# Models, Page 377 of 477 Source: https://savrn.com/models/page/377 Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- 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. 11,442Models 3,247Datasets 285Papers 6,976Publishers 12,091Sourced relationships Updated 2026-10-19 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Models Task - [Text generation1,413](https://savrn.com/models?task=Text+generation) - [Image and text to text512](https://savrn.com/models?task=Image+and+text+to+text) - [Feature extraction341](https://savrn.com/models?task=Feature+extraction) - [Robotics338](https://savrn.com/models?task=Robotics) - [Text classification303](https://savrn.com/models?task=Text+classification) - [Text to image209](https://savrn.com/models?task=Text+to+image) - [Speech recognition171](https://savrn.com/models?task=Speech+recognition) - [Audio classification157](https://savrn.com/models?task=Audio+classification) - [Image classification137](https://savrn.com/models?task=Image+classification) - [Text to speech132](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 learning93](https://savrn.com/models?task=Reinforcement+learning) - [Zero-shot classification93](https://savrn.com/models?task=Zero-shot+classification) - [Image segmentation88](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) - [Translation78](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) - [Video classification59](https://savrn.com/models?task=Video+classification) - [Image to text58](https://savrn.com/models?task=Image+to+text) - [Text to video55](https://savrn.com/models?task=Text+to+video) Library - [transformers2,777](https://savrn.com/models?library=transformers) - [peft345](https://savrn.com/models?library=peft) - [diffusers187](https://savrn.com/models?library=diffusers) - [pytorch178](https://savrn.com/models?library=pytorch) - [gguf167](https://savrn.com/models?library=gguf) - [lerobot139](https://savrn.com/models?library=lerobot) - [sentence-transformers139](https://savrn.com/models?library=sentence-transformers) - [mlx136](https://savrn.com/models?library=mlx) - [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) - [vllm38](https://savrn.com/models?library=vllm) - [onnx36](https://savrn.com/models?library=onnx) - [diffusion-single-file27](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) - [openvino18](https://savrn.com/models?library=openvino) - [coreml17](https://savrn.com/models?library=coreml) - [exllamav316](https://savrn.com/models?library=exllamav3) - [openpi16](https://savrn.com/models?library=openpi) - [litert15](https://savrn.com/models?library=litert) - [rkllm15](https://savrn.com/models?library=rkllm) Modality - [Other6,473](https://savrn.com/models?modality=Other) - [Text2,612](https://savrn.com/models?modality=Text) - [Image and text570](https://savrn.com/models?modality=Image+and+text) - [Image565](https://savrn.com/models?modality=Image) - [Audio478](https://savrn.com/models?modality=Audio) - [Control431](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-2.03,105](https://savrn.com/models?license=apache-2.0) - [mit1,246](https://savrn.com/models?license=mit) - [other703](https://savrn.com/models?license=other) - [cc-by-nc-4.0193](https://savrn.com/models?license=cc-by-nc-4.0) - [cc-by-4.0148](https://savrn.com/models?license=cc-by-4.0) - [bsd-3-clause98](https://savrn.com/models?license=bsd-3-clause) - [gemma62](https://savrn.com/models?license=gemma) - [cc-by-nc-sa-4.049](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) - [llama3.128](https://savrn.com/models?license=llama3.1) - [unknown27](https://savrn.com/models?license=unknown) - [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-m11](https://savrn.com/models?license=creativeml-openrail-m) - [afl-3.07](https://savrn.com/models?license=afl-3.0) - [llama27](https://savrn.com/models?license=llama2) - [osl-3.06](https://savrn.com/models?license=osl-3.0) - [cc0-1.04](https://savrn.com/models?license=cc0-1.0) Access - [Open weights11,087](https://savrn.com/models?access=open_access) - [Access requested at publisher355](https://savrn.com/models?access=gated) Publisher - [Team Mradermacher294](https://savrn.com/models?publisher=mradermacher) - [Yurun Yuan95](https://savrn.com/models?publisher=ryanyr) - [RunningHub.ai94](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 