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.
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.
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.
Most Downloaded
| Model | Publisher | Parameters | License | Monthly downloads | Cheapest GPUs at 16-bit |
|---|---|---|---|---|---|
| all-MiniLM-L6-v2 | Sentence Transformers | 23M | apache-2.0 | 203.2M | 1x MI300X, $1.85/hr |
| 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 | Sentence Transformers | 118M | apache-2.0 | 43M | 1x MI300X, $1.85/hr |
| electra-base-discriminator | — | apache-2.0 | 41M | — | |
| bert-base-uncased | BERT community | 110M | apache-2.0 | 29.7M | 1x MI300X, $1.85/hr |
| Qwen3-0.6B | Qwen | 752M | apache-2.0 | 28.2M | 1x MI300X, $1.85/hr |
| t5-small | T5 community | 61M | apache-2.0 | 24.5M | 1x MI300X, $1.85/hr |
| mobilenetv3_small_100.lamb_in1k | PyTorch Image Models | 3M | apache-2.0 | 18.5M | 1x MI300X, $1.85/hr |
| all-mpnet-base-v2 | Sentence Transformers | 109M | apache-2.0 | 18.3M | 1x MI300X, $1.85/hr |
| wav2vec2-large-xlsr-53-japanese | Jonatas Grosman | — | apache-2.0 | 16.2M | — |
Who Publishes Them
| Publisher | Models |
|---|---|
| Team Mradermacher | 162 |
| Qwen | 81 |
| 50 | |
| Quan | 44 |
| RKNNAI | 43 |
| Convergent Intelligence | 39 |
All 3,285 Models, Page 40 of 55
This is an experimental Mixer codebase for Contrastive. It keeps the large 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 adamw with a step schedule. These are starting values in the script, not evidence of a completed run. For a…
This is an experimental Mixer codebase for Generation. It keeps the base 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 adamw with a cosine schedule. These are starting values in the script, not evidence of a completed run. For a…
static quants of https://huggingface.co/SvalTek/MN-CharThink-12B-Base For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants are available at https://huggingface.co/mradermacher/MN-CharThink-12B-Base-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…
weighted/imatrix quants of https://huggingface.co/SvalTek/MN-CharThink-12B-Base For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/MN-CharThink-12B-Base-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…
Working implementation of Mobilevit for Classification using a tiny 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 novograd with a step schedule. These are starting values in the script, not evidence of…
This repository is a compact, custom PyTorch implementation of Mobilevit for Multitask. The xlarge 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 adamw with a onecycle schedule. These are…
This is an experimental Mobilevit codebase for Retrieval. 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 lamb with a cosine schedule. These are starting values in the script, not evidence of a completed run. For a…
A small Mobilevit implementation for Retrieval, 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 adam with a linear warmup schedule. These are starting values in the script, not…
This repository is a compact, custom PyTorch implementation of Mobilevit for Retrieval. The huge 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 novograd with a cosine schedule. These are…
A small, unprofessional fine-tune of Qwen 2.5, made to have a slightly different personality. Based on huihui-ai/Qwen2.5-0.5B-Instruct-abliterated-v3. Mochi prioritizes having multiple different variants of the base model for different purposes, always using 0.5B parameters. It is only made for fun, and has no actual use if you're looking for something powerful or coherent. - No system prompt required. The personality is now baked in by default. You can still pass a system prompt to adjust behavior, but the Mochi vibe works without one. - Cleaner dataset. 868 examples, all without system prompts, so the model doesn't depend on one to behave correctly. - Factual accuracy is degraded compared…
A small Mocov3 implementation for Retrieval, packaged with an explicit configuration and an initialization checkpoint. The tiny 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 lion with a exponential schedule. These are starting values in the script, not…
This repository is a compact, custom PyTorch implementation of Mocov3 for Classification. The huge 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…
Working implementation of Mocov3 for Classification 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 rmsprop with a polynomial schedule. These are starting values in the script, not evidence…
A research-oriented Mocov3 prototype targeting Generation. The included small 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 lamb with a step schedule. These are starting values in the script, not evidence of a completed run. For a…
Single-label imaging-modality classification for medical images. One shared ConvNet backbone, two softmax heads (family, modality), shipped as a static-INT8 ONNX graph. Predicts what kind of image this is, not what is in it. Anatomy is a different model. Softmax, temperature and thresholds live outside the graph so calibration and policy stay configuration. modality-taxonomy.json travels with the weights - a 20-element logits vector is 20 meaningless numbers without it. Tier is a statement about evidence, not difficulty. modeltrained means the corpus had at least two independent sources and enough patients to make the number falsifiable. experimental means it did not. nottrained classes are…
JSONL inference traces only (no generated WAVs). Each file is named after the model. Records follow the FlexiSLM eval trace schema (index, task, input, output, evaluation, metadata). - flexislmstage2released.jsonl — FlexiSLM-7B Stage2 released checkpoint (ckpt-150000), 12.5 Hz in/out, flow-matching s2s on VoiceBench + OpenAudioBench.
