# Open-Weight Models Under Apache License 2.0, Page 48 Source: https://savrn.com/models/licenses/apache-2-0/page/48 Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- SAVRN Model Hub · Models by License # Open-Weight Models Under Apache License 2.0 3,285 open-weight models released under Apache License 2.0 in the SAVRN Model Hub, with Team Mradermacher, Qwen and Google publishing the most. 3,285Models 1587Publishers 12K to 952.4BParameter range 1Licenses YesCommercial use ## What Apache License 2.0 Allows The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors. [Read the license text](https://www.apache.org/licenses/LICENSE-2.0). ## SAVRN's Take Apache 2.0 asks little of the operator. Commercial use, modification and redistribution are all permitted. What it requires is housekeeping: keep the license and copyright notices, carry any NOTICE file along with the weights, and state the significant changes if you pass a modified version on. It also includes an express patent grant from contributors, the clause a procurement reviewer looks for before a model serves paying customers. So an organization can take the files from the publisher, fine-tune on its own data, run the result in its own facility and bill for the output. On our hub 1,018 models carry this license. Qwen leads with 81, Google 50, Convergent Intelligence 39, PyTorch Image Models 32, OpenMed 31, Helsinki-NLP Research Group 27, and Unsloth AI and PaddlePaddle 22 each. Text generation accounts for 164 of the models, image and text to text for 89, and robotics for 43, so it covers more than chat. Downloads show where it runs. all-MiniLM-L6-v2 from Sentence Transformers, a 23M parameter sentence similarity model, pulls 255,050,544 downloads a month and needs 0.1 GB at 16-bit, small enough to ride on a GPU already serving. ms-marco-MiniLM-L6-v2 follows at 88,642,387 for text ranking. The one text generation model in the top eight is Qwen3-0.6B at 22,498,727 downloads a month: 752M parameters, a 40,960 token context, 1.8 GB at 16-bit, and the cheapest host on the Index for it is one MI300X at $1.85 an hour. SAVRN Research, 2026-09-18 ## Most Downloaded | Model | Publisher | Parameters | License | Monthly downloads | Cheapest GPUs at 16-bit | | --- | --- | --- | --- | --- | --- | | [all-MiniLM-L6-v2](https://savrn.com/models/all-minilm-l6-v2) | Sentence Transformers | 23M | apache-2.0 | 203.2M | 1x MI300X, $1.85/hr | | [ms-marco-MiniLM-L6-v2](https://savrn.com/models/ms-marco-minilm-l6-v2) | Sentence Transformers - Cross-Encoders | 23M | apache-2.0 | 74.7M | 1x MI300X, $1.85/hr | | [paraphrase-multilingual-MiniLM-L12-v2](https://savrn.com/models/paraphrase-multilingual-minilm-l12-v2) | Sentence Transformers | 118M | apache-2.0 | 43M | 1x MI300X, $1.85/hr | | [electra-base-discriminator](https://savrn.com/models/electra-base-discriminator) | Google | — | apache-2.0 | 41M | — | | [bert-base-uncased](https://savrn.com/models/bert-base-uncased) | BERT community | 110M | apache-2.0 | 29.7M | 1x MI300X, $1.85/hr | | [Qwen3-0.6B](https://savrn.com/models/qwen3-0-6b) | Qwen | 752M | apache-2.0 | 28.2M | 1x MI300X, $1.85/hr | | [t5-small](https://savrn.com/models/t5-small) | T5 community | 61M | apache-2.0 | 24.5M | 1x MI300X, $1.85/hr | | [mobilenetv3_small_100.lamb_in1k](https://savrn.com/models/mobilenetv3-small-100-lamb-in1k) | PyTorch Image Models | 3M | apache-2.0 | 18.5M | 1x MI300X, $1.85/hr | | [all-mpnet-base-v2](https://savrn.com/models/all-mpnet-base-v2) | Sentence Transformers | 109M | apache-2.0 | 18.3M | 1x MI300X, $1.85/hr | | [wav2vec2-large-xlsr-53-japanese](https://savrn.com/models/wav2vec2-large-xlsr-53-japanese) | Jonatas Grosman | — | apache-2.0 | 16.2M | — | ## Who Publishes Them | Publisher | Models | | --- | --- | | [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) | 162 | | [Qwen](https://savrn.com/model-publishers/qwen) | 81 | | [Google](https://savrn.com/model-publishers/google) | 50 | | [Quan](https://savrn.com/model-publishers/wq2012) | 44 | | [RKNNAI](https://savrn.com/model-publishers/rknnai) | 43 | | [Convergent Intelligence](https://savrn.com/model-publishers/reaperdoesntknow) | 39 | ## All 3,285 Models, Page 48 of 55 Model ### [qwen36-27b-sae2m-l42](https://savrn.com/models/qwen36-27b-sae2m-l42) [De Schamphelaere](https://savrn.com/model-publishers/ceselder) A dictionarylearning-format BatchTopK sparse autoencoder on the post-layer-42 residual stream of Qwen/Qwen3.6-27B (d = 5120), with F = 2,097,152 (2^21, 409.6x) features and k = 64. Companion to the 131,072-feature SAE at ceselder/qwen36-27b-sae-l42 (same layer, same recipe, 16x smaller dictionary). BatchTopK (Bussmann et al.), dictionarylearning-style trainer run feature-parallel over 8 GPUs (each rank owns 262,144 features; global top-k via all-gather, all-reduced reconstruction, aux-k loss alpha 1/32 with 40,960 aux features, learned activation threshold with EMA beta 0.999). lr 1.77e-5, warm-up 1,000 steps, decay from step 195,312, batch 4,096 tokens x 244,140 steps = 1.0e9 tokens… Open weights apache-2.0 [View model](https://savrn.com/models/qwen36-27b-sae2m-l42) Model · Image and text to text ### [qwen38-27b-swerebench-grpo-step8](https://savrn.com/models/qwen38-27b-swerebench-grpo-step8) [Haixin Nan](https://savrn.com/model-publishers/wanna0720) This is a Hugging Face safetensors export of the completed Step 8 full-parameter GRPO checkpoint. The language-model tensors are converted from the Miles/Megatron training checkpoint. The visual and MTP tensors, tokenizer, configuration, and processor assets come from Qwen/Qwen3.8-27B at revision The run trained on 1,586 SWE-rebench v2 Filtered tasks using independent mini-swe-agent rollouts. It used eight NVIDIA B300 