Unofficial GGUF conversion of tencent/WeMM-Embedding-2B for llama.cpp. It is the default embedding model of imgsearch, a local-first image search app. Not affiliated with or endorsed by Tencent. All credit for the model goes to the WeChat Vision Team; it is licensed under Apache 2.0. Both files are needed for image input. Output: 2048 dimensions, L2-normalize; Matryoshka truncation (re-normalize) to 1024/512/256 works well. The prompt must match the reference processor output exactly. No system turn, no instruction; queries and documents are encoded the same way. Take the last-token hidden state (pooling none, last position, or --pooling last): is the llama.cpp mtmd media marker; mtmd adds…
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.
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 55 of 55
WeVisDoc is an end-to-end document parser for page images. Fine-tuned from Qwen3-VL-2B-Instruct and Qwen3-VL-4B-Instruct, it turns a page into structured Markdown, with LaTeX formulas and HTML tables. WeVisDoc-4B achieves an Overall score of 95.38 on OmniDocBench v1.6 and a mean Overall score of 75.54 across the three PureDocBench tracks, ranking first among the compared end-to-end parsers in all four settings. The following tables include end-to-end document parsing specialists only. WeVisDoc results are means over three inference runs. Avg₃ is the mean of the three PureDocBench track-level Overall scores. marks baseline results obtained with our evaluation pipeline; unmarked baseline…
WeVisDoc is an end-to-end document parser for page images. Fine-tuned from Qwen3-VL-2B-Instruct and Qwen3-VL-4B-Instruct, it turns a page into structured Markdown, with LaTeX formulas and HTML tables. WeVisDoc-4B achieves an Overall score of 95.38 on OmniDocBench v1.6 and a mean Overall score of 75.54 across the three PureDocBench tracks, ranking first among the compared end-to-end parsers in all four settings. The following tables include end-to-end document parsing specialists only. WeVisDoc results are means over three inference runs. Avg₃ is the mean of the three PureDocBench track-level Overall scores. marks baseline results obtained with our evaluation pipeline; unmarked baseline…
This whisper model was trained 2x faster with Unsloth and Huggingface's TRL library.
whisper-heb-v7-multi (Hebrew / English / Arabic / Russian / Yiddish with built-in language ID) plus speech-free negatives: the model has learned to answer with nothing on line noise, hold music, tones and silence instead of inventing words. Same architecture (Whisper large-v3-turbo, 809M), same languages, same usage; drop-in replacement for v7. On telephony audio Whisper fine-tunes hallucinate short phrases when there is no speech. v7 produced a transcript for 98% of real line-noise clips (27% of them "תודה רבה") and 46% of synthetic noise. v8 was warm-started from v7 and trained 5,000 steps on the v7 corpus plus 83,920 speech-free rows (real Voicenter line noise ×4, synthetic…
Model · Speech recognition
whisper-medium_indic_voices_63097_ratio_30_70_lr_1e-4_lgid_None_hinglish_mixed_scripts_FT
This model is a fine-tuned version of openai/whisper-medium on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 16 - evalbatchsize: 16 - gradientaccumulationsteps: 2 - totaltrainbatchsize: 32 - lrschedulertype: constantwithwarmup - lrschedulerwarmupsteps: 500 - numepochs: 100 - Transformers 5.0.0.dev0 - Pytorch 2.9.0+cu126 - Datasets 3.6.0 - Tokenizers 0.22.2
Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains without the need for fine-tuning. Whisper was proposed in the paper Robust Speech Recognition via Large-Scale Weak Supervision by Alec Radford et al from OpenAI. The original code repository can be found here. Disclaimer: Content for this model card has partly been written by the Hugging Face team, and parts of it were copied and pasted from the original model card. Whisper is a Transformer based encoder-decoder model, also referred to as a sequence-to-sequence model. It…
This model is a fine-tuned version of openai/whisper-small on the generator dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 1e-05 - trainbatchsize: 8 - evalbatchsize: 8 - gradientaccumulationsteps: 2 - totaltrainbatchsize: 16 - lrschedulertype: linear - lrschedulerwarmupsteps: 300 - numepochs: 3.0 - Transformers 4.57.6 - Pytorch 2.2.1+cu121 - Datasets 5.0.1 - Tokenizers 0.22.2
Core ML / WhisperKit pack converted from then 8-bit linear weight-quantized (AudioEncoder + TextDecoder) for smaller on-device footprint on Apple Silicon. (not "यार मैं कल ऑफिस नहीं जाऊँगा") Use language: "en" in WhisperKit decoding options. The fine-tune maps Hindi speech onto English/Latin tokens; forcing Hindi often breaks romanization. Typical layout (folder name may match what you uploaded): Or after Hub download, point --model / repo flags at this repository per current whisperkit-cli help. - 8-bit linear weight quantization is lossy storage; compute remains floating point. - Expect near-parity with the float Core ML build, not bit-identical transcripts. - Always spot-check on your…
