# Open-Weight Models Under Apache License 2.0, Page 39 Source: https://savrn.com/models/licenses/apache-2-0/page/39 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 39 of 55 Model ### [MAI-Gym-9B](https://savrn.com/models/mai-gym-9b) [Tongyi-MAI](https://savrn.com/model-publishers/tongyi-mai) Open weights apache-2.0 [View model](https://savrn.com/models/mai-gym-9b) Model ### [MAI-UI-2B-Q4_0-GGUF](https://savrn.com/models/mai-ui-2b-q4-0-gguf) [Yongsheng Yan](https://savrn.com/model-publishers/airhaohan) This model was converted to GGUF format from Tongyi-MAI/MAI-UI-2B 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 [View model](https://savrn.com/models/mai-ui-2b-q4-0-gguf) Model ### [MaLoW-Qwen3.5-0.8B](https://savrn.com/models/malow-qwen3-5-0-8b) [Zhuoming Liu](https://savrn.com/model-publishers/zhuomingliu) Inference checkpoint for Memory as Weights: Internalizing Long-Term History for Streaming Videos. Paper: Memory as Weights: Internalizing Long-Term History for Streaming Videos Download the base model separately: Qwen/Qwen3.5-0.8B. This repository contains inference modules, not a standalone base model. Follow docs/videoevaluation.md in the MaLoW code release for OVO-Bench and StreamingBench. The JSON metadata records the inference configuration; load it through the MaLoW runtime. Weights and inference metadata are distributed under Apache-2.0. See LICENSE and NOTICE; base models and benchmark assets retain their respective upstream terms. Open weights apache-2.0 116M parameters [View model](https://savrn.com/models/malow-qwen3-5-0-8b) Model ### [MaLoW-Qwen3.5-2B](https://savrn.com/models/malow-qwen3-5-2b) [Zhuoming Liu](https://savrn.com/model-publishers/zhuomingliu) Inference checkpoint for Memory as Weights: Internalizing Long-Term History for Streaming Videos. Paper: Memory as Weights: Internalizing Long-Term History for Streaming Videos Download the base model separately: Qwen/Qwen3.5-2B. This repository contains inference modules, not a standalone base model. Follow docs/videoevaluation.md in the MaLoW code release for OVO-Bench and StreamingBench. The JSON metadata records the inference configuration; load it through the MaLoW runtime. Weights and inference metadata are distributed under Apache-2.0. See LICENSE and NOTICE; base models and benchmark assets retain their respective upstream terms. Open weights apache-2.0 171M parameters [View model](https://savrn.com/models/malow-qwen3-5-2b) Model ### [MaLoW-Qwen3.5-2B-MLP](https://savrn.com/models/malow-qwen3-5-2b-mlp) [Zhuoming Liu](https://savrn.com/model-publishers/zhuomingliu) Inference checkpoint for Memory as Weights: Internalizing Long-Term History for Streaming Videos. Evaluation requires the MaLoW code release. The code has been prepared locally; public GitHub publication is pending. Intended repository: MaLoW (forthcoming). Download the base model separately: Qwen/Qwen3.5-2B. This repository contains inference modules, not a standalone base model. Follow docs/vlaevaluation.md in the MaLoW code release for Simpler WidowX. This checkpoint additionally requires the Qwen3.5-2B-MLP baseline adapter as the frozen policy base. Both bundles are required. Reported reference success rates (not a fresh evaluation of this export): datasetstatistics.json supplies action… Open weights apache-2.0 175M parameters [View model](https://savrn.com/models/malow-qwen3-5-2b-mlp) Model ### [MaLoW-Qwen3.5-4B](https://savrn.com/models/malow-qwen3-5-4b) [Zhuoming Liu](https://savrn.com/model-publishers/zhuomingliu) Inference checkpoint for Memory as Weights: Internalizing Long-Term History for Streaming Videos. Paper: Memory as Weights: Internalizing Long-Term History for Streaming Videos Download the base model separately: Qwen/Qwen3.5-4B. This repository contains inference modules, not a standalone base model. Follow docs/videoevaluation.md in the MaLoW code release for OVO-Bench and StreamingBench. The JSON metadata records the inference configuration; load it through the MaLoW runtime. Overall computed from listed subtask scores using benchmark weights 2500:1500:250:250. Weights and inference metadata are distributed under Apache-2.0. See LICENSE and NOTICE; base models and benchmark assets retain… Open weights apache-2.0 235M parameters [View model](https://savrn.com/models/malow-qwen3-5-4b) Model ### [MaLoW-Qwen3.5-4B-Last4s](https://savrn.com/models/malow-qwen3-5-4b-last4s) [Zhuoming Liu](https://savrn.com/model-publishers/zhuomingliu) Inference checkpoint for Memory as Weights: Internalizing Long-Term History for Streaming Videos. Paper: Memory as Weights: Internalizing Long-Term History for Streaming Videos Download the base model separately: Qwen/Qwen3.5-4B. This repository contains inference modules, not a standalone base model. Evaluation uses the last 4 seconds as the current observation, represented by 4 frames. Follow docs/videoevaluation.md in the MaLoW code release for OVO-Bench and StreamingBench. The JSON metadata records