# Models, Page 384 of 477 Source: https://savrn.com/models/page/384 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 ### [occamy-1.0-abliterated-FIT-GGUF](https://savrn.com/models/occamy-1-0-abliterated-fit-gguf) [Scorpio117](https://savrn.com/model-publishers/sc117) Open weights [View model](https://savrn.com/models/occamy-1-0-abliterated-fit-gguf) Model · Image and text to text ### [occamy-1.0-mlx-4bit](https://savrn.com/models/occamy-1-0-mlx-4bit) [Ray](https://savrn.com/model-publishers/tozp) Open weights apache-2.0 35.1B parameters transformers [View model](https://savrn.com/models/occamy-1-0-mlx-4bit) Model ### [occamy-1.0-with-oQ5e-mtp](https://savrn.com/models/occamy-1-0-with-oq5e-mtp) [Nathan Anderson](https://savrn.com/model-publishers/andosen) Open weights [View model](https://savrn.com/models/occamy-1-0-with-oq5e-mtp) Model ### [ocr-freeform](https://savrn.com/models/ocr-freeform) [Liam Foster](https://savrn.com/model-publishers/jimenezdaniel) This repository contains a working research note about OCR Freeform. It organizes motivation, related work, a falsifiable hypothesis, and an evaluation plan. It is not presented as a completed paper or a release of trained models. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as FUNSD, SROIE, and CORD - 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… Open weights mit 49,600 parameters 512 tokens [View model](https://savrn.com/models/ocr-freeform) Model ### [ocr-freeform-test](https://savrn.com/models/ocr-freeform-test) [Jungkookjeon1994](https://savrn.com/model-publishers/jungkookjeon1994) This repository contains a working research note about OCR Freeform. It organizes motivation, related work, a falsifiable hypothesis, and an evaluation plan. It is not presented as a completed paper or a release of trained models. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as FUNSD, SROIE, and CORD - reproducibility checks, failure modes, and open questions - topic-relevant references Start with analysis.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… Open weights cc-by-4.0 16,576 parameters 128 tokens [View model](https://savrn.com/models/ocr-freeform-test) Model ### [ocr-freeform-v2](https://savrn.com/models/ocr-freeform-v2) [N. Ramirez](https://savrn.com/model-publishers/noramirez) This is an exploratory note for OCR Freeform. 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 FUNSD, SROIE, and CORD - reproducibility checks, failure modes, and open questions - topic-relevant references Start with analysis.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 logs. The note is intentionally… Open weights mit 16,576 parameters 128 tokens [View model](https://savrn.com/models/ocr-freeform-v2) Model · Robotics ### [october17-oer10-joint-urma4-fullft-22k](https://savrn.com/models/october17-oer10-joint-urma4-fullft-22k) [Shrg7](https://savrn.com/model-publishers/shrg7) Open weights [View model](https://savrn.com/models/october17-oer10-joint-urma4-fullft-22k) Model · Speech recognition ### [octopus-asr-omniASR-1B-v1.1-indic](https://savrn.com/models/octopus-asr-omniasr-1b-v1-1-indic) [Some](https://savrn.com/model-publishers/guruawe) Fine-tuned omniASRLLM1Bv2 (run 3, step 20,000) blended with the base model at α = 0.85 (WiSE-FT weight interpolation). Use this model for monolingual Indian-language speech. English, Hinglish / code-mixed speech and all other languages are served by v1 (same run, α = 0.5). The α = 0.7 version of this file is in the repo history. α 1.0 was rejected: FLEURS dev (63 languages) 13.63 vs 13.51 for α 0.5, 38 languages worse, and the worst Hinglish / Indian-English scores. Paired bootstrap, α 0.85 vs α 0.7 on IndicVoices dev: −2.00 [−3.12, −1.15], P(better) = 1.00. \ Same-domain: training included IndicVoices train audio. Full α 0.85 test halves and FLEURS-Indic are scored after the current GPU… Access requested at publisher other [View model](https://savrn.com/models/octopus-asr-omniasr-1b-v1-1-indic) Model · Text classification ### [od1-nano](https://savrn.com/models/od1-nano) [Balaji](https://savrn.com/model-publishers/mvbalaji) OD-1 is an open System One decision model: give it a state (text or JSON) and typed questions (choice, noul = yes/no, score = ordinal) and it returns every answer from one forward pass, with full probability distributions including an explicit NOTANSWERABLE outcome. The request/response shape follows the Jev / TypeSafe /v1/systemone wire format. Backbone: Qwen/Qwen3.5-0.8B (Apache-2.0) with typed answer heads read at marker tokens, early-exit heads (layers 6/12/18), and a CLM-style contrastive shortlist head for questions with more than 24 options. Trained with SFT + distillation from Qwen3.5-27B-FP8 (never a compared system), RLCD-style proper-score calibration, an abstention utility… Open weights apache-2.0 transformers [View model](https://savrn.com/models/od1-nano) Model · Text classification ### [od1-nano-lite](https://savrn.com/models/od1-nano-lite) [Balaji](https://savrn.com/model-publishers/mvbalaji) OD-1 is an open System One decision model: give it a state (text or JSON) and typed questions (choice, noul = yes/no, score = ordinal) and it returns every answer from one forward pass, with full probability distributions including an explicit NOTANSWERABLE outcome. The request/response shape follows the Jev / TypeSafe /v1/systemone wire format. Backbone: Qwen/Qwen3.5-0.8B (Apache-2.0) with typed answer heads read at marker tokens, early-exit heads (layers 6/12/18), and a CLM-style contrastive shortlist head for questions with more than 24 options. Trained with SFT + distillation from Qwen3.5-27B-FP8 (never a compared system), RLCD-style proper-score calibration, an abstention utility… Open weights apache-2.0 transformers [View model](https://savrn.com/models/od1-nano-lite) Model · Text classification ### [od1-nano-typed-decisions](https://savrn.com/models/od1-nano-typed-decisions) [Balaji](https://savrn.com/model-publishers/mvbalaji) OD-1 is an open System One decision model: give it a state (text or JSON) and typed questions (choice, noul = yes/no, score = ordinal) and it returns every answer from one forward pass, with full probability distributions including an explicit NOTANSWERABLE outcome. The request/response shape follows the Jev / TypeSafe /v1/systemone wire format. Backbone: Qwen/Qwen3.5-0.8B (Apache-2.0) with typed answer heads read at marker tokens, early-exit heads (layers 6/12/18), and a CLM-style contrastive shortlist head for questions with more than 24 options. Trained with SFT + distillation from Qwen3.5-27B-FP8 (never a compared system), RLCD-style proper-score calibration, an abstention utility… Open weights apache-2.0 transformers [View model](https://savrn.com/models/od1-nano-typed-decisions) Model · Image and text to text ### [oddeye-27b-nvfp4](https://savrn.com/models/oddeye-27b-nvfp4) [Minorufujino](https://savrn.com/model-publishers/finalvent) One street photo in, a structured Japanese observation record out. OddEye is the street-reading model of michiyomi. It is built on Qwen3.8-27B and compressed to 4-bit (NVFP4, about 20 GB), so a single GPU — even an RTX 5090 — can run it. It keeps what it sees (observations) and what it infers (interpretations, each with a confidence label) in separate fields. The record has 13 top-level fields, including objects, text on signs, relations, surfaces, standard road facets (lanes, sidewalks, lighting…), interpretations and a scene summary. All numbers use the recommended prompt (v5.2c), greedy decoding and the same photos for every model. "Generic NVFP4" is a publicly distributed 4-bit version… Open weights apache-2.0 27.4B parameters 262,144 tokens vllm [View model](https://savrn.com/models/oddeye-27b-nvfp4) Model ### [odyssey-models](https://savrn.com/models/odyssey-models) [ADRLAB](https://savrn.com/model-publishers/adrlab) Checkpoints of the end-to-end driving planners evaluated in the Odyssey