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 model-Kurumi-krea2 to invoke the concept.
models-for-asr-chain/ 集中保存 AX650 音频智能链路运行时使用的模型和配套资源。它位于驱动与业务编排层之间：driver/ 和 framework/ 提供模型加载、推理及调度代码，config/sdk-config.yaml 指定这里的模型路径，部署脚本再将生产所需资源同步到目标板。 模型路径由 config/sdk-config.yaml 配置。生产部署应使用 scripts/deploy-ax650.sh 的白名单增量同步，避免遗漏 tokenizer、配置文件或分层 AXModel，也避免将开发期中间文件复制到目标板。 这些文件大多面向 AX650 和指定的 AX Engine/VNPU 模式，不能仅按文件名替换为其他平台或其他编译参数生成的模型。模型升级后应重新执行完整编译、部署依赖检查，并验证对应推理链路。
This model is a fine-tuned version of ModernBERT optimized for logical reasoning, deductive analysis, and structure-based token prediction. It relies on the ModernBertForMaskedLM architecture, making it highly effective at filling in missing contextual logic tokens (fill-mask). - As an encoder-based Masked Language Model, it is not designed for long-form generative text (like ChatGPT). It excels at predicting masked tokens inside structured prompts.
This is an ONNX version of JohnGorri/ModernBERT-Large-Instruct-Logician-v0.5. It was automatically converted and uploaded using this Hugging Face Space. See the pipeline documentation for fill-mask: https://huggingface.co/docs/transformers.js/api/pipelines#modulepipelines.FillMaskPipeline This model is a fine-tuned version of ModernBERT optimized for logical reasoning, deductive analysis, and structure-based token prediction. It relies on the ModernBertForMaskedLM architecture, making it highly effective at filling in missing contextual logic tokens (fill-mask). - As an encoder-based Masked Language Model, it is not designed for long-form generative text (like ChatGPT). It excels at…
A 162M-parameter decoder trained twice from the same initialization on the same 2.62 billion FineWeb-Edu tokens, once along each of two different routes through NVIDIA, AMD and Apple GPUs. The two runs are bitwise identical at every one of the 5,000 optimizer steps: training state, summed gradients, all 64 per-shard losses and the learning rate. Their final checkpoint files have the same SHA-256. This repository distributes that one final model with the evidence from both routes. The model is GPT-3 Small-sized and trained with GPT-3 Small's hyperparameters, but its blocks are modern Llama-style blocks; see the comparison below. It is a research artifact for reproducible training, not a…
MolmoAct2 is an open robotics foundation model from the Allen Institute for AI (Ai2) that maps camera images and language instructions to robot action chunks. The LeRobot implementation supports training and evaluation of the regular MolmoAct2 model. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot molmoact2 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…
