights

[View model](https://savrn.com/models/index-echo-s2st-9b-mlx)

Model · Translation

### [Index-Echo-S2TT-9B-GGUF](https://savrn.com/models/index-echo-s2tt-9b-gguf)

[Index Team](https://savrn.com/model-publishers/indexteam)

Official GGUF conversion of the text LLM backbone (llm/) of IndexTeam/Index-Echo-S2TT-9B, part of the Index-Echo speech-to-text translation (S2TT) model family by bilibili. Converted with llama.cpp (master, 2026-10); static post-training quantization, all bit-widths in this single repository. (sorted by size) Before release, each quantization tier was validated on GPU (NVIDIA A100) against the F16 conversion: per-token KL divergence / RMS delta-p via llama-perplexity, plus greedy-generation spot checks against the original BF16 weights (transformers reference). Generation outputs of Q4KM matched the reference almost verbatim. Converted and published by the Index team, 2026-10-03.

Open weights apache-2.0 llama.cpp

[View model](https://savrn.com/models/index-echo-s2tt-9b-gguf)

Model · Translation

### [Index-Nailong-9B-GGUF](https://savrn.com/models/index-nailong-9b-gguf)

[Index Team](https://savrn.com/model-publishers/indexteam)

Official GGUF conversion of IndexTeam/Index-Nailong-9B, part of the Index-Translate multilingual translation model family (150 languages, terminology/format-constrained translation, controlled dubbing translation, long-document translation). Converted with llama.cpp (master, 2026-10); static post-training quantization, all bit-widths in this single repository. (sorted by size) Translation prompt format (greedy decoding, temperature=0 recommended; use the chat template with enablethinking: false): Beyond plain translation, the models follow the instTrans constrained-translation format. The official client wraps requests into the canonical structure 【源文】 + numbered 1. 【硬性要求】 + 2. 【注意】 +…

Open weights apache-2.0 llama.cpp

[View model](https://savrn.com/models/index-nailong-9b-gguf)

Model · Translation

### [Index-Translate-35B-A3B-preview-GGUF](https://savrn.com/models/index-translate-35b-a3b-preview-gguf)

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

static quants of https://huggingface.co/IndexTeam/Index-Translate-35B-A3B-preview For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants are available at https://huggingface.co/mradermacher/Index-Translate-35B-A3B-preview-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…

Open weights apache-2.0 transformers

[View model](https://savrn.com/models/index-translate-35b-a3b-preview-gguf)

Model · Translation

### [Index-Translate-35B-A3B-preview-i1-GGUF](https://savrn.com/models/index-translate-35b-a3b-preview-i1-gguf)

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

weighted/imatrix quants of https://huggingface.co/IndexTeam/Index-Translate-35B-A3B-preview For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/Index-Translate-35B-A3B-preview-GGUF This is a vision model - mmproj files (if any) will be in the static repository. 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…

Open weights apache-2.0 transformers

[View model](https://savrn.com/models/index-translate-35b-a3b-preview-i1-gguf)

Model

### [Inference_HUAWEI](https://savrn.com/models/inference-huawei)

[Linfeng Wang](https://savrn.com/model-publishers/linfengwangpower)

本包提供内源多模态数据生成、内源推理、外源 bank 构建、离线权重校准和内源＋外源推理。 先按第 1–3 节推理一个内源例子；需要外源检索时再看第 5 节。真实 M4 yearly 的切分、推理和 MASE 计算见完整评测例子。 - 原始历史可以是 NPZ/Zarr、CSV、Arrow 或 Parquet；不能包含可被编码的未来标签。 - query.npz 除数值历史外，还包含每个 32 点 patch 的图像、文本特征。 - runendogenous.py 读取已经生成的特征，不直接把原始 CSV/Arrow 当模型输入。 建议 Python 3.11，支持 3.10–3.12。依赖 requirements.txt。 把下面示例路径换成你自己的路径，文件名保持 checkpoint 原来的写法： 只做内源推理需要内源模型和 CLIP。若测最终联合训练模型的内源结果，还必须加载最终 sharedheadstate.pt；不传 --shared-head 使用的是内源目录里的旧 head。 外源推理另需外源模型和 TS 检索编码器。TS encoder 是冻结的数值骨干，可选用任意基模（TimesFM3等）。 两份文件的变量 ID 都是 series-A、series-B。这个例子的查询取自合成训练序列末尾，仅用于检查接口；它不是独立的 benchmark 测试集。 此时 demo/query.npz 包含： 256 个点对应 8 个 32 点 patch；每个 patch 的图像和文本都编码成 512 维特征。不调用大语言模型，描述由统计量模板生成。…

Open weights

[View model](https://savrn.com/models/inference-huawei)

Model · Text generation

### [Inferno-Exp-1.0](https://savrn.com/models/inferno-exp-1-0)

[AlgoDriveAI](https://savrn.com/model-publishers/algodriveai)

