# Models, Page 170 of 525
Source: https://savrn.com/models/page/170
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 · Text classification

### [Jev-Omni](https://savrn.com/models/jev-omni)

[Wiktor Ryznar](https://savrn.com/model-publishers/rydward98)

A multimodal decision classifier for text, images, audio and video. Supply a question and options; receive a probability for each option—not a generated explanation. Built on Gemma 4 12B IT, with a 30,000-question fine-tuning run. Merged-model results. ¹Equal-weight scenario/group average. Reference scores are officially reported by their developers and may use different evaluation protocols. Jev-Omni cost uses its recorded input tokens at OpenRouter's Gemma 3 12B input rate ($0.05/M); its classifier generates no output tokens. Both classifiers are priced one call per question: a classifier answers one question at a time, so the state is re-sent for each of them and there is no discount for…

Open weights apache-2.0 transformers

[View model](https://savrn.com/models/jev-omni)

Model · Image and text to text

### [Jev-Omni-MLX-4bit](https://savrn.com/models/jev-omni-mlx-4bit)

[Leo Zhang](https://savrn.com/model-publishers/ruiruiz30)

An Apple Silicon MLX conversion of akhilaaa3/Jev-Omni for local inference on a Mac mini with 16GB unified memory. This repository keeps the upstream Jev-Omni unified multimodal checkpoint and its trained 256-way decision head, then converts the language-model weights to 4-bit affine quantization with group size 64. The vision weights remain BF16 and the decision head remains FP32. It is an independent conversion. It is not an official TypeSafe Jev release and does not claim to reproduce TypeSafe's proprietary system. It is also not a new fine-tune. - Mac mini, Apple M4, 10 CPU cores, 16GB unified memory - macOS 26.5.1 - Python 3.13.12 - MLX 0.32.2 - MLX-VLM 0.7.1 - single request, batch…

Open weights apache-2.0 12B parameters 262,144 tokens mlx

[View model](https://savrn.com/models/jev-omni-mlx-4bit)

Model · Text classification

### [Jev-Qwen3Guard-Gen-Domain-0.6B](https://savrn.com/models/jev-qwen3guard-gen-domain-0-6b)

[Pengyi Zhang](https://savrn.com/model-publishers/zhangpy)

English | 简体中文 Jev-Qwen3Guard-Gen-Domain-0.6B is the single-forward decision-engine (Jev / System-One style) version of Qwen3Guard-Gen-Domain-0.6B, a generative guard model for the Hong Kong elderly-care domain. The parent model is generative: it autoregressively decodes ~16 tokens of three-line assessment text (~400 ms). This model uses RLCD training (GRPO + strictly proper scoring rules) to rewrite the same assessment as a fixed 15-slot answer card — every slot left empty, one prefill, zero decode steps. Reading next-token probabilities at each slot anchor yields the full decision: The safety taxonomy (13 categories = 9 general + 4 HK elderly-care additions), the dual evaluation modes…

Open weights apache-2.0 596M parameters 32,768 tokens transformers

[View model](https://savrn.com/models/jev-qwen3guard-gen-domain-0-6b)

Model

### [JevEmbed-Qwen3-Embedding-4B](https://savrn.com/models/jevembed-qwen3-embedding-4b)

[Lychee Team](https://savrn.com/model-publishers/hit-tmg)

Open weights apache-2.0

[View model](https://savrn.com/models/jevembed-qwen3-embedding-4b)

Model

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

[Joebu](https://savrn.com/model-publishers/joebumodels)

Multimodal JEV is a visual judgment model based on Qwen3.5-4B. It evaluates questions about images and returns structured judgments and probabilities rather than generating free-form answers. The checkpoint includes model weights, decision and ordinal heads, tokenizer, image processor, configuration and calibration metadata. Weights are stored in six indexed safetensors shards, totaling approximately 9.7 GiB. Inference requires the custom UnifiedJev loader, the mso runtime modules and their Python dependencies. These must be available on your Python import path; downloading the checkpoint alone is not sufficient. Standard Qwen loading does not implement the JEV judgment interface. The…

Open weights 5.2B parameters 262,144 tokens

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

Model · Text generation

### [JevK5-FP8](https://savrn.com/models/jevk5-fp8)

[Liodon AI](https://savrn.com/model-publishers/liodon-ai)

FP8 quantization of alibiserikbay/JevK5, published by Liodon AI. Quantized with llm-compressor using the FP8DYNAMIC scheme: weights are cast to FP8 (E4M3) per-channel ahead of time, activations are quantized to FP8 dynamically per-token at inference time. No calibration dataset is needed for this scheme, so the quantized weights are numerically just a direct cast of the original — no calibration-set bias to worry about. lmhead is left unquantized (standard practice — negligible size, disproportionate quality impact if quantized). vLLM Text Generation Inference (TGI) SGLang FP8 execution requires an NVIDIA GPU with compute capability ≥ 8.9 (Ada/Hopper/Blackwell — RTX 40-series, L4/L40S…

Open weights other 4.2B parameters 262,144 tokens transformers

[View model](https://savrn.com/models/jevk5-fp8)