pi0 fine-tuned from lerobot/pi0base on (296 episodes; 263 train / 33 held out for validation, stratified over 9 tasks). To run on the robot, see deploy/README.md — tokenizer, contract and a script that makes a checkpoint load with no Hugging Face access. checkpoint-XXXXXX/ every 1,250 steps (80k samples) up to 16,250 (2 epochs). Each is self-contained for the robot: weights, pre/post-processors, PaliGemma tokenizer, rosetta contract, preparedeploy.py, testoffline.py and a DEPLOY.md with the exact commands. The final one also has trainingstate/. Complete checkpoints (with optimizer state) for every published step are also on the team OneDrive under DATA/Processed/pi0v2b64/. Differences from…
Checkpoint for MORAD: Reinforcement Learning with Multi-Objective Rewards for model after 390 updates of online reinforcement learning with the six-component MORAD reward. A single shared policy designs nucleotide sequences for any target 3D backbone, with no per-target optimization. SHA-256 checksums are listed in SHA256SUMS. Structural and pairing metrics use candidate 0 of each target (structures predicted with RhoFold+, pairs with EternaFold); NED is from the thermodynamic evaluation (ViennaRNA). All tables of the paper are in the downloadassets.sh stores this file as artifacts/models/morad/moradupdate390.pt. Any MORAD checkpoint can be evaluated with RIDECHECKPOINT=path/to/policy.pt…
The released model of MORAD: Reinforcement Learning with Multi-Objective diffusion model, post-trained online for 390 updates with the six-objective MORAD reward. One shared policy designs nucleotide sequences for any target 3D backbone, with no per-target optimization. EternaFold, normalized ensemble defect (NED) with ViennaRNA. gRNAde at T = 0.1. Training this model from pretrained RIDE takes about 2 hours on 4 × RTX 3090 (bash scripts/trainmorad.sh).
180B total / ~78B active per request — giant knowledge, agile inference. - 64 domain experts, zero overlap — every training example maps to exactly one expert. Auditable by construction. - Fully auditable data pipeline — raw → tagged → tokenized corpora, code included, in Morpho-72B-MoE-Data. - Resume-first engineering — every expert checkpoint (weights + loss + step) is versioned in Morpho-72B-MoE-Checkpoints. Any box can pick up exactly where the last one died. - 128K context via RoPE YaRN for long documents, codebases, and books. Eight sectors — Finance, Law, Medicine, Tech, Science, Business, Arts, Humanities — each with 8 specialists. Full roster with per-expert data counts is logged…
Stage 5 of the Morpho-180B pipeline (display name; SFT backbone: - Per-expert DPO, same 64 experts, same one-example-one-expert mapping as SFT. - Real preference data only (HH-RLHF, HelpSteer2, UltraFeedback, Nectar), routed per expert by the v5 taxonomy. - DPO adapters initialize from the converged SFT adapters — never from scratch. Stage 5 IN PROGRESS — 17/31 experts DONE (resumed 3x, AUTOSKIP active, final leg running on 97GB GPU box). 24 eligible experts remain in the queue; 33 thin experts (<200 pairs) keep SFT-only per policy. Each row is verified from the expert's bestloss.txt + DONE.txt on this repo. Progress tracked on the model card.