the inference configuration; load it through the MaLoW runtime. Weights and inference metadata are distributed under Apache-2.0. See LICENSE and NOTICE; base models and benchmark assets… Open weights apache-2.0 235M parameters [View model](https://savrn.com/models/malow-qwen3-5-4b-last4s) Model ### [MaLoW-Qwen3.5-9B](https://savrn.com/models/malow-qwen3-5-9b) [Zhuoming Liu](https://savrn.com/model-publishers/zhuomingliu) Checkpoint for Memory as Weights: Internalizing Long-Term History for Streaming Videos. Paper: Memory as Weights: Internalizing Long-Term History for Streaming Videos Download the base model separately: Qwen/Qwen3.5-9B. This repository contains inference modules, not a standalone base model. Follow docs/videoevaluation.md in the MaLoW code release for OVO-Bench and StreamingBench. The JSON metadata records the inference configuration; load it through the MaLoW runtime. Weights and inference metadata are distributed under Apache-2.0. See LICENSE and NOTICE; base models and benchmark assets retain their respective upstream terms. Open weights apache-2.0 309M parameters [View model](https://savrn.com/models/malow-qwen3-5-9b) Model ### [MaLoW-Qwen3-VL-4B-Last8s](https://savrn.com/models/malow-qwen3-vl-4b-last8s) [Zhuoming Liu](https://savrn.com/model-publishers/zhuomingliu) Inference checkpoint for Memory as Weights: Internalizing Long-Term History for Streaming Videos. Paper: Memory as Weights: Internalizing Long-Term History for Streaming Videos Download the base model separately: Qwen/Qwen3-VL-4B-Instruct. This repository contains inference modules, not a standalone base model. This variant uses the last 8 seconds as the current observation, represented by 8 frames. Follow docs/videoevaluation.md in the MaLoW code release for OVO-Bench and StreamingBench. The JSON metadata records the inference configuration; load it through the MaLoW runtime. Weights and inference metadata are distributed under Apache-2.0. See LICENSE and NOTICE; base models and benchmark… Open weights apache-2.0 198M parameters [View model](https://savrn.com/models/malow-qwen3-vl-4b-last8s) Model · Text generation ### [Mangaba-Nano-1M](https://savrn.com/models/mangaba-nano-1m) [Mangaba AI](https://savrn.com/model-publishers/mangaba-ai) Um modelo de linguagem de 1,4 milhão de parâmetros, treinado do zero em português do Brasil pela Mangaba AI. Relatório técnico completo (dados, arquitetura, Muon, ablações, controles e destilação): https://huggingface.co/spaces/mangaba-ai/mangaba-nano-artigo Não há modelo base: os pesos começaram aleatórios e o tokenizer também foi treinado do zero sobre o mesmo corpus. O treino rodou nas GPUs NVIDIA L40S da própria Mangaba, em datacenter no Brasil. É um modelo de pesquisa e de borda: cabe em 5,6 MB, roda em qualquer CPU e serve como base para estudar escalas minúsculas, fazer ajuste fino em tarefas estreitas (classificação, autocompletar, extração simples) ou rodar em microcontroladores e… Open weights apache-2.0 1M parameters 512 tokens transformers [View model](https://savrn.com/models/mangaba-nano-1m) Model ### [mangadict-models](https://savrn.com/models/mangadict-models) [Craig Kolb](https://savrn.com/model-publishers/craigkolb) ONNX models used by the mangadict browser extension to find and read Japanese text in manga pages. They are repackaged from the models below for in-browser inference with onnxruntime-web; see those repos for training details and their licenses (all Apache-2.0). Use tagged revisions (e.g. v1); main may change. Open weights apache-2.0 [View model](https://savrn.com/models/mangadict-models) Model · Image to image ### [Marigold-v2-Qwen-VAE-FlashPack](https://savrn.com/models/marigold-v2-qwen-vae-flashpack) [Fal](https://savrn.com/model-publishers/fal) This September, we are pleased to introduce Qwen-Image-Edit-2509, the monthly iteration of Qwen-Image-Edit. To experience the latest model, please visit Qwen Chat and select the "Image Editing" feature. Compared with Qwen-Image-Edit released in August, the main improvements of Qwen-Image-Edit-2509 include: Install the latest version of diffusers The following contains a code snippet illustrating how to use Qwen-Image-Edit-2509: The primary update in Qwen-Image-Edit-2509 is support for multi-image inputs. Let’s first look at a "person + person" example: Here is a "person + scene" example: Below is a "person + object" example: In fact, multi-image input also supports commonly used ControlNet… Open weights apache-2.0 20.4B parameters diffusers [View model](https://savrn.com/models/marigold-v2-qwen-vae-flashpack) Model · Text generation ### [MarinSkyRL-Open-MOPD-SmolLM3-3B-step-32](https://savrn.com/models/marinskyrl-open-mopd-smollm3-3b-step-32) [Open Athena](https://savrn.com/model-publishers/open-athena) This is a MarinSkyRL-native Open-MOPD student after 32 optimizer steps. It starts from the authors' mixed-domain SFT checkpoint. Student responses were scored by the authors' math, code, and instruction-following RL teachers, routed by domain. The objective uses the student's selected top-16 token IDs and a clipped policy surrogate. This is an early checkpoint, not the authors' step-200 final model. The checkpoint is an unquantized, six-file Hugging Face export of the durable MarinSkyRL globalstep32 FSDP2 checkpoint. The policy export was used for the independent step-32 evaluation. The export's model.safetensors SHA-256 is bb7326640142069bc2e1fba5f54f15e0cccb1ff861f34f318b372eaab7abaf4b.