Model

### [Jev_Qwen3.8-27B-GGUF](https://savrn.com/models/jev-qwen3-8-27b-gguf)

[ramGPT](https://savrn.com/model-publishers/ramgpt)

GGUF quantization of SargeDev/JevQwen3.8-27B. - JevQwen3.8-27B-Q4KM.gguf — Q4KM, about 15.8 GiB, 4.92 BPW The source config declares mtpnumhiddenlayers: 1, but the published source weights contain 64 main blocks (blk.0 through blk.63) and no MTP / NextN tensors. A normal conversion therefore advertises 65 blocks and fails to load in llama.cpp because blk.64. tensors are absent. This target GGUF was converted with llama.cpp bd4f514 using --no-mtp, which produces the correct 64-block target model. Validated with llama.cpp bd4f514 on an RTX 4090. The source model was trained with thinking disabled. For the tuned behavior, use reasoning off. llama-cli -m JevQwen3.8-27B-Q4KM.gguf -ngl 999 -c…

Open weights apache-2.0

[View model](https://savrn.com/models/jev-qwen3-8-27b-gguf)

Model

### [Jev_Qwen3.8-27B-NVFP4-FP8](https://savrn.com/models/jev-qwen3-8-27b-nvfp4-fp8)

[W C](https://savrn.com/model-publishers/sargedev)

NVFP4 + FP8 mixed-precision quantization of SargeDev/JevQwen3.8-27B (itself a QLoRA merge of huihui-ai/Huihui-Qwen3.8-27B-abliterated trained on SargeDev/jev-distill-corpus-v3). - NVFP4 W4A4 (E2M1, block 16, FP8-E4M3 scales): all attention projections + gate/upproj - FP8DYNAMIC (per-channel weights, per-token activations): all downproj — the layer class most sensitive to 4-bit - 28 GB total (vs 51 GB bf16) — loads in vLLM with --quantization compressed-tensors 512 samples, 2048 max seq: 384 held-out jev-distill judge prompts in the exact eval-time format (state + question + options), plus 128 ultrachat200k general-chat rows to keep broad-language calibration honest. Calibrating on the…

Open weights apache-2.0 21.2B parameters 262,144 tokens

[View model](https://savrn.com/models/jev-qwen3-8-27b-nvfp4-fp8)

Model

### [Jeva-9b](https://savrn.com/models/jeva-9b)

[Guilherme Keller2](https://savrn.com/model-publishers/guilherme34)

Bruh Jev is close source... hmmmmm, should i make one myself? YES FOR SURE SOO... Coding sounds Computer almost crashing sounds its here!!! MY SONNN i present you to the world!! - "typenotsafe", 2026

Open weights 9.7B parameters 262,144 tokens

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

Model

### [Jeva-9b-gguf](https://savrn.com/models/jeva-9b-gguf)

[Guilherme Keller2](https://savrn.com/model-publishers/guilherme34)

One more model like Jev, hopefully good, this is the gguf of it btw

Open weights

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

Model

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

[Chegrintsev](https://savrn.com/model-publishers/ruslan777-888)

Open weights

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

Model

### [Johnny-Rotten-SexPistols-1977Era-v2-RVC](https://savrn.com/models/johnny-rotten-sexpistols-1977era-v2-rvc)

[Muhammad Farhan Adhiprawira](https://savrn.com/model-publishers/farhan-36)

Open weights

[View model](https://savrn.com/models/johnny-rotten-sexpistols-1977era-v2-rvc)

Model · Image to image

### [JoyAI-Image-Edit-Diffusers](https://savrn.com/models/joyai-image-edit-diffusers)

[Le Jin](https://savrn.com/model-publishers/jinstudio)

Open weights apache-2.0 16.3B parameters diffusers

[View model](https://savrn.com/models/joyai-image-edit-diffusers)

Model · Image to image

### [JoyAI-Image-Edit-Plus-Diffusers](https://savrn.com/models/joyai-image-edit-plus-diffusers)

[Le Jin](https://savrn.com/model-publishers/jinstudio)

Open weights apache-2.0 16.3B parameters diffusers

[View model](https://savrn.com/models/joyai-image-edit-plus-diffusers)

Model

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

[Julxsmq](https://savrn.com/model-publishers/julxsmq)

Open weights

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

Model · Text classification

### [Julia-1-LiteRT](https://savrn.com/models/julia-1-litert)

[LiteRT Community (FKA TFLite)](https://savrn.com/model-publishers/litert-community)

The SupersonicLabs/Julia-1 decision model, converted to LiteRT with litert-torch, answers one question in a median of 80.7 ms (first session) to 112.1 ms (second session) of graph time on a Samsung Galaxy S26 GPU with explicit FP32 computation, at a 512-token window (three runs on 2026-09-30; each run's thermal state is below). Julia-1 reads a state (text or JSON), a typed question and 2 to 20 options, and returns one probability per option from one forward pass. A choice question returns the winning option, a score question the expected index on an ordered rubric, and a noul (yes/no) question the probability of true. On the phone, all 706 validation rows got the same answer as the author's…