GPUs (TP=2, CP=4), 32 prompt groups with eight rollouts each per optimizer step, global batch size 256, and learning rate 2e-6. This is the no-branch GRPO baseline checkpoint, not a branching-policy checkpoint. Source training-checkpoint completion marker SHA-256… Open weights apache-2.0 transformers [View model](https://savrn.com/models/qwen38-27b-swerebench-grpo-step8) Model ### [qwen38-27b-swerebench-grpo-step8-training-state](https://savrn.com/models/qwen38-27b-swerebench-grpo-step8-training-state) [Haixin Nan](https://savrn.com/model-publishers/wanna0720) This repository preserves the completed Step 8 checkpoint from a full-parameter GRPO run using a 1,586-task SWE-rebench v2 Filtered corpus. It is a Miles/Megatron distributed checkpoint, including model and optimizer state, metadata, and the dataset cursor. It is intended for research recovery and continued training. The model weights for direct inference are published separately as a Hugging Face safetensors export. The archived run used eight NVIDIA B300 GPUs with tensor parallelism 2 and context parallelism 4. Each step requested 32 prompt groups with eight rollouts per group (global batch size 256); the learning rate was 2e-6. Training used mini-swe-agent, independent sandboxes and… Open weights apache-2.0 [View model](https://savrn.com/models/qwen38-27b-swerebench-grpo-step8-training-state) Model · Text generation ### [qwen38-40b-prune](https://savrn.com/models/qwen38-40b-prune) [DavidB](https://savrn.com/model-publishers/davd-b01) One upstream MoE, six generations of masks, one bit-exact surgery, a rebuilt factual memory, and a three-stage post-training (SFT → SimPO → RLVR) — with every error I made and every idea I falsified along the way. This repository is the complete technical record of the project: the activation-instrumentation artifacts, the mask-generation methodology, the surgery code, the PLE (n-gram memory) rebuild, the trainers, and the full lab notebook. The champion weights live in sibling repos What I set out to do: take Qwen3.8-Flash-Next — an ~180B-class MoE (~130B of core network + 51B of n-gram memory table + 4B MTP) — and cut it by 75% of its experts (512 → 128) while conserving the maximum… Open weights apache-2.0 transformers [View model](https://savrn.com/models/qwen38-40b-prune) Model ### [qwen38-dspark-moe-256exp](https://savrn.com/models/qwen38-dspark-moe-256exp) [Yusheng Su](https://savrn.com/model-publishers/yushengsu) 5-layer DSpark MoE drafter for Qwen3.8-27B with 256 routed experts (width 512, top-10) and one shared expert (2048): Kan Wu's kan/moe-3-qwen38 SpecForge recipe with half the experts, trained from scratch for 3 epochs on RadixArk/Qwen3.8-27B-Regen-Mixture-v1 (global batch 256, lr 5e-4 constant) on one 4x GB300 node. Each finished epoch is a complete servable SGLang export in its own folder: Training curves: https://wandb.ai/milestraining/specforge-5layer-moe-0.5%2Aexperts/runs/qwen38-dspark-moe-256exp-3ep-gb300 Recipe and config: branch exp/qwen38-dspark-moe-256exp-3ep-gb300 of https://github.com/yushengsu-thu/SpecForge Open weights apache-2.0 [View model](https://savrn.com/models/qwen38-dspark-moe-256exp) Model ### [qwen38-dspark-moe-2layer](https://savrn.com/models/qwen38-dspark-moe-2layer) [Yusheng Su](https://savrn.com/model-publishers/yushengsu) 2-layer MoE DSpark draft models for Qwen3.8-27B: an ablation of Kan Wu's 5-layer Qwen3.8-27B DSpark MoE drafter (SpecForge branch kan/moe-3-qwen38) with the draft reduced from 5 to 2 decoder layers and nothing else changed (512 routed experts x 512 wide, top-10 softmax routing, sigmoid-gated shared expert of width 2048, router centering; 8.48B total / about 1.08B active parameters). One servable export per training epoch, each in its own folder (config.json, model.safetensors, tokenizer and a README with the training metrics of that checkpoint): Serving needs an SGLang build with the Qwen3MoeDSparkModel draft class (upstream SGLang 0.5.18 only ships the dense Qwen3DSparkModel). Download one… Open weights apache-2.0 [View model](https://savrn.com/models/qwen38-dspark-moe-2layer) Model ### [qwen38-dspark-moe-2layer-1ep](https://savrn.com/models/qwen38-dspark-moe-2layer-1ep) [Yusheng Su](https://savrn.com/model-publishers/yushengsu) Placeholder. The servable export of the 2-layer Qwen3.8-27B DSpark MoE drafter after epoch 1 will be uploaded here automatically when that checkpoint is written. Live training metrics: https://wandb.ai/milestraining/specforge-2%20layer%20moe Code and recipes: https://github.com/yushengsu-thu/SpecForge/tree/exp/qwen3.8-27b-dspark-moe-2layer Open weights apache-2.0 [View model](https://savrn.com/models/qwen38-dspark-moe-2layer-1ep) Model ### [qwen38-dspark-moe-2layer-2ep](https://savrn.com/models/qwen38-dspark-moe-2layer-2ep) [Yusheng Su](https://savrn.com/model-publishers/yushengsu) Placeholder. The servable export of the 2-layer Qwen3.8-27B DSpark MoE drafter after epoch 2 will be uploaded here automatically when that checkpoint is written. Live training metrics: https://wandb.ai/milestraining/specforge-2%20layer%20moe Code and recipes: https://github.com/yushengsu-thu/SpecForge/tree/exp/qwen3.8-27b-dspark-moe-2layer Open weights apache-2.0 [View model](https://savrn.com/models/qwen38-dspark-moe-2layer-2ep) Model ### [qwen38-dspark-moe-2layer-3ep](https://savrn.com/models/qwen38-dspark-moe-2layer-3ep) [Yusheng Su](https://savrn.com/model-publishers/yushengsu) Placeholder. The servable export of the 2-layer Qwen3.8-27B DSpark MoE drafter after epoch 3 will be uploaded here automatically when that checkpoint is written. Live training metrics: https://wandb.ai/milestraining/specforge-2%20layer%20moe Code and recipes: https://github.com/yushengsu-thu/SpecForge/tree/exp/qwen3.8-27b-dspark-moe-2layer Open weights apache-2.0 [View model](https://savrn.com/models/qwen38-dspark-moe-2layer-3ep) Model ### [Qwen38Jrell-MTP-256K](https://savrn.com/models/qwen38jrell-mtp-256k) [Kyounghoon Lee](https://savrn.com/model-publishers/willyesiam) This repository is an Ollama deployment package for Qwen3.8 27B. It combines the jrell IQ4XS-style main quantization with a separate Q40 MTP draft model and targets a 262,144-token (256K) context window. This is a packaging and runtime-configuration project, not a new training run, fine-tune, merge, or official Qwen release. It does not claim ownership of the upstream model or quantizations. The repository contains scripts, configuration, attribution, and documentation. It does not contain the large GGUF weights. install.ps1 downloads those artifacts from their canonical upstream repositories at install time, so this repository can be renamed and updated without re-uploading multi-GB files.… Open weights apache-2.0 gguf [View model](https://savrn.com/models/qwen38jrell-mtp-256k) Model · Text generation ### [qwen3p5-0p8b-medical-mlp5](https://savrn.com/models/qwen3p5-0p8b-medical-mlp5) [Aditya G](https://savrn.com/model-publishers/adityag-india) Qwen/Qwen3.5-0.8B fine-tuned directly (no LoRA/adapters) on medical reasoning data, to turn a general-purpose model into a domain-focused one. Overfitting to the training data was an accepted trade-off of this experiment; the goal was to study the training/validation loss behaviour. Loss is computed on the assistant response only (reasoning + answer); the prompt is masked. Best validation loss at epoch. See losscurve.png for train vs validation loss. - Research into domain adaptation, partial fine-tuning, and overfitting behaviour of small models. - Offline experimentation with medical-style question answering and reasoning formats. - Clinical decision support, diagnosis, treatment… Open weights apache-2.0 752M parameters 262,144 tokens transformers [View model](https://savrn.com/models/qwen3p5-0p8b-medical-mlp5) Model · Image and text to text ### [qwen3p6-35b-a3b-pin-2p02bit-r46](https://savrn.com/models/qwen3p6-35b-a3b-pin-2p02bit-r46) [MinJae Choi](https://savrn.com/model-publishers/minjaechoi) Following the February release of the Qwen3.5 series, we're pleased to share the first open-weight variant of Qwen3.6. Built on direct feedback from the community, Qwen3.6 prioritizes stability and real-world utility, offering developers a more intuitive, responsive, and genuinely productive coding experience. This release delivers substantial upgrades, particularly in For more details, please refer to our blog post Qwen3.6-35B-A3B. Empty cells (--) indicate scores not available or not applicable. For streamlined integration, we recommend using Qwen3.6 via APIs. Below is a guide to use Qwen3.6 via OpenAI-compatible API. Qwen3.6 can be served via APIs with popular inference frameworks. In… Open weights apache-2.0 36B parameters 262,144 tokens transformers [View model](https://savrn.com/models/qwen3p6-35b-a3b-pin-2p02bit-r46) Model ### [qwen3vl_8b_w4a8_lmhead4bit](https://savrn.com/models/qwen3vl-8b-w4a8-lmhead4bit) [Gordon Z](https://savrn.com/model-publishers/gordon-zou) Open weights apache-2.0 [View model](https://savrn.com/models/qwen3vl-8b-w4a8-lmhead4bit) Model ### [Qwimi-4B-i1-GGUF](https://savrn.com/models/qwimi-4b-i1-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) weighted/imatrix quants of https://huggingface.co/Rumiii/Qwimi-4B For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/Qwimi-4B-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/qwimi-4b-i1-gguf) Model ### [Qwopus3-5-9B-Coder-Flash-GGUF](https://savrn.com/models/qwopus3-5-9b-coder-flash-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) static quants of https://huggingface.co/Jackrong/Qwopus3-5-9B-Coder-Flash For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. 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… Open weights apache-2.0 transformers [View model](https://savrn.com/models/qwopus3-5-9b-coder-flash-gguf) Model ### [Qwopus3.8-27B-Flash-V2-Apostate-Uncensored-GGUF](https://savrn.com/models/qwopus3-8-27b-flash-v2-apostate-uncensored-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) static quants of https://huggingface.co/veldierin/Qwopus3.8-27B-Flash-V2-Apostate-Uncensored For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. 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… Open weights apache-2.0 transformers [View model](https://savrn.com/models/qwopus3-8-27b-flash-v2-apostate-uncensored-gguf) Model · Text to image ### [r3iko2](https://savrn.com/models/r3iko2) [Mike](https://savrn.com/model-publishers/mej023) A DreamBooth-LoRA for Krea 2, trained on Krea 2 RAW and shown on Krea 2 Turbo. The samples below were generated with this LoRA on Turbo (8 steps). Use the token r3iko2 to invoke the concept. Open weights apache-2.0 diffusers [View model](https://savrn.com/models/r3iko2) Model ### [radamn-ai-v1](https://savrn.com/models/radamn-ai-v1) [Wellerson Blay](https://savrn.com/model-publishers/luffynox) This llama model was trained 2x faster with Unsloth Open weights apache-2.0 transformers [View model](https://savrn.com/models/radamn-ai-v1) Model ### [radiochat-laya-multilingual-onnx](https://savrn.com/models/radiochat-laya-multilingual-onnx) [Shiva Prakash](https://savrn.com/model-publishers/foreverstorm) A browser-ready export of convaiinnovations/laya-multilingual (revision e4e9ddf21a7b1903b7acffd8814ad4307bf63a67), the multilingual checkpoint of Laya by Convai Innovations — a non-autoregressive decision model that answers typed questions (choice, score, yes/no) in one forward pass. All credit for the model goes to its authors; see the Laya repository. Licensed Apache-2.0, like the original. Used by RadioChat to moderate chat messages on the user's own device. The split layout that laya-ts loads: 1. laya-ts/scripts/exportonnx.py --repo convaiinnovations/laya-multilingual (laya commit 9d95567), which checks the ONNX outputs against PyTorch (max logit difference ~2e-6). 