Unofficial Research Turbo LoRA for MOSS-Image using Mean Flow Distillation MOSS-Image-Base-Turbo-MFD-v0.1 - 50 aux/20 student, 4-6 steps inference ComfyUI version is.comfy.safetensors Thank you HuggingFace, OpenMOSS Team, and An Zhao and Shengyuan Zhang and Zhongjian Sun and Yixiang Zhou and Zejian Li and Ling Yang and Tianrun Chen and Lingyun Sun
This is an MLX model for Apple Silicon. It is not directly compatible with the PyTorch CUDA runner. Converted from mlx-community/MOSS-TTS-8B-8bit at revision b9262754ad5245942472c7b4ff2b53cec3a07ec0: Q6 backbone and Q8 speech path, affine quantization with group size 64. Structurally validated on Linux; Apple Silicon runtime and speech-quality validation are pending. See RESULT.md, recipe.json, and the audit reports for details. The upstream model card follows. This MLX implementation brings MOSS-TTS to Apple Silicon through the MLX runtime, adapting the TTS backbone, multi-head audio token generation, and MOSS Audio Tokenizer workflow to run inside the MLX-Audio stack. As part of…
Hybrid conversion of MOSS-TTS-v1.5 and MOSS Audio Tokenizer. The 8.49B autoregressive language model runs on GPU through native Swift MLX; seventeen Core AI codec groups render mono 24 kHz PCM on ANE. English controls are qualified; other upstream languages are not qualified here. Glade is a native Swift library for speech inference on Apple silicon, using Core AI, Core ML, and model-specific host implementations. Its runtime source is currently private. This repository distributes the model assets and runtime metadata used by that library. Python is export/measurement tooling, not an application dependency. Mac qualification is included; iPhone and Watch qualification is not claimed.…
GGUF quantization of OpenMOSS-Team/MOSS-VL-Realtime-NF4 (11B cross-attention VLM: Qwen3 text trunk with 12 pure cross-attn layers + SigLIP tower with deepstack), repackaged for the SkyVision.cpp inference engine. - NF4 weights are dequantized bit-exactly against bitsandbytes CUDA first (checkmossvlnf4.py); - the fused cross-KV projection weight is built at load time (engine-side); 12 GB GPU deployments: --n-ctx 4096 (and MOSSXCTX=6144 for long videos). Full chat, multi-turn sessions, realtime streaming (proactive narration with ) and a web UI are available via --serve. Follows the base model's license (see the OpenMOSS-Team/MOSS-VL-Realtime-NF4 page).
Optimized ONNX Runtime (ORT) and Static INT8 QDQ / Dynamic INT8 models for MossFormer2 2-Speaker Speech Separation at 16kHz. Optimized for mobile inference with NNAPI / XNNPACK / CPU.
MSBuddy project, maintained by Shipei Xing. Original database files from msbuddydatav0.2.4 and LightGBM model from msbuddydatav0.3.0. Apache-2.0 is recorded on the basis of the upstream project licence; the release pages do not state separate asset-specific terms. Its application to these files is an inference, not an independently confirmed per-file licence grant. The original licence is retained as db0.2.4-ml0.3.0/LICENSE. The software license is reported separately; it does not replace the model/data license. These are byte-identical upstream files. No model conversion was performed.
Wan2.1-Fun-1.3B-Inp/ - wav2vec2-base-960h/ - extra/13B-single-v1.pth - extra/14B-multi-v1.pth - extra/configaf2v13B.json - extra/configaf2v14B.json
This is Horizon-Labs/multilingual-reranker-small in a smaller ONNX file, for running on a CPU. Same model, same tokenizer, same scores to within a small margin. The file is about a third smaller than the original quantized one. We use it in Sno Station to rerank memories for coding agents. The token embeddings, the attention projections and the first MLP projection are stored in 8 bits. The second MLP projection stays in float. Its inputs carry a few very large values, and 8 bits move the scores too much there. Weights are unsigned 8-bit, per channel. Signed weights overflow on AVX2 CPUs without VNNI and flatten every score. quantize.py rebuilds the file from the original onnx/model.onnx at…
Classify text in 30+ languages into any labels you choose, with no training. Use it with the transformers zero-shot-classification pipeline, like facebook/bart-large-mnli, but multilingual, smaller, and with an 8k-token context window (fine-tuned at up to 1,024 tokens). non-commercial sets). See Training. Part of Horizon Labs' open models (collection). Labels: notentailment (0) and entailment (1). For each candidate label the model scores whether the text entails "This example is {label}." (or your hypothesistemplate). Any NLI-style use works too: pass text and textpair to a text-classification pipeline. Accuracy, single-label (multilabel=False: the label with the highest entailment score…