/ Import a cleaner font / / Light Mode Palette / / Dark Mode Palette - High Contrast Neon / --bg-pink-light: #1a050d; / Deep burgundy / --bg-pink-dark: #0a0205; / Near black / --glass-bg: rgba(30, 5, 15, 0.6); / More transparent for neon effect / --glass-border: rgba(255, 105, 180, 0.5); / Brighter border / --text-main: #ffb6c1; / Light pink text / / Main Container - Glassmorphism Effect / / Animated Border Glow / background: linear-gradient(45deg, var(--primary-pink), transparent, var(--secondary-pink), transparent) border-box; / Header Section / / Waifu Image Container / / Content Sections / / List Styling / / Disclaimer Section / / Badge Styling / / Fix for Hugging Face's own dark mode…
Jieyuan Pei, Meiyi Lu, Sining Ang, Yubo Zhao, Zhangyi Hu, Mingwei Xu, Haokai Ding, Wei Li, Zihan You, Jianwei Zheng, Li Yu, Yifeng Pan, Ji Tao, Rongjunchen Zhang, Yan Wang Checkpoints of World4Scorer (paper, code). World4Scorer makes the candidate scorer of a generate-and-select planner a trajectory-conditioned latent world model. One shared predictor gives every candidate trajectory a predicted state, and score heads read its outcomes (collision, drivable area, progress, time to collision, comfort) from that state. Simulator outcomes supervise every candidate; the one future the driving log actually recorded anchors the shared predictor during training. No future is needed at test time.…
Ziyang Ye 1, Junchao Huang 1,2, Evelyn Zhang 2, Zhihao Xie 1, Ruicheng Zhang 3, Boyao Han 1, Litao Ban 4, Ziye Wang 4, Xinting Hu 5, Shaoshuai Shi 4, Zhuotao Tian 2, Li Jiang 1,2† Project page | Code, so a strict loadstatedict works. The inference code needs the five weight files above and nothing else of this repository. EMA weights of the training stages, fp32, under the same names as the generator file; training from them starts from the exact weights. They also hold the visibility probe the training uses (8 tensors). The stage 2 checkpoint and the training code are added to this repository next. This fetches the inference files above, plus the Wan2.2 VAE, tokenizer and config.json from…
Wrench is a pruned, task-specific developer-tool SLM derived from Qwen3.6-35B-A3B. It contains 3,881,244,016 parameters and stays below the 4.25B parameter ceiling. This is a public experimental artifact. It is downloadable and reproducible, but it is not a claim that the final 4M retrieval-quality or MiniMax-parity gates have passed. The canonical distribution format is Hugging Face Safetensors. The package embeds the tokenizer hook, deterministic mechanical lookup runtime, long-context overlay, hash-bound package metadata, and the FreeToken launcher. It is intended to feel like one model directory, not a separately installed harness. The bundled launcher requires a compatible FreeToken…
This repository contains the post-trained Xiaomi-Robotics-1 (XR-1) checkpoint for further post-training with the Xiaomi-Robotics-1 codebase. XR-1 is a Vision-Language-Action (VLA) foundation model for mobile manipulation, initialized from a model pre-trained on over 100K hours of embodiment-free UMI trajectories and subsequently post-trained on over 10K hours of cross-embodiment data. The reference environment requires Python >= 3.9, a CUDA GPU, transformers==4.57.1, and FlashAttention 2. The Qwen3-VL backbone is loaded from Qwen/Qwen3-VL-4B-Instruct. Clone the source repository and install the dependencies: Download modelstates.pt from the Files and versions tab and place it at: Download…
This repository contains the Hugging Face checkpoint used by Xiaomi-Robotics-1 for RoboCasa evaluation. It includes the model weights, custom Transformers model and processor code, tokenizer files, and RoboCasa action normalization statistics. The custom model and processor must be loaded with trustremotecode=True. For reproducible multi-GPU evaluation, use the standard eight-server launcher in the companion source repository. Use the companion Xiaomi-Robotics-1 source repository and follow evalrobocasa/README.md. The server launcher accepts either this Hugging Face repository ID after downloading it locally or a local snapshot path. Apache License 2.0. See LICENSE.
This repository contains the Hugging Face checkpoint used by Xiaomi-Robotics-1 for RoboCasa365 evaluation. It includes the model weights, custom Transformers model and processor code, tokenizer files, and RoboCasa365 action normalization statistics. The custom model and processor must be loaded with trustremotecode=True. For reproducible multi-GPU evaluation, use the standard eight-server launcher in the companion source repository. Use the companion Xiaomi-Robotics-1 source repository and follow evalrobocasa365/README.md. The server launcher accepts either this Hugging Face repository ID after downloading it locally or a local snapshot path. Apache License 2.0. See LICENSE.