Open weights apache-2.0 litert

[View model](https://savrn.com/models/julia-1-litert)

Model · Text classification

### [Julia-1-MLX](https://savrn.com/models/julia-1-mlx)

[Zain Merchant](https://savrn.com/model-publishers/zainmerchan)

Julia-1 for Apple silicon. Runs Supersonic Labs' Julia-1 decision model on the Mac's GPU with MLX through the julia-mlx runtime: the same answers as the official PyTorch runtime on its published evaluations, 5–14× faster on the same Mac. The upstream model repository is SupersonicLabs/Julia-1. The files in this repository are Supersonic Labs' Julia-1 checkpoint, unchanged (model.safetensors SHA-256 df853bf7fe424420011f3d0c47a05d7341aa9eefa7fb9f203ea4aada4ad95b72). The runtime maps it into MLX directly, so no converted copy is needed. Precision (dtype="float16") and embedding placement are load-time options rather than separate files. This is an independent project, not affiliated with or…

Open weights apache-2.0 144M parameters mlx

[View model](https://savrn.com/models/julia-1-mlx)

Model · Text generation

### [K2-Horizon-3.7B-Q4_0-ARM-GGUF](https://savrn.com/models/k2-horizon-3-7b-q4-0-arm-gguf)

[Matthew Broudy](https://savrn.com/model-publishers/bpmatt)

every weight matrix, including the 250,624-token embedding table and output head, is Q40 (llama-quantize --pure), made from IFM's BF16 GGUF. The official GGUF repo has no Q40 file. On ARM CPUs with the i8mm or dotprod instructions llama.cpp runs Q40 faster than the K-quants: on a Snapdragon phone this file reads prompts 1.4-1.7x as fast as IFM's Q4KM, but generates only 1.04-1.11x as fast, and it is 9 % smaller. I tested it as an on-device chat model for an Android voice assistant (llama.cpp, CPU only). The file name follows the naming of IFM's GGUF repo (K2-Horizon-4B-.gguf). K2-Horizon family (3.7B core parameters plus a large vocabulary; 5.06B parameters in the file). All credit for the…

Open weights apache-2.0 gguf

[View model](https://savrn.com/models/k2-horizon-3-7b-q4-0-arm-gguf)

Model

### [K2-Horizon-3.7B-heretic-xortron-GGUF](https://savrn.com/models/k2-horizon-3-7b-heretic-xortron-gguf)

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

static quants of https://huggingface.co/darkc0de/K2-Horizon-3.7B-heretic-xortron For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants are available at https://huggingface.co/mradermacher/K2-Horizon-3.7B-heretic-xortron-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/k2-horizon-3-7b-heretic-xortron-gguf)

Model

### [K2-Horizon-3.7B-heretic-xortron-i1-GGUF](https://savrn.com/models/k2-horizon-3-7b-heretic-xortron-i1-gguf)

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

weighted/imatrix quants of https://huggingface.co/darkc0de/K2-Horizon-3.7B-heretic-xortron

Open weights

[View model](https://savrn.com/models/k2-horizon-3-7b-heretic-xortron-i1-gguf)

Model

### [K2-Horizon-32B-heretic-i1-GGUF](https://savrn.com/models/k2-horizon-32b-heretic-i1-gguf)

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

weighted/imatrix quants of https://huggingface.co/soyaakinohara/K2-Horizon-32B-heretic For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/K2-Horizon-32B-heretic-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/k2-horizon-32b-heretic-i1-gguf)

Model

### [K2-Horizon-7B-GGUF](https://savrn.com/models/k2-horizon-7b-gguf)

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

static quants of https://huggingface.co/IFM/K2-Horizon-7B For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants are available at https://huggingface.co/mradermacher/K2-Horizon-7B-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 See…

Open weights apache-2.0 transformers

[View model](https://savrn.com/models/k2-horizon-7b-gguf)

Model

### [K2-Horizon-7B-MXFP8](https://savrn.com/models/k2-horizon-7b-mxfp8)

[Use-AI](https://savrn.com/model-publishers/fp8enjoyer)

MXFP8-quantized version of IFM/K2-Horizon-7B, targeting NVIDIA Blackwell GPUs. This repository provides an MXFP8 conversion of K2-Horizon-7B for use with block-scaled FP8 inference runtimes. The model weights were converted to MXFP8 using: - Blackwell-compatible block-scaled matrix multiplication The converted model contains approximately 254 MXFP8 modules. IFM/K2-Horizon-7B Please refer to the original model card for architecture details, training information, intended use, and limitations. The main quantized checkpoint is provided in SafeTensors format. Users should also obtain or use the corresponding configuration, tokenizer, and model architecture files from the original K2-Horizon-7B…

Open weights apache-2.0

[View model](https://savrn.com/models/k2-horizon-7b-mxfp8)

Model

### [K2-Horizon-7B-heretic-GGUF](https://savrn.com/models/k2-horizon-7b-heretic-gguf)

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

static quants of https://huggingface.co/Avicennasis/K2-Horizon-7B-heretic

Open weights

[View model](https://savrn.com/models/k2-horizon-7b-heretic-gguf)

## 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.