2. ONNX Runtime 1.30… Open weights apache-2.0 onnx [View model](https://savrn.com/models/radiochat-laya-multilingual-onnx) Model · Reinforcement learning ### [ragen-frozenlake_05b_instruct_ppo100_20261018_020449](https://savrn.com/models/ragen-frozenlake-05b-instruct-ppo100-20261018-020449) [Xiaoyi Wang](https://savrn.com/model-publishers/xiaoyou77527) Base model: Qwen/Qwen2.5-0.5B-Instruct. Environment: 4x4 FrozenLake, p=0.8 safe-tile probability, no slipping, grid and coordinates. PPO/GAE, no rollout filtering. at most 2 actions per turn, response limit 128 tokens. Actor LR 1e-6, critic LR 1e-5, entropy coefficient 0.001. See results/config.yaml for all settings. Validation: fixed 64 seeds (123 through 186), greedy decoding, before training and every 10 steps. Training maps start at seed 10000. One training run only. W&B: https://wandb.ai/xiaoyiwang2027-northwestern-university/ragenpilotfrozenlake/runs/8baf50af33eb Best saved TRAINED checkpoint by validation success, then reward: step0030 Each completed HF checkpoint is under… Open weights apache-2.0 [View model](https://savrn.com/models/ragen-frozenlake-05b-instruct-ppo100-20261018-020449) Model ### [Rainbow-AI-Specialist-MLC](https://savrn.com/models/rainbow-ai-specialist-mlc) [Herish Kondeti](https://savrn.com/model-publishers/rainbowlord) Rainbow-AI-Specialist is a custom multi-domain fine-tuned AI model based on google/gemma-2-2b-it, trained using QLoRA via Hugging Face TRL (SFTTrainer). You can load and test this adapter easily in Python using PEFT and Transformers: import torch from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel modelid = "google/gemma-2-2b-it" tokenizer = AutoTokenizer.frompretrained(modelid) basemodel = AutoModelForCausalLM.frompretrained(modelid, torchdtype=torch.float16, devicemap="auto") model = PeftModel.frompretrained(basemodel, "RainbowLord/Rainbow-AI-Specialist") inputs = tokenizer("### User: Write a Python script for a simple trading bot.\n### Rainbow:"… Open weights apache-2.0 [View model](https://savrn.com/models/rainbow-ai-specialist-mlc) Model · Text generation ### [Raon-1-mini-preview](https://savrn.com/models/raon-1-mini-preview) [KRAFTON](https://savrn.com/model-publishers/krafton) Access requested at publisher apache-2.0 28.8B parameters transformers [View model](https://savrn.com/models/raon-1-mini-preview) Model · Text generation ### [RARE-Negative](https://savrn.com/models/rare-negative) [Maggie](https://savrn.com/model-publishers/eurekatian) Open weights apache-2.0 transformers [View model](https://savrn.com/models/rare-negative) Model · Text generation ### [RavenX-CyberAgent-Qwen3.6-35B-A3B-Splash-Q4](https://savrn.com/models/ravenx-cyberagent-qwen3-6-35b-a3b-splash-q4) [Yanni Pang](https://savrn.com/model-publishers/yannipang) RavenX-CyberAgent-Qwen3.6-35B-A3B converted to the Splash schema-4 splash-packed-q4-moe format for Apple Silicon. This repository is a Splash runtime package, not an MLX checkpoint, GGUF file, or Transformers checkpoint. The target and tokenizer come from the which derives from the The compatible DFlash 2 draft and vision weights are reused from All 40 converted language-model layer files were built from RavenX's weights. The reused official Splash weight assets are the draft model and vision encoder. Install Splash on an Apple Silicon Mac, then replace YOURUSERNAME with this repository's owner: After Splash prints Ready, open http://127.0.0.1:8000 or send a chat completion request. The 8K… Open weights apache-2.0 [View model](https://savrn.com/models/ravenx-cyberagent-qwen3-6-35b-a3b-splash-q4) Model · Text generation ### [reachy-mini-motion-planner-0.8b](https://savrn.com/models/reachy-mini-motion-planner-0-8b) [Binh Pham](https://savrn.com/model-publishers/binhpham) Text-to-motion planner for Reachy Mini: a prompt goes in, and a ready-to-play head, antenna and body trajectory comes out. This repo is a complete serving bundle: The response holds the recipe, a one-line idea, and moves: Reachy Mini recorded-move dicts ({"time", "settargetdata": [{"head": 4x4, "antennas", "bodyyaw"}]}), already projected onto the robot's reachable set. Serving effort: "low" runs this model. Speed: ~0.18 s median per prompt on one RTX PRO 6000 (FP8 + fine-tuned MTP; planner 0.14 s), the fastest tier; weaker on multi-phase motions (yawns, bows, build-up events). GPU memory: ~6 GB. Compact system prompt (units, recipe grammar, 4 motion rules, 3 examples), user message = the… Open weights apache-2.0 transformers [View model](https://savrn.com/models/reachy-mini-motion-planner-0-8b) Model · Text generation ### [reachy-mini-motion-planner-27b](https://savrn.com/models/reachy-mini-motion-planner-27b) [Binh Pham](https://savrn.com/model-publishers/binhpham) Text-to-motion planner for Reachy Mini: a prompt goes in, and a ready-to-play head, antenna and body trajectory comes out. This repo is a complete serving bundle: The response holds the recipe, a one-line idea, and moves: Reachy Mini recorded-move dicts ({"time", "settargetdata": [{"head": 4x4, "antennas", "bodyyaw"}]}), already projected onto the robot's reachable set. Serving effort: "high" runs this model. Speed: ~0.84 s median per prompt on one RTX PRO 6000 (FP8 + fine-tuned MTP speculative decoding; planner 0.75 s). GPU memory: ~40 GB with FP8. Compact system prompt (units, recipe grammar, 4 motion rules, 3 examples), user message = the prompt (word. one sentence of context. works… Open weights apache-2.0 transformers [View model](https://savrn.com/models/reachy-mini-motion-planner-27b) Model · Text generation ### [reachy-mini-motion-planner-4b](https://savrn.com/models/reachy-mini-motion-planner-4b) [Binh Pham](https://savrn.com/model-publishers/binhpham) Text-to-motion planner for Reachy Mini: a prompt goes in, and a ready-to-play head, antenna and body trajectory comes out. This repo is a complete serving bundle: The response holds the recipe, a one-line idea, and moves: Reachy Mini recorded-move dicts ({"time", "settargetdata": [{"head": 4x4, "antennas", "bodyyaw"}]}), already projected onto the robot's reachable set. Serving effort: "medium" runs this model. Speed: ~0.31 s median per prompt on one RTX PRO 6000 (FP8 + fine-tuned MTP; planner 0.25 s), 2.7x faster than the 27B. GPU memory: ~12 GB. Compact system prompt (units, recipe grammar, 4 motion rules, 3 examples), user message = the prompt (word. one sentence of context. works best).… Open weights apache-2.0 transformers [View model](https://savrn.com/models/reachy-mini-motion-planner-4b) Model · Image to image ### [Real-ESRGAN-OpenVINO-INT4](https://savrn.com/models/real-esrgan-openvino-int4) [Max Sun](https://savrn.com/model-publishers/hellosun) 把 Real-ESRGAN 的 RealESRGANx4plus （RRDBNet，23 個 RRDB block，16.7 M 參數）轉成 OpenVINO IR，並用 NNCF 做成 INT4 / INT8 / FP16 多種精度版本。所有模型都是純 OpenVINO 推論， 執行時不需要 PyTorch，只要 openvino + opencv + numpy（首次使用會自動從本 repo 下載權重）。 測試機：Intel Xeon Platinum 8559C（Emerald Rapids，16 vCPU，含 AMX-INT8 / AVX512-VNNI / AVX512-FP16）、 OpenVINO 2026.4.1、tile=128、tilepad=16。 畫質（3 張 inputs/ 測試圖，PSNR / SSIM 對 fp16 版本；fp16 與 PyTorch FP32 差異 直接下載 00sourcelr.mp4 你的瀏覽器不支援 video 標籤，請改用 直接下載 20sidebyside1920x780.mp4 若播放器不支援 HTML video，可直接看這支 GIF： 原始 PyTorch FP32（放大後） 你的瀏覽器不支援 video 標籤，請改用 直接下載 10pytorchfp324x.mp4 OpenVINO FP16 你的瀏覽器不支援 video 標籤，請改用 直接下載 11openvinofp164x.mp4 OpenVINO INT8（W8A8） 你的瀏覽器不支援 video 標籤，請改用 直接下載… Open weights apache-2.0 openvino [View model](https://savrn.com/models/real-esrgan-openvino-int4) Model · Any to any ### [Realtime-Venus](https://savrn.com/models/realtime-venus) [inclusionAI](https://savrn.com/model-publishers/inclusionai) This repository hosts two checkpoints of the Realtime-Venus system: - Realtime-Venus-Omni (Realtime-Venus-Omni/): the 9B audio-visual interaction model. It continuously watches and listens, decides whether and when to respond, and generates text and speech on a shared causal timeline. Adapted from MiniCPM-o 4.5, it supports proactive interaction, semantic interruption handling, and training-free long-video memory. - Realtime-Venus-Audio (Realtime-Venus-Audio/): the audio-focused checkpoint on the same streaming backbone, for audio understanding and audio-driven conversation with text or speech output. Both directories contain model weights and custom Hugging Face Transformers code. The… Open weights apache-2.0 transformers [View model](https://savrn.com/models/realtime-venus) Model · Any to any ### [Realtime-Venus-Omni-ablated](https://savrn.com/models/realtime-venus-omni-ablated) [Jared H](https://savrn.com/model-publishers/justsumguy) The Realtime-Venus-Omni checkpoint from inclusionAI/Realtime-Venus (itself a fine-tune of openbmb/MiniCPM-o-45), with refusal behaviour reduced by abliteration of the language tower. BF16, same file layout and remote code as upstream. An NVFP4 build is at Only the language tower (llm., Qwen3-8B shape, 36 layers). The vision tower, audio tower, resampler, audio projection, TTS talker and token2wav assets are upstream's, unmodified. - 72 matrices were changed: selfattn.oproj and mlp.downproj in all 36 layers. - The largest relative change is ‖ΔW‖/‖W‖ = 0.379. The upstream sample media (assets/sample1real.mp4, assets/speechin.mp4) are not included. - The language tower was extracted as a… Open weights apache-2.0 transformers [View model](https://savrn.com/models/realtime-venus-omni-ablated) Model · Any to any ### [Realtime-Venus-Omni-ablated-NVFP4](https://savrn.com/models/realtime-venus-omni-ablated-nvfp4) [Jared H](https://savrn.com/model-publishers/justsumguy) quantised with NVIDIA ModelOpt 0.46.1. It is 9.1 GB on disk. The abliteration details and numbers are on that page. In short: heretic ARA branch, ARA-LoRA rank 128, trial 83, refusals 98 → 4 of 100, KL 0.249 on recheck, no healing applied. - llm calibration: 256 samples × 512 tokens of tatsu-lab/alpaca (shuffled, seed 0), run through the extracted text tower. Mean NLL on 8 calibration chats: BF16 3.941, NVFP4 fake-quant 4.065 (+0.123). - vpm calibration: 80 images from johnowhitaker/imagenette2-320. Three quarters were unsliced; one quarter used maxslicenums=4. - vpm held-out check (16 images): - One RTX 5060 Ti 16 GB (SM120) and one RTX 3050 Ti Laptop 4 GB (SM86), with lmhead on CPU during… Open weights apache-2.0 transformers [View model](https://savrn.com/models/realtime-venus-omni-ablated-nvfp4) Model ### [REAP](https://savrn.com/models/reap) [Gangyin Wang](https://savrn.com/model-publishers/constellationw) Open weights apache-2.0 [View model](https://savrn.com/models/reap) Model ### [reasoning-rlvr-sft](https://savrn.com/models/reasoning-rlvr-sft) [CHAGANTI RAJA RAVI TEJA](https://savrn.com/model-publishers/raviteja546) Open weights apache-2.0 1.7B parameters [View model](https://savrn.com/models/reasoning-rlvr-sft) Model ### [reggie-chronicles-whitepaper](https://savrn.com/models/reggie-chronicles-whitepaper) [JAMES A BROUSE](https://savrn.com/model-publishers/bigchilli1978) This repository serves as the official research archive for the REGGIE sovereign autonomous