… Open weights apache-2.0 3.3B parameters 65,536 tokens transformers [View model](https://savrn.com/models/marinskyrl-open-mopd-smollm3-3b-step-32) Model · Text generation ### [mario-html-4B-GGUF](https://savrn.com/models/mario-html-4b-gguf) [Wesjos Max](https://savrn.com/model-publishers/wesjos) A visual-UI language model fine-tuned from Qwen3.5-4B, quantized to GGUF for llama.cpp. It answers in a small semantic HTML component system — bento grids, metric cards, steppers, callouts — wrapped in one root, instead of plain Markdown. Reasoning-native, and no system prompt required. One root element; everything else is an optional child: ai-view-header is not in the source repo's theme.css — it renders with default header styling. Fine, but define your own if you want tighter control. Apache License 2.0. Open weights apache-2.0 transformers [View model](https://savrn.com/models/mario-html-4b-gguf) Model · Robotics ### [marker_test_new_camera_setup_20260920_221944](https://savrn.com/models/marker-test-new-camera-setup-20260920-221944) [Krishaanth Ramaraj](https://savrn.com/model-publishers/krish5831) Action Chunking with Transformers (ACT) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot act guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning… Open weights apache-2.0 52M parameters lerobot [View model](https://savrn.com/models/marker-test-new-camera-setup-20260920-221944) Model ### [matching](https://savrn.com/models/matching-3) [Fahri Nugroho](https://savrn.com/model-publishers/sbynu-groho) A small Mixer implementation for Matching, 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 polynomial schedule. These are starting values in the script, not… Open weights apache-2.0 24,832 parameters 512 tokens [View model](https://savrn.com/models/matching-3) Model ### [matching](https://savrn.com/models/matching-5) [Yujin V. Park](https://savrn.com/model-publishers/parkyujinwell) This is an experimental Swin T codebase for Matching. It keeps the small setup intentionally manageable so architecture changes can be inspected before a full training run. - 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, not evidence of a completed run. For… Open weights apache-2.0 16,576 parameters 512 tokens [View model](https://savrn.com/models/matching-5) Model ### [matching-run2](https://savrn.com/models/matching-run2) [Aditya T. Ramanathan](https://savrn.com/model-publishers/aqramanathan) A small ViT implementation for Matching, packaged with an explicit configuration and an initialization checkpoint. The small 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 rmsprop with a polynomial schedule. These are starting values in the script, not evidence… Open weights apache-2.0 16,576 parameters 256 tokens [View model](https://savrn.com/models/matching-run2) Model ### [matching-tryout](https://savrn.com/models/matching-tryout) [Diego Diaz](https://savrn.com/model-publishers/diego-diaz) Working implementation of Tiny Transformer for Matching using a base 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 adam with a exponential schedule. These are starting values in the script, not evidence… Open weights apache-2.0 49,600 parameters 512 tokens [View model](https://savrn.com/models/matching-tryout) Model ### [matching25](https://savrn.com/models/matching25) [Meena D. Malhotra](https://savrn.com/model-publishers/mmalhotrameena) A small CLIP implementation for Matching, 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 constant warmup schedule. These are starting values in the script, not… Open weights apache-2.0 33,088 parameters 256 tokens [View model](https://savrn.com/models/matching25) Model · Text generation ### [math-slm-qwen2.5-0.5b-v4](https://savrn.com/models/math-slm-qwen2-5-0-5b-v4) [Mandavi Singh](https://savrn.com/model-publishers/singhmandavi) A domain-specific small language model for step-by-step math problem solving, built by team03 (SLM Learners) for the Pramana SLM++ Bootcamp Round 2 submission. For an OpenAI-compatible endpoint, serve with servehf.py (stdlib + transformers only, no Ollama needed). Precision note: training ran in bf16 compute (QLoRA 4-bit NF4 base), but the merged checkpoint uploaded here is float16 (the merge step reloads the base in fp16). - Public Hugging Face datasets pulled via pulldata.py; licenses verified through the HF API on 2026-09-05 and recorded in datamanifest.md. - Held-out eval set built with buildheldouteval.py from raw ExamBench rows never used in training, with a