Pu69](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) - [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805) - [Wang49](https://savrn.com/models?publisher=davidwdw) - [Microsoft48](https://savrn.com/models?publisher=microsoft) - [Yurun Yuan48](https://savrn.com/models?publisher=yuruny) - [NVIDIA46](https://savrn.com/models?publisher=nvidia) - [Khazzz1c44](https://savrn.com/models?publisher=khazic) - [Quan44](https://savrn.com/models?publisher=wq2012) - [BAAR43](https://savrn.com/models?publisher=aixk) - [Christopher Subia-Waud42](https://savrn.com/models?publisher=cwaud) [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) 11,442 models, sorted by most downloaded. Model ### [my-embodied-ai](https://savrn.com/models/my-embodied-ai) [Marcin Nowak](https://savrn.com/model-publishers/marcinnowakwyn) This is an exploratory note for Embodied AI. It records the intended comparison, likely confounders, and reproducibility requirements before any benchmark result is reported. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as task-appropriate public benchmarks named in the main note - reproducibility checks, failure modes, and open questions - topic-relevant references Start with summary.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later, they should include dataset versions, commands, seeds, hardware, and raw… Open weights cc-by-4.0 16,576 parameters 512 tokens [View model](https://savrn.com/models/my-embodied-ai) Model ### [my-first-repo](https://savrn.com/models/my-first-repo) [Tom Wang](https://savrn.com/model-publishers/twangprime) Open weights [View model](https://savrn.com/models/my-first-repo) Model · Text generation ### [my-qwen-lora-cisco](https://savrn.com/models/my-qwen-lora-cisco) [SN](https://savrn.com/model-publishers/banhcarrot) A merged (LoRA-finetuned) variant of Qwen/Qwen2.5-1.5B-Instruct, fine-tuned for Cisco-related assistant tasks. This repository contains full merged weights — not a PEFT adapter. Load it directly with transformers; no peft and no adapter merging step is required. The model is tagged endpointscompatible and can be deployed directly to for a dedicated, OpenAI-compatible /v1/chat/completions API. Direct use: [e.g. answering Cisco product, configuration, and troubleshooting questions.] Out of scope: This model is not production-grade for safety- or compliance-critical networking decisions. Verify all configuration and command output before applying it to real hardware. Do not use it as the sole… Open weights apache-2.0 1.5B parameters 32,768 tokens transformers [View model](https://savrn.com/models/my-qwen-lora-cisco) Model ### [my-suno-rapper-8b](https://savrn.com/models/my-suno-rapper-8b) [Sayka](https://savrn.com/model-publishers/dsponhkik) Open weights mit [View model](https://savrn.com/models/my-suno-rapper-8b) Model ### [my_awesome_billsum_model](https://savrn.com/models/my-awesome-billsum-model) [Ataru](https://savrn.com/model-publishers/ataruuu) Open weights [View model](https://savrn.com/models/my-awesome-billsum-model) Model · Fill mask ### [my_awesome_eli5_mlm_model](https://savrn.com/models/my-awesome-eli5-mlm-model) [Ibrahim Samir Elmansoury](https://savrn.com/model-publishers/ibrahimmansoury) This model is a fine-tuned version of distilbert/distilroberta-base on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 8 - evalbatchsize: 8 - lrschedulertype: linear - numepochs: 3 - Transformers 5.17.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights apache-2.0 82M parameters 514 tokens transformers [View model](https://savrn.com/models/my-awesome-eli5-mlm-model) Model ### [my_jev-0.6b](https://savrn.com/models/my-jev-0-6b) [Wt](https://savrn.com/model-publishers/siriusyiyi) Open weights apache-2.0 [View model](https://savrn.com/models/my-jev-0-6b) Model ### [my_merged_models](https://savrn.com/models/my-merged-models) [Mario](https://savrn.com/model-publishers/luc100) Open weights [View model](https://savrn.com/models/my-merged-models) Model ### [myfirstrepo](https://savrn.com/models/myfirstrepo) [Ankit Singhal](https://savrn.com/model-publishers/itforankit) Open weights [View model](https://savrn.com/models/myfirstrepo) Model ### [mymodel](https://savrn.com/models/mymodel) [Mrzhang](https://savrn.com/model-publishers/tunmljn) Access requested at publisher apache-2.0 [View model](https://savrn.com/models/mymodel) Model ### [n1xyb0SIjjCx2hLI](https://savrn.com/models/n1xyb0sijjcx2hli) [14A4](https://savrn.com/model-publishers/14a4) Open weights [View model](https://savrn.com/models/n1xyb0sijjcx2hli) Model ### [n3KsLMTA2e4pFbLL](https://savrn.com/models/n3kslmta2e4pfbll) [14A4](https://savrn.com/model-publishers/14a4) Open weights [View model](https://savrn.com/models/n3kslmta2e4pfbll) Model ### [na0xLvMPNfyu9Hpm](https://savrn.com/models/na0xlvmpnfyu9hpm) [14A4](https://savrn.com/model-publishers/14a4) Open weights [View model](https://savrn.com/models/na0xlvmpnfyu9hpm) Model ### [nacre-judge-v1](https://savrn.com/models/nacre-judge-v1) [Nautilus-org](https://savrn.com/model-publishers/nautilus-compass) Nautilus 独立判读侧现役判分器(LoRA on Qwen3-1.7B)。 - 三态判读 pass / fail / insufficientevidence,J1 binary acc 88.51%(生产评测,ECE 0.072) - 部署纪律(PRECOR B/C 实测 291 题,2026-10-07):只允许 bf16/fp16——fp16 对 bf16 判定一致率 1.0000,int8 0.9828 FAIL,int4 0.9416 FAIL(禁用) - 判定表:docs/metering/PRECORBCVERDICT20261007.md(nautilus-compass 主仓) - 归属:NACRE 机制·判读实例 v1(机制名+实例版本号,组织对齐账 2026-10-06) Open weights apache-2.0 [View model](https://savrn.com/models/nacre-judge-v1) Model · Text to image ### [nanites-keypunch-z-image-1.0](https://savrn.com/models/nanites-keypunch-z-image-1-0) [Nanites](https://savrn.com/model-publishers/nanite-labs) LoRA adapter for Z-Image (Tongyi-MAI, Apache-2.0), fine-tuned on 20 public-domain reference stills of mid-century data instrumentation and paperwork (FSA/OWI, Harris & Ewing, National Photo Company, HABS/HAER surveys) in the idiom of 1930s-1960s U.S. government documentary film stills. Stark black-and-white: punched cards, tabulating machines and card sorters, strip-chart recorders, dial gauges, ledgers, dense tabular reports. Harsh light, deep blacks, film grain. Use keypunch in your prompt to invoke the idiom. See SOURCES.md in the repo for the per-file rights basis. Open weights apache-2.0 diffusers [View model](https://savrn.com/models/nanites-keypunch-z-image-1-0) Model · Text generation ### [nanites-smoky-1b-chat](https://savrn.com/models/nanites-smoky-1b-chat) [Nanites](https://savrn.com/model-publishers/nanite-labs) A Llama-3.2-1B-Instruct model fine-tuned to play the role of an old resident of the Great Smoky Mountains of the 1930s in conversation. You talk to it; the old-timer answers in period voice with regional vocabulary. This is the chat-tuned sibling of the CosyVoice2 gender-pooled speech voices (Earl / male at Nanite-Labs/nanites-smoky-speech-earl, Ethel / female at Nanite-Labs/nanites-smoky-speech-ethel). The chat model provides the language behind the TTS; pair them for a text-and-speech experience. Given a modern-English question about life in the Smokies (work, family, food, music, religion, hardships, the mountains themselves), the model answers in 1-3 sentences in the voice of an old… Open weights apache-2.0 1.2B parameters 131,072 tokens peft [View model](https://savrn.com/models/nanites-smoky-1b-chat) Model · Text generation ### [nanites-smoky-1b-chat-gguf](https://savrn.com/models/nanites-smoky-1b-chat-gguf) [Nanites](https://savrn.com/model-publishers/nanite-labs) Q4KM quant of the merged nanites-smoky-1b-chat model — a Llama-3.2-1B-Instruct fine-tune that plays an old resident of the Great Smoky Mountains of the 1930s. For Ollama / LM Studio / llama.cpp users who don't want the base-model + adapter merge step. Adapter (train output) lives at Nanite-Labs/nanites-smoky-1b-chat; this repo holds the merged + quantized GGUF for direct use. Alpaca-style (### Instruction: / ### Input: / ### Response:). Example prompt Open weights apache-2.0 [View model](https://savrn.com/models/nanites-smoky-1b-chat-gguf) Model · Text generation ### [nanites-whaler-1b-chat](https://savrn.com/models/nanites-whaler-1b-chat) [Nanites](https://savrn.com/model-publishers/nanite-labs) A Llama-3.2-1B-Instruct model fine-tuned to play the role of a 19th-century American whaling first mate in conversation. You talk to it; the mate answers in period voice. This is the chat-tuned sibling of Nanite-Labs/nanites-whaler-1b, which is a text completer that continues 19th-century whaling logbook entries. The chat version is a different animal — don't mix them up. Talk to it like a sailor would talk to the mate on deck. It answers in the voice of a 19th-c. ship's mate, brief and direct, using "try-works", "by D.R.", "the Captain", "no speak of it", etc. Multi-turn is supported — the mate sees your prior Q&A in the context window and can