intelligence ecosystem engineered by James Brouse (Bigchilli1978). Licensed under Apache 2.0. Open weights apache-2.0 [View model](https://savrn.com/models/reggie-chronicles-whitepaper) Model · Text generation ### [reggie-v3.6-rlvr-production-GGUF](https://savrn.com/models/reggie-v3-6-rlvr-production-gguf) [JAMES A BROUSE](https://savrn.com/model-publishers/bigchilli1978) The Reggie open-weights model family, engineered by James Brouse (Bigchilli1978) in Chilliwack, British Columbia, has achieved over 8,521 downloads across Hugging Face: - 7,402 downloads across viral community importance-matrix quantizations by mradermacher (including reggie-v2-7b-i1-GGUF at 4,500+ and reggie-v3-7b-i1-GGUF at 1,760+). - 1,119 direct downloads across 15 official Bigchilli1978 repositories. Notice to Community Quantizers: If you quantize this model (GGUF, EXL2, AWQ, HQQ), feel free to open a discussion or tag Bigchilli1978 so we can link your quants here! Reggie v3.6 is continuously aligned using an on-premises reinforcement learning pipeline (SelfEvolutionDaemon). Rather… Open weights apache-2.0 [View model](https://savrn.com/models/reggie-v3-6-rlvr-production-gguf) Model · Text generation ### [Rem_Mistral_22B](https://savrn.com/models/rem-mistral-22b) [Timur](https://savrn.com/model-publishers/tomry0) This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library. Open weights apache-2.0 22.2B parameters 131,072 tokens transformers [View model](https://savrn.com/models/rem-mistral-22b) Model ### [removeit-lama-coreml](https://savrn.com/models/removeit-lama-coreml) [Joey R](https://savrn.com/model-publishers/skywalker33) LaMa (Large Mask Inpainting, Samsung AI Center Moscow, Apache 2.0) in Core ML, from mlboydaisuke/LaMa-CoreML, unzipped. Open weights apache-2.0 [View model](https://savrn.com/models/removeit-lama-coreml) Model ### [removeit-sam2-coreml](https://savrn.com/models/removeit-sam2-coreml) [Joey R](https://savrn.com/model-publishers/skywalker33) SAM 2.1 Small (Meta, Apache 2.0) in Apple's Core ML conversion, mirrored from apple/coreml-sam2.1-small. Open weights apache-2.0 [View model](https://savrn.com/models/removeit-sam2-coreml) Model · Text ranking ### [rerank-indonesia-Q8_0-GGUF](https://savrn.com/models/rerank-indonesia-q8-0-gguf) [Radhi Fadlillah](https://savrn.com/model-publishers/radhifadlillah) This model was converted to GGUF format from madebyaris/rerank-indonesia using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model. Install llama.cpp through brew (works on Mac and Linux) Invoke the llama.cpp server or the CLI. Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well. Step 2: Move into the llama.cpp folder and build it with LLAMACURL=1 flag along with other hardware-specific flags (for ex: LLAMACUDA=1 for Nvidia GPUs on Linux). Open weights apache-2.0 sentence-transformers [View model](https://savrn.com/models/rerank-indonesia-q8-0-gguf) Model · Text generation ### [ReScraper](https://savrn.com/models/rescraper) [Chenyan Xiong Research Group at CMU](https://savrn.com/model-publishers/cx-cmu) ReScraper is a 0.6B refiner that replaces the heuristic HTML-to-text stack of a pretraining data pipeline — a rule-based scraper followed by rule-based cleaning filters — with a single small language model. The model reads the visible text of a raw web page, rendered one block per line with line ids, and generates a short program: 1. An block of line removals (rm 1-4, rm 6, …) that strips headers, menus, boilerplate and other non-content lines. 2. One operation tag for the page: An executor applies the program to the page, so extraction and cleaning happen in one model instead of a cascade of separate stages. The system prompt and the executor that applies the generated program are in the… Open weights apache-2.0 [View model](https://savrn.com/models/rescraper) Model · Speech recognition ### [results](https://savrn.com/models/results) [Sum](https://savrn.com/model-publishers/summi1125) This model is a fine-tuned version of openai/whisper-small on an unknown dataset. The following hyperparameters were used during training: - learningrate: 1e-05 - trainbatchsize: 8 - evalbatchsize: 8 - lrschedulertype: linear - numepochs: 3 - mixedprecisiontraining: Native AMP - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights apache-2.0 242M parameters transformers [View model](https://savrn.com/models/results) Model ### [retrieval](https://savrn.com/models/retrieval-6) [Nikolay](https://savrn.com/model-publishers/flatwhitenikolay) A research-oriented Dino prototype targeting Retrieval. The included xlarge setup documents defaults and file formats without presenting unverified performance numbers. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses adamw with a step schedule. These are starting values in the script, not evidence of a completed run. For a… Open weights apache-2.0 16,576 parameters 512 tokens [View model](https://savrn.com/models/retrieval-6) Model ### [retrieval-best3](https://savrn.com/models/retrieval-best3) [Christopher W. Rodriguez](https://savrn.com/model-publishers/christopherreyh) Working implementation of Mobilevit for Retrieval using a large configuration. The repository focuses on transparent code and repeatable smoke tests; benchmark claims are deliberately omitted. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses lion with a constant warmup schedule. These are starting values in the script, not evidence… Open weights apache-2.0 33,088 parameters 256 tokens [View model](https://savrn.com/models/retrieval-best3) Model ### [retrieval-large](https://savrn.com/models/retrieval-large) [Harutoanp](https://savrn.com/model-publishers/harutoanp) This repository is a compact, custom PyTorch implementation of Albef for Retrieval. The giant configuration is intended for code review, smoke tests, and small controlled experiments rather than as a production-ready pretrained release. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses adam with a linear warmup schedule. These are… Open weights apache-2.0 33,088 parameters 512 tokens [View model](https://savrn.com/models/retrieval-large) Model ### [retrieval-notes](https://savrn.com/models/retrieval-notes) [Katarzyna Wozniak](https://savrn.com/model-publishers/katarzynawozniak) A small Efficientformer implementation for Retrieval, packaged with an explicit configuration and an initialization checkpoint. The nano variant is a reproducible starting point, not a trained model release. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses novograd with a onecycle schedule. These are starting values in the script… Open weights apache-2.0 33,088 parameters 256 tokens [View model](https://savrn.com/models/retrieval-notes) Model ### [retrieval-run3](https://savrn.com/models/retrieval-run3) [Mason Davis](https://savrn.com/model-publishers/cianggraini) A research-oriented Flamingo prototype targeting Retrieval. The included huge setup documents defaults and file formats without presenting unverified performance numbers. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses adafactor with a step schedule. These are starting values in the script, not evidence of a completed run. For a… Open weights apache-2.0 16,576 parameters 512 tokens [View model](https://savrn.com/models/retrieval-run3) Model · Text generation ### [revonance-drafter-qwen2.5-0.5b](https://savrn.com/models/revonance-drafter-qwen2-5-0-5b) [Yuan Hao Sum](https://savrn.com/model-publishers/trychoosing) The Drafter agent of a LangGraph multi-agent support system for the fictional shop ReVonance. Base model Qwen/Qwen2.5-0.5B-Instruct, fine-tuned with LoRA (merged). Stage: sft (sft = supervised on the response simulator; dpo = then aligned with human and critic preferences via Direct Preference Optimisation). Knowledge-base fingerprint: f9cdb07b5df6. Demo model trained on synthetic data; it only knows the toy policies of ReVonance. Open weights apache-2.0 494M parameters 32,768 tokens transformers [View model](https://savrn.com/models/revonance-drafter-qwen2-5-0-5b) Model · Text generation ### [revonance-drafter-smollm2-360m](https://savrn.com/models/revonance-drafter-smollm2-360m) [Yuan Hao Sum](https://savrn.com/model-publishers/trychoosing) The Drafter agent of a LangGraph multi-agent support system for the fictional shop ReVonance. Base model HuggingFaceTB/SmolLM2-360M-Instruct, fine-tuned with LoRA (merged). Stage: sft (sft = supervised on the response simulator; dpo = then aligned with human and critic preferences via Direct Preference Optimisation). Knowledge-base fingerprint: f9cdb07b5df6. Demo model trained on synthetic data; it only knows the toy policies of ReVonance. Open weights apache-2.0 362M parameters 8,192 tokens transformers [View model](https://savrn.com/models/revonance-drafter-smollm2-360m) Model ### [rex](https://savrn.com/models/rex) [NguyenThanhDat](https://savrn.com/model-publishers/dat2508) Open weights apache-2.0 149M parameters [View model](https://savrn.com/models/rex) Model · Image and text to text ### [REX-Gemma-4-26B-A4B-Bio-F3](https://savrn.com/models/rex-gemma-4-26b-a4b-bio-f3) [AI45Research](https://savrn.com/model-publishers/ai45research) Open weights apache-2.0 28B parameters transformers [View model](https://savrn.com/models/rex-gemma-4-26b-a4b-bio-f3) Model · Zero shot object detection ### [rfdetr-textalign-ONNX](https://savrn.com/models/rfdetr-textalign-onnx) [Trung M. Bui](https://savrn.com/model-publishers/mtbui2010) Open-vocabulary detection by adding one trained projection to a closed-set RF-DETR Small detector, served by VisionServe The detector exports the 256-d feature fi of each of its 300 object queries as an extra ONNX output, queryfeats. A trained matrix P maps that feature into the text-embedding space of a CLIP or SigLIP text tower, and each class gets a cosine score against the embedding of its name: The vocabulary is the prompt, for example "cup. cola can. banana.". Changing it needs no re-export and no second detector: the text tower runs once per new vocabulary, and the result is cached. Boxes always come from the detector. pull downloads one subfolder of this repo, writes a… Open weights apache-2.0 onnx [View model](https://savrn.com/models/rfdetr-textalign-onnx) Model · Text generation ### [rick-morty-distilgpt2](https://savrn.com/models/rick-morty-distilgpt2) [Zune toka](https://savrn.com/model-publishers/hfzune) This model is a fine-tuned version of distilbert/distilgpt2 on the None dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 4 - evalbatchsize: 4 - gradientaccumulationsteps: 4 - totaltrainbatchsize: 16 - lrschedulertype: linear - numepochs: 1 - mixedprecisiontraining: Native AMP - Transformers 5.17.0 - Pytorch 2.11.0+cu128 - Datasets 5.0.1 - Tokenizers 0.23.1 Open weights apache-2.0 82M parameters transformers [View model](https://savrn.com/models/rick-morty-distilgpt2) Model ### [riemo](https://savrn.com/models/riemo) [Blaize Rouyea](https://savrn.com/model-publishers/beerooyay) This gptoss model was trained 2x faster with Unsloth Open weights apache-2.0 transformers [View model](https://savrn.com/models/riemo) Model ### [rigcut-models](https://savrn.com/models/rigcut-models) [Sreevardhan Ch](https://savrn.com/model-publishers/svch04) Open weights apache-2.0 [View model](https://savrn.com/models/rigcut-models) Model · Text classification ### [riidolaya-development-claim-hints-semantic-contrast-v0.1](https://savrn.com/models/riidolaya-development-claim-hints-semantic-contrast-v0-1) [Kim](https://savrn.com/model-publishers/jooyoon) This is an experimental, CPU-only Go classifier for three claims expressed in English or Korean development comments. One fixed semantic-contrast data continuation reduced mean three-head cross-entropy on a newly sealed AI-reference research evaluation from 0.996490 to 0.860646, compared with a fixed float continuation control. The paired difference was −0.135844, with a 95% lineage-bootstrap interval of [−0.154294, −0.117779]. The prespecified research-progress conditions were met. The general stratum got worse: its candidate-minus-control CE difference was +0.023867. Completion hints remain unqualified, and these AI-reference results do not establish human agreement, private-work… Open weights apache-2.0 [View model](https://savrn.com/models/riidolaya-development-claim-hints-semantic-contrast-v0-1) Model · Text classification ### [riidolaya-development-claim-hints-v0.1](https://savrn.com/models/riidolaya-development-claim-hints-v0-1) [Kim](https://savrn.com/model-publishers/jooyoon) This original CPU-only Go classifier separately estimates response requests, current development work and completion reports. Each head has true/false/unknown classes. It generates no prose and never verifies task completion or authorizes application writes. This version is not operationally qualified. On 240 original AI-reviewed development rows, gated true proposals/correct were KO: response1/1, activity7/7, completion0/0 (undefined precision); EN: response6/6, activity6/6, completion3/4. Pooled completion precision was75%, below the frozen98% research floor. Unknown activity/completion references had only3 families per locale, below the minimum5. Few perfect proposals do not establish… Open weights apache-2.0 [View model](https://savrn.com/models/riidolaya-development-claim-hints-v0-1) Model · Speech recognition ### [RK182X-ASR-SeaCo-Paraformer](https://savrn.com/models/rk182x-asr-seaco-paraformer) [RKNNAI](https://savrn.com/model-publishers/rknnai) 本仓库提供由 iic/speechseacoparaformerlargeasrnat-zh-cn-16k-common-vocab8404-pytorch 转换得到的 RKNN ASR 模型。 - Model ID：RKNNAI/RK182X-ASR-SeaCo-Paraformer - 模型显示名称：RK182X-ASR-SeaCo-Paraformer - 源模型：iic/speechseacoparaformerlargeasrnat-zh-cn-16k-common-vocab8404-pytorch - 模型类型：ASR ModelScope 完整下载： Hugging Face 完整下载： ModelScope 指定配置下载： Hugging Face 指定配置下载： - 使用配套 RKNN Runtime 和驱动；运行前用 rknn-smi -v 检查设备端版本。 - CIF 和 BiasEncoder 组件在 CPU 上运行。 源模型许可证见根目录 LICENSE；同时遵守 RKNN Toolkit 和 RKNN Runtime 许可条款。 本发布物为源模型的 RKNN 转换/量化衍生物，保留原始许可与归属声明，见 Notice。 Open weights apache-2.0 [View model](https://savrn.com/models/rk182x-asr-seaco-paraformer) Model · Image classification ### [RK182X-CNN-MobileNetV1](https://savrn.com/models/rk182x-cnn-mobilenetv1) [RKNNAI](https://savrn.com/model-publishers/rknnai) 本仓库提供由 tensorflow/models 转换得到的 RKNN CNN 模型。 - Model ID：RKNNAI/RK182X-CNN-MobileNetV1 - 模型显示名称：RK182X-CNN-MobileNetV1 - 源模型：tensorflow/models - 模型类型：CNN ModelScope 完整下载： Hugging Face 完整下载： ModelScope 指定配置下载： Hugging Face 指定配置下载： - 使用配套 RKNN Runtime 和驱动；运行前用 rknn-smi -v 检查设备端版本。 - 源模型许可证：Apache License 2.0，正文见 LICENSE，归属和转换说明见 NOTICE。 - 本仓库交付物是源模型的 RKNN 转换衍生物，仍受源模型许可和使用限制约束。 - 同时遵守 RKNN Toolkit、RKNN Runtime 相关许可；本仓库不包含运行库。 Open weights apache-2.0 [View model](https://savrn.com/models/rk182x-cnn-mobilenetv1) Model · Image classification ### [RK182X-CNN-MobileNetV2](https://savrn.com/models/rk182x-cnn-mobilenetv2) [RKNNAI](https://savrn.com/model-publishers/rknnai) 本仓库提供由 onnx/models 转换得到的 RKNN CNN 模型。 - Model ID：RKNNAI/RK182X-CNN-MobileNetV2 - 模型显示名称：RK182X-CNN-MobileNetV2 - 源模型：onnx/models - 模型类型：CNN ModelScope 完整下载： Hugging Face 完整下载： ModelScope 指定配置下载： Hugging Face 指定配置下载： - 使用配套 RKNN Runtime 和驱动；运行前用 rknn-smi -v 检查设备端版本。 - 源模型许可证：Apache License 2.0，正文见 LICENSE，归属和转换说明见 NOTICE。 - 本仓库交付物是源模型的 RKNN 转换衍生物，仍受源模型许可和使用限制约束。 - 同时遵守 RKNN Toolkit、RKNN Runtime 相关许可；本仓库不包含运行库。 Open weights apache-2.0 [View model](https://savrn.com/models/rk182x-cnn-mobilenetv2) Model · Image classification ### [RK182X-CNN-ResNet50V2](https://savrn.com/models/rk182x-cnn-resnet50v2) [RKNNAI](https://savrn.com/model-publishers/rknnai) 本仓库提供由 onnx/models 转换得到的 RKNN CNN 模型。 - Model ID：RKNNAI/RK182X-CNN-ResNet50V2 - 模型显示名称：RK182X-CNN-ResNet50V2 - 源模型：onnx/models - 模型类型：CNN ModelScope 完整下载： Hugging Face 完整下载： ModelScope 指定配置下载： Hugging Face 指定配置下载： - 使用配套 RKNN Runtime 和驱动；运行前用 rknn-smi -v 检查设备端版本。 - 源模型许可证：Apache License 2.0，正文见 LICENSE，归属和转换说明见 NOTICE。 - 本仓库交付物是源模型的 RKNN 转换衍生物，仍受源模型许可和使用限制约束。 - 同时遵守 RKNN Toolkit、RKNN Runtime 相关许可；本仓库不包含运行库。 Open weights apache-2.0 [View model](https://savrn.com/models/rk182x-cnn-resnet50v2) ## Questions ### Can I use Apache License 2.0 models commercially? Yes. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors. ### Which Apache License 2.0 models are most downloaded? By monthly downloads reported by the Hugging Face Hub: . ## Other Licenses - [mit](https://savrn.com/models/licenses/mit) 1305 - [cc-by-nc-4.0](https://savrn.com/models/licenses/cc-by-nc-4-0) 209 - [cc-by-4.0](https://savrn.com/models/licenses/cc-by-4-0) 164 - [bsd-3-clause](https://savrn.com/models/licenses/bsd-3-clause) 108 - [gemma](https://savrn.com/models/licenses/gemma) 63 - [cc-by-nc-sa-4.0](https://savrn.com/models/licenses/cc-by-nc-sa-4-0) 52 - [llama3.2](https://savrn.com/models/licenses/llama3-2) 40 - [agpl-3.0](https://savrn.com/models/licenses/agpl-3-0) 32 - [unknown](https://savrn.com/models/licenses/unknown) 32 - [llama3.1](https://savrn.com/models/licenses/llama3-1) 28 - [cc-by-sa-4.0](https://savrn.com/models/licenses/cc-by-sa-4-0) 20 - [gpl-3.0](https://savrn.com/models/licenses/gpl-3-0) 20 [See all](https://savrn.com/models/licenses)