closed-loop benchmark. - baseline: the upstream model. It takes NAVSIM's drivingcommand and has no route. - sdroute: drivingcommand is removed, and a standard-definition (SD) route becomes the only navigation signal. - The code that loads and runs these checkpoints is OdysseyZoo. A link will be added here once it is public. - The closed-loop scenes are in ADRLAB/odyssey-scenes. - The simulator's image restorer (Fixer, fine-tuned from NVIDIA Fixer) is in fixer/. See Fixer. Paths match the OdysseyZoo tree, so the files land in place when you download into an OdysseyZoo checkout. - Every planner checkpoint was trained on… Open weights other [View model](https://savrn.com/models/odyssey-models) Model · Reinforcement learning ### [ogpo-dd-checkpoints](https://savrn.com/models/ogpo-dd-checkpoints) [Shashwat Saxena](https://savrn.com/model-publishers/shashwatsaxena136) Complete code and saved checkpoints for Square and ToolHang, seed 1, comparing current-actor confidence ordering with random position ordering. Success buffer is disabled in both selected variants. No license grant is implied by this export. - checkpoints/ / /: saved policies, identical per-task BC anchors, latest full replay buffer and rolling metadata, metric CSVs, and model weights. - analysisinputs/: a fixed 4096-observation sample per task from the earlier UMAP snapshot (2048 per variant, seed 42), for shared-input representation comparisons. Only optimizer-state dictionaries (optstate) are removed from model checkpoints. Current actor, target actor, current critic, target critic… Open weights [View model](https://savrn.com/models/ogpo-dd-checkpoints) Model · Time series forecasting ### [ohlcv-transformer](https://savrn.com/models/ohlcv-transformer) [Suryaansh Prithvijit Singh](https://savrn.com/model-publishers/ultron28) Engineered & Architected by Suryaansh Prithvijit Singh (@Ultron28 / @Ultron09) The ULTRON Financial Conformer (1.189B) is a large-scale foundation model specifically designed to ingest raw minute-level OHLCV candlestick time series across liquid US equities and generate multi-horizon continuous returns and directional probability forecasts. Unlike conventional time-series transformers (e.g. PatchTST, Chronos, TimesFM) that treat financial candlesticks as 1D scalar sequences, this architecture treats the microstructure as a continuous multi-scale physical manifold. It combines: 1. 6-Scale Temporal Inception Tokenizer: Continuous 1D convolutions spanning $[3\text{m}, 7\text{m}, 15\text{m}… Open weights apache-2.0 pytorch [View model](https://savrn.com/models/ohlcv-transformer) Model · Image to text ### [ohmyshot-ppformulanet-plus-l-coreml](https://savrn.com/models/ohmyshot-ppformulanet-plus-l-coreml) [Moraxcheng](https://savrn.com/model-publishers/moraxcheng) PaddlePaddle's PP-FormulaNetplus-L, which reads a picture of a formula as LaTeX, converted to Core ML ML programs with 8-bit weights for OhMyShot, a screenshot app for macOS, which downloads them as a model pack to copy the formula in a selection as LaTeX. The weights are PaddlePaddle's, quantized; see LICENSE.txt for the license and what was changed. Decoding starts from token 2 at position 0 and ends when the decoder gives 2. Run on the CPU (.cpuOnly). Made with Tools/model-packs/formulapack.py from this upstream revision: - https://huggingface.co/PaddlePaddle/PP-FormulaNetplus-Lsafetensors/tree/6cc004a8e91ea3f3819b05317f40b290cbe99c2b Checked against the OhMyShot formula corpus… Open weights apache-2.0 coreml [View model](https://savrn.com/models/ohmyshot-ppformulanet-plus-l-coreml) Model · Text generation ### [old-wb-l40s-lora-weights](https://savrn.com/models/old-wb-l40s-lora-weights) [WMTH-100d2exam](https://savrn.com/model-publishers/wmth-100d2exam) The model weights of the LoRA / QLoRA runs done on the L40S server during the Warsaw Model Trainers Hackathon (25-26.09.2026): PEFT adapters, Trainer checkpoints (with optimizer state), lora.gguf adapters for