final overlap check that… Open weights apache-2.0 494M parameters 32,768 tokens [View model](https://savrn.com/models/math-slm-qwen2-5-0-5b-v4) Model · Video to video ### [Matrix-Game-4.0](https://savrn.com/models/matrix-game-4-0) [Skywork](https://savrn.com/model-publishers/skywork) Scene-specialized causal video renderers for Medieval Castle, Ancient Egypt, and City Racing. An executable game determines gameplay, physics and input, while the model re-renders clean low-poly RGB. HUD and sound remain separate, frame-aligned presentation layers. Each scene checkpoint has 5,001,016,512 BF16 parameters. The weights require the Matrix-Game 4.0 inference source; they are not drop-in Diffusers pipelines. Training code and training data are not included. Shared encoders/codecs are installed once for all scenes. See game asset details. Use the source release's manifest downloader with a pinned Hub commit revision and --repo-id Skywork/Matrix-Game-4.0 --repo-type model for both… Open weights apache-2.0 [View model](https://savrn.com/models/matrix-game-4-0) Model ### [max-agent](https://savrn.com/models/max-agent) [Munhyeongog](https://savrn.com/model-publishers/hyongok2) Open weights apache-2.0 [View model](https://savrn.com/models/max-agent) Model · Feature extraction ### [mdbr-leaf-ir-coreml](https://savrn.com/models/mdbr-leaf-ir-coreml) [Justin Vincent](https://savrn.com/model-publishers/innerloop-dev) This is MongoDB's mdbr-leaf-ir (revision 4262131b, Apache License 2.0), converted to Core ML by Innerloop for Breadcrumb's search and recall by meaning. All credit for the model goes to MongoDB. - leaf.mlmodelc: the compiled Core ML program, fp16 storage, macOS 14+. Input inputids (int32, shape [1, L], L one of 16, 32, 64, 128, 256, 512; pad with 0). Output embedding: the finished 768-float, unit-length vector. The model's own mean pooling, its 384-to-768 dense layer and its normalisation are inside the program. - vocab.txt: the model's own WordPiece vocabulary (BERT uncased), unchanged. - Queries take the prefix Represent this sentence for searching relevant passages:; documents take none.… Open weights apache-2.0 [View model](https://savrn.com/models/mdbr-leaf-ir-coreml) Model ### [mechanics-ai999x](https://savrn.com/models/mechanics-ai999x) [AI999X Labs](https://savrn.com/model-publishers/ai999x) Open weights apache-2.0 mechanics-ai999x [View model](https://savrn.com/models/mechanics-ai999x) Model · Text classification ### [medsync-indobert-inacbg](https://savrn.com/models/medsync-indobert-inacbg) [MUHAMMAD DHIAUDDIN](https://savrn.com/model-publishers/dragneva) Model klasifikasi teks klinis multi-task berbasis Medical-IndoBERT (Domain-Adaptive Pretrained) yang disempurnakan melalui Supervised Fine-Tuning (SFT) dengan arsitektur Dual-Head: 1. Head 1 (ICD-10): Klasifikasi Diagnosis Klinis Primer (WHO ICD-10). 2. Head 2 (INA-CBGs): Prediksi Kelompok Tarif Casemix Klaim (Kemenkes RI / UNU-IIGH Casemix). Senior atau peneliti dapat langsung menguji model ini menggunakan kode berikut: Dataset dan bobot dikembangkan sesuai regulasi perlindungan data medis UU PDP No. 27/2022. Rekam Medis (Nomor Rekam Medis, No. SEP, Nama Pasien) telah dideidentifikasi dan disimpan di repositori privat terpisah. Open weights apache-2.0 512 tokens [View model](https://savrn.com/models/medsync-indobert-inacbg) Model ### [meeting-avatar](https://savrn.com/models/meeting-avatar) [Red Kit](https://savrn.com/model-publishers/red-kit) a real person with a cloned voice and an LLM brain, served over WebRTC, ready to bridge into Zoom / Google Meet / Teams / LiveKit. Built on OpenAvatarChat (pinned commit 8b7b3b4, Apache-2.0) with a deployment-focused Docker image, configs, and meeting-bridge documentation. (requires l40s-class Space hardware — see below). (upstream reports 2.2 s avg on RTX 4090). One concurrent session per GPU with the MuseTalk config; use config/meetingavatarlite.yaml for lighter hardware. End-to-end boot smoke-tested on Hugging Face Jobs (NVIDIA A10G, CUDA 12.8): system deps → pinned clone → install → weights → boot → HTTP 200 + CosyVoice2 zero-shot synthesis on GPU (9 min, exit 0; handler load 22.5 s… Open weights apache-2.0 cosyvoice [View model](https://savrn.com/models/meeting-avatar) Model · Text to speech ### [MegaTTS3-TurboSpark](https://savrn.com/models/megatts3-turbospark) [Whit3rabbit](https://savrn.com/model-publishers/cowwhyso) MegaTTS3 (ByteDance) converted to safetensors for TurboSpark's Rust runtime. Each component lives in its own folder (alignerlm/, diffusiontransformer/, durationlm/, wavvae/, g2p/); manifest.json lists every file's sha256. See NOTICE.md for the exact sources and their hashes. The WavVAE encoder's origin and license are unverified; read the notice before redistributing. Clone only voices you have the right to use. Open weights apache-2.0 [View model](https://savrn.com/models/megatts3-turbospark) Model · Text generation ### [Mellum2.1-12B-A2.5B-Thinking-GGUF](https://savrn.com/models/mellum2-1-12b-a2-5b-thinking-gguf) [Fares](https://savrn.com/model-publishers/thefaresmostafa) Open weights apache-2.0 gguf [View model](https://savrn.com/models/mellum2-1-12b-a2-5b-thinking-gguf) Model ### [MemDuet-S1-Qwen2.5-1.5B-GGUF](https://savrn.com/models/memduet-s1-qwen2-5-1-5b-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) static quants of https://huggingface.co/jiangchengsong/MemDuet-S1-Qwen2.5-1.5B 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… Open weights apache-2.0 transformers [View model](https://savrn.com/models/memduet-s1-qwen2-5-1-5b-gguf) Model ### [MemDuet-S1-Qwen3.5-0.8B-GGUF](https://savrn.com/models/memduet-s1-qwen3-5-0-8b-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) static quants of https://huggingface.co/jiangchengsong/MemDuet-S1-Qwen3.5-0.8B 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… Open weights apache-2.0 transformers [View model](https://savrn.com/models/memduet-s1-qwen3-5-0-8b-gguf) Model · Text generation ### [MemDuet-S2-7B](https://savrn.com/models/memduet-s2-7b) [Jiangcheng Song](https://savrn.com/model-publishers/jiangchengsong) Open weights apache-2.0 7.6B parameters transformers [View model](https://savrn.com/models/memduet-s2-7b) Model · Image and text to text ### [MemGUI-8B-RL](https://savrn.com/models/memgui-8b-rl) [Anonymous](https://savrn.com/model-publishers/memgui-rl) Anonymous release for the ICLR 2027 submission MemGUI-RL: Reinforcement Learning for Proactive Context Management in Long-Horizon Mobile GUI Agents. Project page: https://memgui-rl-anonymous.github.io/ MemGUI-8B-RL is MemGUI-8B-SFT (Qwen3-VL-8B-Instruct supervised on MemGUI-3K) post-trained for 100 optimizer steps with FARPO (Folding-Aware Reward-decoupled Policy Optimization, span-to-step ratio rho = 9). The policy speaks the ConAct (Context-as-Action) interface of MemGUI-Agent: every response contains a folding directive for its own history, an optional memory operation and the next GUI action. The checkpoint is a standard Qwen3VLForConditionalGeneration model (weights in bf16, ~17.5 GB).… Open weights apache-2.0 8.8B parameters 262,144 tokens [View model](https://savrn.com/models/memgui-8b-rl) Model · Text generation ### [menasaat-qwen3-4b-oman-qlora-v2](https://savrn.com/models/menasaat-qwen3-4b-oman-qlora-v2) [Jaifar Alshizawi](https://savrn.com/model-publishers/jaifar) Second-generation LoRA adapter fine-tuning Qwen/Qwen3-4B (Apache 2.0) on a combined bilingual corpus: the Oman Wikipedia instruction set plus 1,095 tourism-grounded pairs from menasaat/menasaat-oman-tourism-places. مُحوِّل LoRA يُخصِّص Qwen3-4B على مجموعة موسّعة تجمع ويكيبيديا العُمانية وبيانات الأماكن السياحية. Tourism knowledge: opening hours, locations (wilaya/governorate), best visiting seasons, and visitor tips for 159 places across all 11 governorates — plus Arabic location questions (أين تقع...؟) answered from the dataset. Tourism answers are grounded in the compiled dataset; verify time-sensitive details (hours, seasons) before high-stakes use. General-knowledge capability matches… Open weights apache-2.0 peft [View model](https://savrn.com/models/menasaat-qwen3-4b-oman-qlora-v2) Model · Text generation ### [menasaat-qwen3-4b-oman-qlora-v2](https://savrn.com/models/menasaat-qwen3-4b-oman-qlora-v2-2) [Menasaat | Virtual Platforms LLC](https://savrn.com/model-publishers/menasaat) Second-generation LoRA adapter fine-tuning Qwen/Qwen3-4B (Apache 2.0) on a combined bilingual corpus: the Oman Wikipedia instruction set plus 1,095 tourism-grounded pairs from menasaat/menasaat-oman-tourism-places. مُحوِّل LoRA يُخصِّص Qwen3-4B على مجموعة موسّعة تجمع ويكيبيديا العُمانية وبيانات الأماكن السياحية. Tourism knowledge: opening hours, locations (wilaya/governorate), best visiting seasons, and visitor tips for 159 places across all 11 governorates — plus Arabic location questions (أين تقع...؟) answered from the dataset. Tourism answers are grounded in the compiled dataset; verify time-sensitive details (hours, seasons) before high-stakes use. General-knowledge capability matches… Open weights apache-2.0 peft [View model](https://savrn.com/models/menasaat-qwen3-4b-oman-qlora-v2-2) Model ### [MERNIK](https://savrn.com/models/mernik) [Wepiqxx](https://savrn.com/model-publishers/wepiqx) MERNIK ("the one who measures") is the evolution of the ASHQ1 battlefield zoo: fewer utility duels, more verdicts. The method (priority queue) is built; MERNIK is how we prove anything about it. Think of it as a finetune of ASHQ1: same base weights (queue, pins, tied groups), retrained objective (measure-first protocol, three-column verdicts) — and far more capabilities on top (slow capability ring, KLD-aware teachers, relief ceilings, the zoo bench, norms shields). But the legend is not forgotten: every scar in the ledger traces back to it. Allocator signals never certify. Capability scores never steer. Mixing them is how the PPL disease happened. - PPL = canary. Cheap, lies by sharpening.