Inferno-Exp-1.0 is a 76.2M-parameter decoder-only base language model trained from scratch by AlgoDriveAI. It is a math-first model: its training data centers on arithmetic and math word problems, and it is geared toward the AxiomicLabs small-language-model benchmarks used in SLM challenges, ArithMark 3.0 and ArithMark 2.0 (results below). Future versions will build on this math foundation with improvements aimed at broader language understanding and logical reasoning. This is a base model (no instruction tuning). The model code ships in the repo, so load it with trustremotecode=True. All scores are zero-shot on the checkpoint in this repo. The ArithMark benchmarks were scored with…

Open weights apache-2.0 76M parameters 2,048 tokens transformers

[View model](https://savrn.com/models/inferno-exp-1-0)

Model

### [Inovay.iA](https://savrn.com/models/inovay-ia)

[Rian Cruz Nascimento](https://savrn.com/model-publishers/frutspowerbrazilian)

Open weights apache-2.0

[View model](https://savrn.com/models/inovay-ia)

Model

### [Instagram_Downloads](https://savrn.com/models/instagram-downloads)

[Godfathercollections Storage](https://savrn.com/model-publishers/tgfstorage01)

Open weights apache-2.0

[View model](https://savrn.com/models/instagram-downloads)

Model · Text to image

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

[Xuanlang Dai](https://savrn.com/model-publishers/xuanlangdai)

InstantFusion connects heterogeneous image generators through a learned shared latent interface. Model-specific Latent AutoEncoders (LAEs) align intermediate states while preserving the information needed for reconstruction and continued denoising. The base generators remain frozen during LAE training. The shared interface supports generation acceleration, multi-preference composition, and cross-model on-policy distillation (OPD). This repository provides trained LAE interface weights. The base generators must be obtained separately. LAE reconstruction examples for Qwen-Image, FLUX.1, and SD3. Mean SSIM is reported over 100 images for each model. Intermediate states can be transferred…

Open weights

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

Model

### [InstructPro2](https://savrn.com/models/instructpro2)

[Charles Novak](https://savrn.com/model-publishers/charlesnovak)

Model weights for InstructPro2: Natural Language and Biological Grounding Guided De Novo Protein Design — a 3.2B protein design model that generates sequences from natural-language function descriptions, optionally conditioned on a ligand or an antigen. The Stage 2 checkpoints are larger because they carry the gated cross-attention adapters and the frozen ChemBERTa and ESM-2 encoders. Pass a checkpoint to the scripts in the code repository: The scripts set the architecture mode and the generation settings for each checkpoint; see the code repository's README.

Open weights mit fairseq

[View model](https://savrn.com/models/instructpro2)

Model · Image segmentation

### [IntegraCAR-DeepLabv3-ResNet50-64](https://savrn.com/models/integracar-deeplabv3-resnet50-64)

[Laboratório de Inteligência Computacional e Sistemas de informação](https://savrn.com/model-publishers/laicsiifes)

Open weights mit segmentation-models-pytorch

[View model](https://savrn.com/models/integracar-deeplabv3-resnet50-64)

Model · Object detection

### [Intel_ssdlite_mobilenet_v2_FP16](https://savrn.com/models/intel-ssdlite-mobilenet-v2-fp16)

[Advantech](https://savrn.com/model-publishers/advantech-eiot)

This repository distributes an existing OpenVINO IR conversion of the TensorFlow SSDLite MobileNet V2 COCO object-detection model for the Advantech Intel EdgeAI SDK benchmark. Advantech did not train the original model. Download both model files into the same directory and retain their filenames. FP16 describes the stored weights; it does not guarantee that all operations execute with FP16 precision on every device. The tensor names and shapes describe the XML in this repository. Other upstream conversions can use different output names or preprocessing. The original artifact metadata records OpenVINO 2024.6.0 conversion tooling. The existing XML and BIN were retained without reconversion…

Open weights apache-2.0 openvino

[View model](https://savrn.com/models/intel-ssdlite-mobilenet-v2-fp16)

Model · Text generation

### [InterimLabs-Qwen3.5-4B-GGUF-Q4_K_M](https://savrn.com/models/interimlabs-qwen3-5-4b-gguf-q4-k-m)

[Interim Labs](https://savrn.com/model-publishers/interimlabs)

This is the pinned Q4KM GGUF file for Minstrel's optional Advanced local language engine on Windows and Linux. The model weights are byte-identical to mradermacher/Qwen3.5-4B-GGUF at 1a5df2c0cba51dae8ac5888420360d8703707171; the GGUF source file SHA-256 is 51eafbc127f35598c8f1d2ec58b2520d6126c7d1195c4eca26832e63a2939d39. The signed Minstrel manifest and provenance file are included for slot detailed-local, artifact version 0.1.0. The reference upstream is Qwen/Qwen3.5-4B at 851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a. The GGUF source does not establish the exact Qwen revision it converted. The Apache-2.0 license file is copied from that reference snapshot. Minstrel currently keeps this choice…