llama.cpp and trainingargs.bin. These are adapters, not full models. Every run uses the base google/gemma-4-12B-it-qat-q40-unquantized (the local path in adapterconfig.json points to a server copy of it). Layout: l40s-results/ /..., the same paths as oldwb/l40s-results/ in the GitHub repo Endote/WarsawModelTrainersHackathon, which holds the logs, metrics, data statistics and write-ups (README.md, EXPERIMENTS.md, BENCHMARKSRESEARCH.md). Restore into a checkout of that repo: python… Open weights apache-2.0 peft [View model](https://savrn.com/models/old-wb-l40s-lora-weights) Model · Reinforcement learning ### [old_checkpoints](https://savrn.com/models/old-checkpoints) [Jaemoo Choi](https://savrn.com/model-publishers/toony42) Open weights apache-2.0 [View model](https://savrn.com/models/old-checkpoints) Model ### [olens_ablations](https://savrn.com/models/olens-ablations) [Agam Bhatia](https://savrn.com/model-publishers/agu18dec) Every arm here differs from the recipe of record by exactly one flag. Data, pool, crop seed, layer set, learning rate, batch and seeds are held fixed across arms so the only free variable is the one named. The whitened-FVE ruler that the program optimises and reports anti-correlates with lens quality. The AR trained without whitening scores far worse on the ruler and makes a materially better lens, and running both to saturation does not rescue it. Compare arms only at MATCHED cumulative steps. Arms stopped early by a budget cut have lower totals, and reading their endpoints against a fully-trained arm inverts the ordering: unitcos looks worse than base on endpoints but is identical to it… Open weights [View model](https://savrn.com/models/olens-ablations) Model ### [olmo-compassion-web-s3407-part1-reasoning-gatelogs](https://savrn.com/models/olmo-compassion-web-s3407-part1-reasoning-gatelogs) [Compassion aligned machine learning](https://savrn.com/model-publishers/compassioninmachinelearning) Open weights [View model](https://savrn.com/models/olmo-compassion-web-s3407-part1-reasoning-gatelogs) Model ### [olmo-urban-part1](https://savrn.com/models/olmo-urban-part1) [Compassion aligned machine learning](https://savrn.com/model-publishers/compassioninmachinelearning) Open weights 7.3B parameters 65,536 tokens [View model](https://savrn.com/models/olmo-urban-part1) Model ### [olmo-urban-s1-part1-gatelogs](https://savrn.com/models/olmo-urban-s1-part1-gatelogs) [Compassion aligned machine learning](https://savrn.com/model-publishers/compassioninmachinelearning) Open weights [View model](https://savrn.com/models/olmo-urban-s1-part1-gatelogs) Model ### [olmo-urban-web-seed1-CPT-best](https://savrn.com/models/olmo-urban-web-seed1-cpt-best) [Compassion aligned machine learning](https://savrn.com/model-publishers/compassioninmachinelearning) Open weights 7.3B parameters 65,536 tokens [View model](https://savrn.com/models/olmo-urban-web-seed1-cpt-best) Model · Text generation ### [olmo3-190m-zh-full-continue](https://savrn.com/models/olmo3-190m-zh-full-continue) [Lee](https://savrn.com/model-publishers/mikelee000) 基于 MikeLee000/olmo3-190m-zh-full 完成一轮持续预训练（Continued Pretraining，CPT）的课程实验模型，约 187M 参数。 不是 SFT 或指令对话模型。 使用普通文本作为提示进行续写，不使用 applychattemplate，不应将其作为可靠的问答助手。 - 架构：Olmo3ForCausalLM，12 层，hidden size 768，intermediate size 3072，词表 48,000。 - 数据：cmz1024/llm101-olmo3-zh-demo-data 仓库中的 continue/tokenizedv31.bin。 - 全参数持续预训练；不使用 LoRA；序列长度 2,048；BF16。 - 1 epoch，共 11,918 个 optimizer steps。 - Batch size 16，gradient accumulation 4，有效 batch size 64。 - Learning rate 2e-4，warmup ratio 0.1，cosine scheduler。 - Transformers 5.9.0，PyTorch 2.5.1。 - 最终 validation loss：1.9467759132385254。这是特定验证集上的语言模型损失，不是准确率或通用问答质量评分。 发布文件来自 /checkpoints/olmo3zhcontinue/final，已核对与 checkpoint-11918/model.safetensors 的 SHA-256 一致：… Open weights apache-2.0 187M parameters 4,096 tokens transformers [View model](https://savrn.com/models/olmo3-190m-zh-full-continue) ## 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.