… Open weights apache-2.0 [View model](https://savrn.com/models/mernik) Model · Text classification ### [metask-jev-4b-policy-mix](https://savrn.com/models/metask-jev-4b-policy-mix) [Raymond wei](https://savrn.com/model-publishers/raymond1122) A calibrated typed-decision model: give it a state (text, ticket, policy, JSON) and a typed question — choice, boolean, or rubric score — and it returns a probability for every option in a single forward pass (~24 ms). No generation, no parsing, nothing to hallucinate. 12 of 13 subsets exceed Bespoke Nimble-9B — a model 2.2× its size — same prompt format, same scoring protocol. The primary suite: BoolQ, MultiNLI, PAWS, PubMedQA, SQuAD-2, VitaminC, Civil Comments, Aegis 2.0, MASSIVE (en/de), HelpSteer-2, SummEval (consistency / relevance). Every item human-labeled; byte-reproducible (manifest-locked ids + sha256); same protocol as the Bespoke Nimble evaluation. Wins: verification-style noul… Open weights apache-2.0 4.5B parameters 262,144 tokens transformers [View model](https://savrn.com/models/metask-jev-4b-policy-mix) Model ### [MI4ToolCalling](https://savrn.com/models/mi4toolcalling) [Xijie Gong](https://savrn.com/model-publishers/xijiegong) Open weights apache-2.0 [View model](https://savrn.com/models/mi4toolcalling) Model ### [MiMo-V2.6-Distill-Qwen-9B-RL](https://savrn.com/models/mimo-v2-6-distill-qwen-9b-rl) [Arman Rafiee](https://savrn.com/model-publishers/armand0e) Training in progress. Weights expected within the next 2 weeks. You can monitor progress here: https://training.armand0e.com Apologies for the delay, had some issues in grading. The training run was rolled back to before the bug was introduced. New ETA is ~5 Days Open weights apache-2.0 [View model](https://savrn.com/models/mimo-v2-6-distill-qwen-9b-rl) Model ### [MinhaIA](https://savrn.com/models/minhaia) [Mario](https://savrn.com/model-publishers/delsonmarion) Open weights apache-2.0 [View model](https://savrn.com/models/minhaia) Model ### [miniagent-1.7B-strapi-GGUF](https://savrn.com/models/miniagent-1-7b-strapi-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) static quants of https://huggingface.co/omneity-labs/miniagent-1.7B-strapi 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… Open weights apache-2.0 transformers [View model](https://savrn.com/models/miniagent-1-7b-strapi-gguf) Model ### [MiniAI-Quata2-4b-GGUF](https://savrn.com/models/miniai-quata2-4b-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) static quants of https://huggingface.co/M1n1A1/MiniAI-Quata2-4b For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants are available at https://huggingface.co/mradermacher/MiniAI-Quata2-4b-i1-GGUF If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 See… Open weights apache-2.0 transformers [View model](https://savrn.com/models/miniai-quata2-4b-gguf) Model ### [MiniAI-Quata2-4b-i1-GGUF](https://savrn.com/models/miniai-quata2-4b-i1-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) weighted/imatrix quants of https://huggingface.co/M1n1A1/MiniAI-Quata2-4b For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/MiniAI-Quata2-4b-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/miniai-quata2-4b-i1-gguf) Model · Robotics ### [minicpm-gr00t-banana-posttrain-30k](https://savrn.com/models/minicpm-gr00t-banana-posttrain-30k) [Tingrui Shen](https://savrn.com/model-publishers/trshen925) This repository contains the latest 30,000-step post-training checkpoint from the StarVLA MiniCPM-GR00T experiment on the BananaInBowl HDF5 dataset. The main weight file is steps30000pytorchmodel.pt. The model was initialized from the 145,000-step DROID pretraining checkpoint and trained with two RGB views resized directly to 448x448, a 15-step action horizon, and 8-dimensional state/action vectors. This is a StarVLA training checkpoint rather than a standalone Transformers frompretrained model. Use the matching StarVLA code and the included config.full.yaml to load it. Open weights apache-2.0 [View model](https://savrn.com/models/minicpm-gr00t-banana-posttrain-30k) Model ### [MiniCPM-V-4.7-1B-BF16-zhenwu-FlagOS](https://savrn.com/models/minicpm-v-4-7-1b-bf16-zhenwu-flagos) [FlagRelease](https://savrn.com/model-publishers/flagrelease) ModelBest, together with OpenBMB, has open-sourced MiniCPM-V4.7, a next-generation on-device multimodal large model, releasing two variants at once: 1B and 35B-A3B (MoE). Based on the FlagOS unified multi-chip software stack, the Zhongzhi FlagOS community completed day-0 cross-chip adaptation covering 12 platforms: T-Head, NVIDIA, Huawei Ascend, Hygon, Kunlunxin, MetaX, Moore Threads, Tsingmicro, Iluvatar CoreX, Sunrise, Mac (M5 Pro), and CIX P1. Model images are now available on ModelScope and HuggingFace simultaneously. This adaptation extends both model variants to ARM edge CPU platforms, enabling viable deployment of multimodal understanding on both data-center and end-user devices. The… Open weights apache-2.0 1.6B parameters 262,144 tokens [View