This repository contains the Hugging Face checkpoint used by Xiaomi-Robotics-1 for VLABench evaluation. It includes the model weights, custom Transformers model and processor code, tokenizer files, and VLABench action normalization statistics. The custom model and processor must be loaded with trustremotecode=True. For the VLABench simulation environment, follow the companion source repository at evalvlabench/README.md. The evaluation client uses a separate vlabench conda environment with Python 3.10 and the pinned MuJoCo / dmcontrol dependencies described there. For reproducible multi-GPU evaluation, use the standard multi-server launcher in the companion Xiaomi-Robotics-1 source…
X-VLA is a soft-prompted, flow-matching Vision-Language-Action framework that treats each robot or hardware setup as a "task" encoded with a small set of learnable Soft Prompt embeddings, letting a single model reconcile diverse robot morphologies, sensors, and action spaces. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot xvla 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…
X-VLA is a soft-prompted, flow-matching Vision-Language-Action framework that treats each robot or hardware setup as a "task" encoded with a small set of learnable Soft Prompt embeddings, letting a single model reconcile diverse robot morphologies, sensors, and action spaces. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot xvla 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…
Korean instruction-following language model. Built from Yejin 8B v3 (pretrained from scratch by Yejin: cooler8/yejin-korean-8b-v3-base): continued pretraining for 6,000 steps on public Korean data, Yejin chat vector from our own SFT model, then LoRA (rank 64, lr 2e-4). The LoRA data mixes our phase-1 format data (45,013 samples) with 39,290 short Korean solutions written by Qwen/Qwen3.6-27B (Apache-2.0) for questions from our own puzzle generator, KMMLU train, AI Hub school exams and AI Hub reading comprehension; only solutions that reached the known answer were kept (sequence-level distillation; no teacher weights or logits). No benchmark test items were used, and the teacher solutions…
static quants of https://huggingface.co/rwang220/Yida-Model-14B 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 quant And…
Central model repository for ILoveSeafood AI Engineering Ecosystem. 1. YOLO Box Detection (yolo/yolo11n.pt): Real-time seafood box detection from surveillance camera feeds. 2. ARIMA Demand Forecasting (arima/arimaFrozenSeafood.pkl): Optimal stock inventory prediction.
DHQ = Dual-Head Quant. Z-Image-Engineer-V6 (a Qwen3-4B fine-tune that is both a Z-Image text encoder and a prompt writer), quantized so that every tensor gets the precision that matters to what the Z-Image DiT actually reads (measured per tensor with gradients through the DiT's text input), with an importance matrix computed on V6 itself. Each file is exactly the size of the standard llama.cpp file of the same name, and the text-encoder error is lower at every level. Tuned for Z-Image only. It was not measured with FLUX.2 [klein] 4B; for klein use the plain Qwen3-4B file in DHQ files are quantized for use as a diffusion model's text encoder: each tensor's precision is chosen by how much its…
weighted/imatrix quants of https://huggingface.co/ZenithLLM/zen-ingress-1-3b-dpo-merged For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/zen-ingress-1-3b-dpo-merged-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…
A ~565M-parameter language model that interleaves Mamba-3 (MIMO) and Gated DeltaNet-2 (GDN-2) sequence mixers with two MCEN stages (a reversible fixed-point "reasoning" core with a delta-rule memory), trained from scratch on the Zenyx-V3 pretraining mixture with the DeepSeek tokenizer. This repository holds training checkpoints, logs and diagnostics of an ongoing run; it is not a finished release. Data mixture — 121 sources in the frozen manifest (data/manifestv2.json, created 2026-10-10 06:04:26, hash 9454e840c2b9f032…); grouped shares: The comparison models are the same author's: Zenyx-v2-base (85M unique parameters, 32 effective layers, 32k vocabulary) and Zenyx-V3-Base (1.58B-parameter…
Zenyx V3 is an efficient 1.5B-parameter Mixture-of-Experts (MoE) foundation model built for low-latency inference and high throughput. It is written from scratch in JAX/Flax and trained on TPU v5e-8. active per token, with a Sinkhorn transport-based gate. latent subspace, cutting HBM bandwidth and memory footprint. stability at scale. YaRN and RoPE scaling factors are precomputed so context can be extended at inference time beyond the trained length. All tasks are evaluated with the standard base-model protocol: the model scores the log-likelihood of every candidate continuation and the highest-scoring one is taken as the answer. Nothing is generated and no output parsing is involved, so…