Classify text in 30+ languages into any labels you choose, with no training. Use it with the transformers zero-shot-classification pipeline, like facebook/bart-large-mnli, but multilingual, smaller, and with an 8k-token context window (fine-tuned at up to 1,024 tokens). non-commercial sets). See Training. Part of Horizon Labs' open models (collection). Labels: notentailment (0) and entailment (1). For each candidate label the model scores whether the text entails "This example is {label}." (or your hypothesistemplate). Any NLI-style use works too: pass text and textpair to a text-classification pipeline. Accuracy, single-label (multilabel=False: the label with the highest entailment score…
This is an experimental Mixer codebase for Multitask. It keeps the base 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 adafactor with a linear warmup schedule. These are starting values in the script, not evidence of a completed…
Working implementation of Perceiver for Multitask using a nano 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 lamb with a cosine schedule. These are starting values in the script, not evidence of a…
A research-oriented Flamingo prototype targeting Multitask. The included large 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 adam with a linear warmup schedule. These are starting values in the script, not evidence of a completed run. For…
This repository is a compact, custom PyTorch implementation of Mocov3 for Multitask. The small 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 lamb with a step schedule. These are starting…
Working implementation of DeiT for Multitask using a tiny 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 rmsprop with a step schedule. These are starting values in the script, not evidence of a completed…
This is an experimental Flamingo codebase for Multitask. It keeps the xlarge 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 lamb with a linear warmup schedule. These are starting values in the script, not evidence of a completed…
This is an experimental Flamingo codebase for Multitask. It keeps the nano 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 lion with a onecycle schedule. These are starting values in the script, not evidence of a completed run. For a…
A small Swin T implementation for Multitask, 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 adamw with a constant warmup schedule. These are starting values in the script, not…
This is an experimental Cnn Transformer codebase for Multitask. 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 adafactor with a polynomial schedule. These are starting values in the script, not evidence of a…
The on-device judge of Muraqib, a desktop tool that checks posts from watched social-media accounts against editorial rules an auditor writes, and shows the auditor the cases to decide on. - Use it with the base model's vision file (mmproj-F16.gguf from unsloth/gemma-4-E4B-it-GGUF) for photos, video frames and sound. - It expects Muraqib's own system prompt and JSON answer shape (notes, claims, then one verdict per rule). Used any other way, it behaves like a slightly changed Gemma 4 E4B.
Muse Glimmer is a 30-billion-parameter causal language model with a dedicated perception encoder, distilled from Muse Spark and purpose-built for autonomous agentic tasks on consumer hardware. The model integrates multi-step reasoning, reliable tool use, multimodal understanding, and failure recovery into a single model that runs locally without requiring cloud infrastructure or network access. Building effective agents requires key capabilities working together to achieve the user’s goals. Muse Glimmer is trained and evaluated on these capabilities: End-to-end Agentic Task Completion. Muse Glimmer achieves strong success rates on full-task benchmarks including DeepSearch QA, MCP-Atlas…
This model checkpoint represents the full integration of Topological Continual Backpropagation (Topo-CBP) with the Hugging Face Trainer on the 30B multimodal architecture Muse-Glimmer-30B. The training workflow combines standard Hugging Face orchestration with bare-silicon parameter governance via TopoAdamW across sequential downstream classification tasks. code: https://github.com/frank-morales2020/AST/blob/main/TOPOMUSEHFTRAINER.ipynb article: https://medium.com/ai-simplified-in-plain-english/the-architectural-amnesia-of-metas-muse-solving-catastrophic-forgetting-without-the-cloud-crutch-9b839345138e The model and training ran successfully to the end across all three tasks, demonstrating…
This model is a fine-tuned version of distilbert/distilroberta-base on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 8 - evalbatchsize: 8 - lrschedulertype: linear - numepochs: 3 - Transformers 5.17.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2
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 1305
- cc-by-nc-4.0 209
- cc-by-4.0 164
- bsd-3-clause 108
- gemma 63
- cc-by-nc-sa-4.0 52
- llama3.2 40
- agpl-3.0 32
- unknown 32
- llama3.1 28
- cc-by-sa-4.0 20
- gpl-3.0 20