model](https://savrn.com/models/minicpm-v-4-7-1b-bf16-zhenwu-flagos) Model · Image and text to text ### [MiniCPM-V-4.7-1B-GGUF](https://savrn.com/models/minicpm-v-4-7-1b-gguf) [Nguyen Quoc Vinh](https://savrn.com/model-publishers/ngquocvinh) Community GGUF quantizations of openbmb/MiniCPM-V-4.7-1B. If this GGUF made your day easier, a coffee would make mine. I build and test these releases myself. Your coffee helps keep me going. Thank you for supporting this work. MiniCPM-V 4.7 1B is OpenBMB's compact multimodal model for image and video understanding. The upstream model describes single-image, multi-image, and video use, with an efficient vision encoder and on-device deployment targets. See the official model card and the upstream deployment guide. The upstream configuration declares a 262,144-token text context. The example below uses a 4,096-token context; long-context behavior has not been evaluated in this release. This… Open weights apache-2.0 llama.cpp [View model](https://savrn.com/models/minicpm-v-4-7-1b-gguf) Model ### [MiniCPM-V-4.7-35B-A3B-BF16-zhenwu-FlagOS](https://savrn.com/models/minicpm-v-4-7-35b-a3b-bf16-zhenwu-flagos) [FlagRelease](https://savrn.com/model-publishers/flagrelease) ModelBest, together with OpenBMB, has open-sourced the next-generation on-device multimodal large model MiniCPM-V4.7, releasing two variants at once: 1B and 35B-A3B (MoE). The Zhongzhi FlagOS community completed day-0 cross-chip adaptation covering 12 platforms — T-Head, NVIDIA, Huawei Ascend, Hygon, Kunlunxin, MetaX, Moore Threads, Tsingmicro, Iluvatar CoreX, Sunrise, Mac (M5 Pro), and CIX P1 — based on the FlagOS unified multi-chip software stack. Model images are now live on ModelScope and HuggingFace simultaneously. This adaptation extends both variants to ARM edge CPU platforms as well, enabling a viable deployment path for multimodal understanding on both data-center and end-user… Open weights apache-2.0 [View model](https://savrn.com/models/minicpm-v-4-7-35b-a3b-bf16-zhenwu-flagos) Model · Text generation ### [MiniCPM5-2B-Math](https://savrn.com/models/minicpm5-2b-math) [Caldalis](https://savrn.com/model-publishers/caldalis) English | MiniCPM5-2B-Math is a math and proof model built on MiniCPM5: a dense 2.5B model based on MiniCPM5-2B and post-trained for long-form mathematical reasoning and proof writing. It runs on a single consumer GPU or a laptop and keeps the native 128K context. - Strong for its size. As a plain single call with up to 128K tokens of thinking, 92.3 on AIME 2025, 89.8 on AIME 2026 and 81.8 on HMMT February 2026 (avg@16), and 100 / 93.3 / 90.9 with a 16-sample vote. The official general-purpose MiniCPM5-2B scores 87.1 / 89.8 / 68.4 under the same protocol. - Writes checkable proofs. On IMO-ProofBench Basic, an average single call with up to 128K tokens writes a complete proof for 21 of the… Open weights apache-2.0 2.5B parameters 131,072 tokens transformers [View model](https://savrn.com/models/minicpm5-2b-math) Model · Text generation ### [MiniCPM5-2B-Math-GGUF](https://savrn.com/models/minicpm5-2b-math-gguf) [Caldalis](https://savrn.com/model-publishers/caldalis) GGUF builds of MiniCPM5-2B-Math, a 2.5B math and proof specialist, for llama.cpp, Ollama and LM Studio. Pair it with the MiniCPM-Math Harness to run a small proof swarm on a laptop. Converted with llama.cpp b10964 (converthftogguf.py, tokenizer pre-type minicpm5) and quantized with llama-quantize. The files load on llama.cpp b9354 or newer, which added the MiniCPM5 tokenizer; use b9833 or newer, which added the MiniCPM5 chat parser that separates the reasoning from the answer. The recommended sampling settings (temperature 0.9, top-p 0.95, min-p 0, top-k off) are embedded in the GGUF metadata, so llama.cpp uses them by default. All results on the model card were measured with the BF16… Open weights apache-2.0 gguf [View model](https://savrn.com/models/minicpm5-2b-math-gguf) Model · Text classification ### [MiniCPM5-2B-pjev-LoRA](https://savrn.com/models/minicpm5-2b-pjev-lora) [Adrian Ciprian Iancu](https://savrn.com/model-publishers/adriandj3) A one-token decision model you can switch on per request, on the MiniCPM5-2B server you already run. Kailune-AI/pjev-2b is a strong typed-decision model: give it a state, a question and lettered options, and it returns a calibrated probability for every option in a single forward pass. It ships as a full merged model (about 2.7 GB in Q80). If your agent already serves openbmb/MiniCPM5-2B for chat, a second full model costs VRAM you probably do not have. This repository re-expresses pjev-2b as a rank-64 LoRA over the stock MiniCPM5-2B weights. Load it once next to the chat model, turn it on only for decision requests, and pay about 200 MB of VRAM instead of 2.7 GB. Measured on a private… Open weights apache-2.0 peft [View model](https://savrn.com/models/minicpm5-2b-pjev-lora) Model ### [minimax-h3-loras](https://savrn.com/models/minimax-h3-loras-2) [Groot Johnson](https://savrn.com/model-publishers/iamgroot1212) All the essential MiniMax H3 LoRas in one place! Open weights apache-2.0 [View model](https://savrn.com/models/minimax-h3-loras-2) Model · Text generation ### [minimind-math-v3](https://savrn.com/models/minimind-math-v3) [Jiwubu](https://savrn.com/model-publishers/jiwubu) minimind-math-v3 是一个基于 MiniMind 项目从零训练的小型中文数学语言模型，权重已转换为 Qwen3 兼容格式，可直接使用 HuggingFace transformers 加载推理。 - 架构：Dense Transformer（Decoder-only），未使用 MoE - 数据：以数学/推理语料为主的预训练数据（pretraint2tmini.jsonl），并经过数学 SFT 训练 - Tokenizer：基于 MiniMind 自训 tokenizer 修改的 patched tokenizer，详见下方说明 本模型的预训练与 SFT 均基于 jiwubu/minimind 代码仓库完成。相对上游 jingyaogong/minimind 的主要修改是新增 math/ 目录，用于生成数学 SFT 用的加减乘除算术数据（genmathdataaddsub.py、genmathdatamuldiv.py 等），并提供配套评测脚本（evalmath.py）。 最简单的方式是用仓库自带的交互式验证脚本（chat template + 贪心解码 + 流式输出）： 或直接用 transformers： - 预训练 2 个 epoch，batch size 32，最大序列长度 340 - 优化器 AdamW，初始学习率 5e-4（带 warmup + 余弦衰减） - 混合精度：bfloat16 本模型使用基于 MiniMind 原版 tokenizer 修改的 patched tokenizer（Sequence[Digits, ByteLevel]）：… Open weights apache-2.0 64M parameters 32,768 tokens transformers [View model](https://savrn.com/models/minimind-math-v3) Model ### [Ministral-3-3B-Reasoning-UNCENSORED-heretic-i1-GGUF](https://savrn.com/models/ministral-3-3b-reasoning-uncensored-heretic-i1-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) Open weights apache-2.0 transformers [View model](https://savrn.com/models/ministral-3-3b-reasoning-uncensored-heretic-i1-gguf) Model · Text generation ### [Ministral-3-8B-Instruct-2512-matura-grpo-lora-closed-em](https://savrn.com/models/ministral-3-8b-instruct-2512-matura-grpo-lora-closed-em) [WMTH-100d2exam](https://savrn.com/model-publishers/wmth-100d2exam) Ministral-3-8B-Instruct-2512 post-trained for the Polish history matura (extended level) during the Warsaw Model Trainers alpha=64) on top of mistralai/Ministral-3-8B-Instruct-2512-BF16 (the bf16 twin of the FP8 -2512 release). closedem = exact match on closed items. Graded scores (deepseek-flash-high@think via benchmark/bench.py) are reported separately. Open weights apache-2.0 262,144 tokens transformers [View model](https://savrn.com/models/ministral-3-8b-instruct-2512-matura-grpo-lora-closed-em) Model · Text generation ### [Ministral-3-8B-Instruct-2512-matura-sft-grpo-lora-closed-em](https://savrn.com/models/ministral-3-8b-instruct-2512-matura-sft-grpo-lora-closed-em) [WMTH-100d2exam](https://savrn.com/model-publishers/wmth-100d2exam) Ministral-3-8B-Instruct-2512 post-trained for the Polish history matura (extended level) during the Warsaw Model Trainers alpha=64) on top of mistralai/Ministral-3-8B-Instruct-2512-BF16 (the bf16 twin of the FP8 -2512 release). closedem = exact match on closed items. Graded scores (deepseek-flash-high@think via benchmark/bench.py) are reported separately. Open weights apache-2.0 262,144 tokens transformers [View model](https://savrn.com/models/ministral-3-8b-instruct-2512-matura-sft-grpo-lora-closed-em) Model · Text generation ### [Ministral-3-8B-Instruct-2512-matura-sft-lora-distill-epod](https://savrn.com/models/ministral-3-8b-instruct-2512-matura-sft-lora-distill-epod) [WMTH-100d2exam](https://savrn.com/model-publishers/wmth-100d2exam) Ministral-3-8B-Instruct-2512 post-trained for the Polish history matura (extended level) during the Warsaw Model Trainers alpha=64) on top of mistralai/Ministral-3-8B-Instruct-2512-BF16 (the bf16 twin of the FP8 -2512 release). closedem = exact match on closed items. Graded scores (deepseek-flash-high@think via benchmark/bench.py) are reported separately. Open weights apache-2.0 peft [View model](https://savrn.com/models/ministral-3-8b-instruct-2512-matura-sft-lora-distill-epod) Model ### [MiniSystemOne](https://savrn.com/models/minisystemone) [Huangyusi](https://savrn.com/model-publishers/huangyusi) Open weights apache-2.0 [View model](https://savrn.com/models/minisystemone) Model ### [minyue](https://savrn.com/models/minyue) [YI SHI](https://savrn.com/model-publishers/shiyi098) Open weights apache-2.0 [View model](https://savrn.com/models/minyue) Model · Agent ### [mira-agent-instinct](https://savrn.com/models/mira-agent-instinct) [Balogun Saheed](https://savrn.com/model-publishers/hoodx) Mira is an agent-oriented LLM with instinctive reasoning compression and safe recursive self-improvement loops. Designed for long-horizon tasks, tool use, and self-play fine-tuning with eval gating. Results in.evalresults/: Terminal-Bench 2.1 Not AGI. Agent-oriented LLM with heuristic compression. Recursive self-improvement is offline and eval-gated. Tool use requires guardrails. Apache-2.0 Open weights apache-2.0 32,768 tokens transformers [View model](https://savrn.com/models/mira-agent-instinct) Model ### [Mirror-Cloud](https://savrn.com/models/mirror-cloud) [StrongNguyen](https://savrn.com/model-publishers/strong568) Open weights apache-2.0 [View model](https://savrn.com/models/mirror-cloud) ## 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? 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