This repo is a post-training of Zero-WAM (zero-wam-pretrain, 10.8B) on 49 RoboCasa atomic tasks. The only task signal is a prompt video: the model never sees language, in training or at eval. The prompt is a video of the same task, taken from a different episode and a different scene. Everything in this repo was produced in a single run. All files are uncompressed (stored) zip archives. Full write-ups are inside results.zip: - report.md: overview of the experiment and its results. - reportpipeline.md: detailed record of how the data-conversion and training pipelines were built. Online, randomized RoboCasa target-split scenes, with prompt videos generated live by MimicGen. 10 scenarios per…
Zircon v2 is a 0.6B-parameter decision model from Fahrenheit Research. It runs fully on-device on Apple silicon. You give it an email, a message or a pending task plus a set of options, and it returns a calibrated probability for each option in under 50 ms per decision. (1) MacBook Pro (Apple M5), 8-bit weights, median. Speed varies by hardware. (2) Fahrenheit Research internal testing, September 2026, on held-out emails not seen in training. (3) Fahrenheit Research internal testing, September 2026. 400 cases (2,000 decisions) from the public LocalLLaMA typed-decisions test set. (4) Fahrenheit Research internal testing, September 2026, on game seeds not seen in training. Built on Qwen3-0.6B…
Status: experimental test completed; continuous background mode is disabled. The test did not change the serving checkpoint, app, or model weight files in this repository. This update documents the experiment only. A separate controller completed two successive coach → learner cycles without chat input, then stopped at its configured limit. It supplied a planning request and a follow-up, retaining the actual first coach answer as conversation history. The second lesson drafted with the checkpoint learned from the first. The student used the existing Map5/SEE memory and linear reranker, plus the frozen FP32 GPT2-medium chooser. The external coach was Qwen/Qwen2.5-32B-Instruct, revision…
A LoRA adapter that turns openbmb/MiniCPM-V-4.7-1B (open-weight ~1.5B VLM, Apache-2.0) into a focused document-OCR model for Devanagari (Hindi) + English text, including the conjuncts and mixed-script layouts where the base model is weakest. pure-Devanagari, mixed Hindi+English+digits, conjunct-heavy vocabulary (क्षेत्र, ज्ञान, त्रिभुज, …) gradient checkpointing, NaViT variable-shape slicing (4× downsample, ≤36 slices) Measured with eval/runeval.py (NF4, seed 0) + eval/metrics.py (NFC-normalized, whitespace-stripped Levenshtein CER). Per-bucket mean CER on the 50-image set (base → LoRA): The 50-image set is genuinely out-of-distribution (real fonts/layouts vs synthetic training), so the…
weighted/imatrix quants of https://huggingface.co/z51722369/ZOZ-Function-Master-3B For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/ZOZ-Function-Master-3B-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…
High-efficiency, hardware-tested GGUF releases of Qwen3.8-27B (27 Billion Parameters, Dense Architecture). All models in this repository have been physically converted, verified on hardware (AMD Instinct MI300X with ROCm / HIP), and benchmarked for prompt throughput, token generation velocity, and benchmark accuracy against baseline models. This repository provides three specialized model tiers: - zraldv1-ac (Accuracy-Priority Tier): 17.08 GiB (18.3 GB). Near-lossless retention (99.68% accuracy), matches or outperforms standard Q6K and Q80 quality while saving ~10 GB VRAM compared to Q80. - zraldv1-ba (Balanced Sweet-Spot): 14.46 GiB (15.5 GB). Optimal balance (99.12% accuracy), fits…
Official high-efficiency GGUF release of Qwen3.8-27B optimized for Accuracy Priority Tier. This repository contains zraldv1-ac.gguf (17.08 GiB / 18.34 GB), physically benchmarked on AMD Instinct MI300X hardware. For cross-comparison tables against standard Q80, Q6K, Q5K, Q4K, and Q2K models, visit the master repository: - Base model by the Qwen Team (Alibaba) under Apache 2.0. - Runtime by Georgi Gerganov and the llama.cpp community.
Official high-efficiency GGUF release of Qwen3.8-27B optimized for Balanced Sweet-Spot Tier. This repository contains zraldv1-ba.gguf (14.46 GiB / 15.52 GB), physically benchmarked on AMD Instinct MI300X hardware. For cross-comparison tables against standard Q80, Q6K, Q5K, Q4K, and Q2K models, visit the master repository: - Base model by the Qwen Team (Alibaba) under Apache 2.0. - Runtime by Georgi Gerganov and the llama.cpp community.
Official high-efficiency GGUF release of Qwen3.8-27B optimized for Compressed Size Tier. This repository contains zraldv1-cs.gguf (10.18 GiB / 10.93 GB), physically benchmarked on AMD Instinct MI300X hardware. For cross-comparison tables against standard Q80, Q6K, Q5K, Q4K, and Q2K models, visit the master repository: - Base model by the Qwen Team (Alibaba) under Apache 2.0. - Runtime by Georgi Gerganov and the llama.cpp community.
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
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- bsd-3-clause 108
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- llama3.2 40
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- llama3.1 28
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