refer to earlier exchanges. period voice) + 8… Open weights apache-2.0 1.2B parameters 131,072 tokens peft [View model](https://savrn.com/models/nanites-whaler-1b-chat) Model · Text generation ### [nanites-whaler-1b-chat-gguf](https://savrn.com/models/nanites-whaler-1b-chat-gguf) [Nanites](https://savrn.com/model-publishers/nanite-labs) Q4KM quant of the merged nanites-whaler-1b-chat model — a Llama-3.2-1B-Instruct fine-tune that plays a 19th-century American whaling first mate. For Ollama / LM Studio / llama.cpp users who don't want the base-model + adapter merge step. Adapter (train output) lives at Nanite-Labs/nanites-whaler-1b-chat; this repo holds the merged + quantized GGUF for direct use. Alpaca-style (### Instruction: / ### Input: / ### Response:). Example prompt Open weights apache-2.0 [View model](https://savrn.com/models/nanites-whaler-1b-chat-gguf) Model · Text generation ### [nanoBeard-sloop-14M](https://savrn.com/models/nanobeard-sloop-14m) [Lo Jahn](https://savrn.com/model-publishers/ihatetomatoes) A tiny pirate-themed GPT trained from scratch on a piratized version of TinyStories, then SFT-tuned. Built as a learning project — closer to nanoGPT than to a production LM. - model.safetensors — model weights. - config.json — architecture config (load into training.config.Config). - piratebpe.json — tokenizer (load with tokenizers.Tokenizer.fromfile). - trainingmetadata.json — full training config + metrics snapshot. - banner.png — the banner above. This model is not a transformers model — it uses the custom GPT class from this repo. - Trained on a small synthetic corpus (TinyStories, piratized). Vocabulary, grammar, and world knowledge are extremely narrow. - Short context window (256… Open weights 14M parameters pytorch [View model](https://savrn.com/models/nanobeard-sloop-14m) Model · Image and text to text ### [nanoVLM-230M-packing-ab](https://savrn.com/models/nanovlm-230m-packing-ab) [Arjun Srinivasan Ambalam](https://savrn.com/model-publishers/arjun1995) Four checkpoints, not one model. This repository is a controlled experiment: two training arms × two seeds, 10,000 steps each, identical in every respect except how packed training rows are masked. The comparison is the artifact. Any single checkpoint here is a weak 230M research model and is not meant for use. Each arm lives on its own branch: Weights are fp32, exactly as trained — these are the tensors every number below was computed from. Optimizer state is not included. nanoVLM packs several unrelated samples into one 4096-token row to avoid padding waste. Upstream's packing applies a single causal mask over the whole row, so a sample attends into the earlier, unrelated samples packed… Open weights apache-2.0 nanovlm [View model](https://savrn.com/models/nanovlm-230m-packing-ab) Model · Text to speech ### [nanobot-channel-voice-models](https://savrn.com/models/nanobot-channel-voice-models) [iChizer0](https://savrn.com/model-publishers/ichizer0) RKNN model artifacts and a checksum-pinned weights index for nanobot-channel-voice. The repository includes four official openWakeWord phrase heads, each available as an original-ONNX package for CPU/ONNX-capable hosts and as a validated RV1126B hybrid package: Choose one variant for the target host. Every fetch is a complete package: ONNX entries install the three original upstream graphs plus a phrase sidecar; RV1126B entries install the phrase head, exact Python-mel filterbank, and static RKNN embedding. The runtime fetches a complete model entry through nanobot-voice: weights-index.json is the authoritative manifest of available keys, source provenance, checksums, and acceptance… Open weights other [View model](https://savrn.com/models/nanobot-channel-voice-models) Model · Text generation ### [nanogpt-shakespeare-char](https://savrn.com/models/nanogpt-shakespeare-char) [Ricardo Jose Dos Santos Cruz](https://savrn.com/model-publishers/ricardojscruz1963) 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 11M parameters transformers [View model](https://savrn.com/models/nanogpt-shakespeare-char) Model · Text generation ### [nanolang-bitnet-b158-700m](https://savrn.com/models/nanolang-bitnet-b158-700m) [Dracko14](https://savrn.com/model-publishers/dracko14) Open weights mit nanow [View model](https://savrn.com/models/nanolang-bitnet-b158-700m) ## 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.