Open weights apache-2.0 llama.cpp

[View model](https://savrn.com/models/interimlabs-qwen3-5-4b-gguf-q4-k-m)

Model · Text generation

### [InterimLabs-Qwen3.5-4B-MLX-4bit](https://savrn.com/models/interimlabs-qwen3-5-4b-mlx-4bit)

[Interim Labs](https://savrn.com/model-publishers/interimlabs)

This is a text-only, 4-bit affine/group-64 MLX derivative for Minstrel's optional Advanced local language engine. It is based on the pinned mlx-community/Qwen3.5-4B-4bit snapshot 0e7ffd5c629ef7719d4cbc04069232580bfa9d9c. The deriveqwen35text.py tool removed vision tensors and capped the packaged context at 8,192 tokens. The signed Minstrel manifest and provenance file are included. The manifest is for Minstrel's detailed-local slot, artifact version 0.1.0; it is not a general quality certification. The reference upstream is Qwen/Qwen3.5-4B at 851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a. The source conversion does not establish the exact Qwen revision it used. The Apache-2.0 license file is…

Open weights apache-2.0 4.2B parameters 8,192 tokens mlx

[View model](https://savrn.com/models/interimlabs-qwen3-5-4b-mlx-4bit)

Model

### [InterimLabs-Qwen3.5-9B-GGUF-Q4_K_M](https://savrn.com/models/interimlabs-qwen3-5-9b-gguf-q4-k-m)

[Interim Labs](https://savrn.com/model-publishers/interimlabs)

Open weights

[View model](https://savrn.com/models/interimlabs-qwen3-5-9b-gguf-q4-k-m)

Model · Image and text to text

### [InternVL3-8B-CoT-SFT-vu-ckpt1100](https://savrn.com/models/internvl3-8b-cot-sft-vu-ckpt1100)

[Margi Pandya](https://savrn.com/model-publishers/margipandya)

A multi-image chain-of-thought fine-tune of OpenGVLab/InternVL3-8B, merged into standalone weights so it loads with trustremotecode=True in transformers or vLLM without the adapter. final model. - LoRA, r=64, alpha=128, dropout=0.05, on q/k/v/oproj and gate/up/downproj - modulestosave: ["visionmodel", "mlp1"] — the vision tower and the projector were trained in full rather than low-rank adapted, which is why the adapter was 1.3 GB and why merging is required for serving Accuracy against the base model. The prompt is stated per row, because it matters more than the fine-tune does: rows are only comparable to other rows evaluated the same way. MIRB and MuirBench/BLINK use different image…

Open weights apache-2.0 7.9B parameters 32,768 tokens transformers

[View model](https://savrn.com/models/internvl3-8b-cot-sft-vu-ckpt1100)

Model · Image and text to text

### [Inverse-LLaVA-7B](https://savrn.com/models/inverse-llava-7b)

[Xuhui Zhan](https://savrn.com/model-publishers/xuhuizhan5)

Inverse-LLaVA maps language features into the visual feature space and fuses them inside the language decoder. This is the full-data 7B checkpoint trained jointly on visual instructions, without a separate alignment-pretraining stage. On Linux with a CUDA-compatible PyTorch installation, install the source package: Use a commit hash in revision= to pin a particular Hub snapshot. The weights file has the stable identity below; the loader verifies its checksums before constructing the model. It downloads the pinned Vicuna and CLIP weights separately when they are not cached. The 695 MB delta is not the total download or GPU-memory requirement. Allow space for the approximately 14 GB language…

Open weights llama2

[View model](https://savrn.com/models/inverse-llava-7b)

Model · Text to speech

### [Irodori-TTS-v4.1-Small-fp32](https://savrn.com/models/irodori-tts-v4-1-small-fp32)

[HIDING FOX](https://savrn.com/model-publishers/hidingfox)

This model was converted to MLX format from Aratako/Irodori-TTS-v4.1-Small using mlx-audio version 0.4.7. prediction in one checkpoint. - v4.1 differs from v4 only in the duration predictor, which upstream retrained separately with every other parameter frozen. The remaining 683 of 714 tensors are bit-identical to v4. - The ModernBERT-ja-310m text encoder weights and its tokenizer are bundled, and the Semantic-DACVAE-Japanese-32dim codec ships in dacvae/, so inference needs no additional downloads. and concatenates them, which matches training better than one long recording. - Short caption-only prompts are shorter than in v4, but still over-predicted. Measured with the same reference clip…

Open weights 766M parameters mlx-audio

[View model](https://savrn.com/models/irodori-tts-v4-1-small-fp32)

## 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.