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AI Models 2119
- Nous-Hermes-Llama2-llamafile↗
jartine
WARNING: This README may contain inaccuracies. It was generated automatically by forking TheBloke/Nous-Hermes-Llama2-GGUF and piping the README through sed. Errors should be reported to jartine, and do not reflect TheBloke. You can support his work on Patreon. llamafile is a new format introduced by Mozilla Ocho on Nov 20th 2023. It uses Cosmopolitan Libc to turn LLM weights into runnable llama.cpp binaries that run on the stock installs of six OSes for both ARM64 and AMD64. llamafile offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible. The creator of the source model has listed its license as `['mit']`, and this quantization has therefore used that same license.
- Qwen3.6-35B-A3B-GGUF↗
jimbothigpen
A complete set of importance-matrix (imatrix) GGUF quantizations of **Qwen/Qwen3.6-35B-A3B** — a 35B-parameter Mixture-of-Experts model (~3B active per token) — spanning the full ladder from `IQ1_*` up to `Q8_0`, plus the `BF16` baseline. This set exists as a **canonical reference** for cross-quant / cross-backend quality (perplexity) and throughput comparison. Every quant in this repository was produced from the same `BF16` source and the **same importance matrix** (`Qwen3.6-35B-A3B-imatrix.gguf`, included here for full reproducibility). > **Generated with the jimbothigpen/llama.cpp fork.** > These GGUFs and their imatrix are produced by that fork's quantization toolchain. It is a fork of > upstream ggml-org/llama.cpp that integrates features ported > from several llama.cpp forks — including the IQ-K / IQ-KS / IQ-KT (trellis) quant families from > ik_llama.cpp — plus ROCm and Vulkan backend support.
- Ornith-1.5-27B-A3B-Coder-MTP-GGUF↗
ManniX-ITA
GGUF quantizations of **`ManniX-ITA/Ornith-1.5-27B-A3B-Coder`** — the pure competence-map 184-expert cut of `Ornith-1.5-35B-A3B`. **→ Recipe, drop maps, the full 11-bench evaluation, the cross-family comparison and the length/deliberation analysis all live on the weights card.** This card covers the quantized files only. > Its sibling **CoderX** > uses the same map with a REAP stability floor and measures better on almost every code and > reasoning axis at the same expert budget and the same file size. Start there unless you > specifically want this arm.
- shap-e-img2img↗
OpenAI
Shap-E introduces a diffusion process that can generate a 3D image from a text prompt. It was introduced in Shap-E: Generating Conditional 3D Implicit Functions by Heewoo Jun and Alex Nichol from OpenAI. *We present Shap-E, a conditional generative model for 3D assets. Unlike recent work on 3D generative models which produce a single output representation, Shap-E directly generates the parameters of implicit functions that can be rendered as both textured meshes and neural radiance fields. We train Shap-E in two stages: first, we train an encoder that deterministically maps 3D assets into the parameters of an implicit function; second, we train a conditional diffusion model on outputs of the encoder. When trained on a large dataset of paired 3D and text data, our resulting models are capable of generating complex and diverse 3D assets in a matter of seconds. When compared to Point-E, an explicit generative model over point clouds, Shap-E converges faster and reaches comparable or better sample quality despite modeling a higher-dimensional, multi-representation output space. We release model weights, inference code, and samples at this https URL.*
- vjepa2-vitg-fpc64-256↗
facebook
A frontier video understanding model developed by FAIR, Meta, which extends the pretraining objectives of VJEPA, resulting in state-of-the-art video understanding capabilities, leveraging data and model sizes at scale. The code is released in this repository. V-JEPA 2 is intended to represent any video (and image) to perform video classification, retrieval, or as a video encoder for VLMs. To load a video, sample the number of frames according to the model. For this model, we use 64.
- TerraMind-1.0-tiny↗
ibm-esa-geospatial
TerraMind is the first multimodal any-to-any generative foundation model for Earth Observation jointly developed by IBM, ESA, and Forschungszentrum Jülich. TerraMind uses a dual-scale transformer-based encoder-decoder architecture, simultaneously processing pixel-level and token-level data. The model was pre-trained on 500B tokens from 9M spatiotemporally aligned multimodal samples from the TerraMesh dataset. Modality-specific patch embeddings allow direct processing of raw inputs, while modality-specific FSQ-VAEs are used for image tokenization. For sequence-like modalities such as coordinates, an adapted WordPiece tokenizer is employed. During pre-training, TerraMind leverages masked token reconstruction, learning complex cross-modal correlations to generate high-quality latent representations.
- roberta-base-go_emotions↗
SamLowe
A version of this model in ONNX format (including an INT8 quantized ONNX version) is now available at https://huggingface.co/SamLowe/roberta-base-go_emotions-onnx. These are faster for inference, esp for smaller batch sizes, massively reduce the size of the dependencies required for inference, make inference of the model more multi-platform, and in the case of the quantized version reduce the model file/download size by 75% whilst retaining almost all the accuracy if you only need inference. go_emotions is based on Reddit data and has 28 labels. It is a multi-label dataset where one or multiple labels may apply for any given input text, hence this model is a multi-label classification model with 28 'probability' float outputs for any given input text. Typically a threshold of 0.5 is applied to the probabilities for the prediction for each label. The model was trained using `AutoModelForSequenceClassification.from_pretrained` with `problem_type="multi_label_classification"` for 3 epochs with a learning rate of 2e-5 and weight decay of 0.01.
- rut5-base-summ↗
d0rj
Finetuned ai-forever/ruT5-base for text and dialogue summarization. - d0rj/samsum-ru - IlyaGusev/gazeta - zjkarina/matreshka - rcp-meetings/rudialogsum_v2 - GEM/wiki_lingua - mlsum > Scheduler, optimizer and trainer states are saved into this repo, so you can use that to continue finetune with your own data with existing gradients.
- hubert-large-ll60k↗
facebook
The large model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out this blog for more in-detail explanation of how to fine-tune the model. The model was pretrained on Libri-Light.
- whisper-large-v3-turbo↗
OpenAI
Whisper is a state-of-the-art model for automatic speech recognition (ASR) and speech translation, proposed in the paper Robust Speech Recognition via Large-Scale Weak Supervision by Alec Radford et al. from OpenAI. Trained on >5M hours of labeled data, Whisper demonstrates a strong ability to generalise to many datasets and domains in a zero-shot setting. Whisper large-v3-turbo is a finetuned version of a pruned Whisper large-v3. In other words, it's the exact same model, except that the number of decoding layers have reduced from 32 to 4. As a result, the model is way faster, at the expense of a minor quality degradation. You can find more details about it in this GitHub discussion. **Disclaimer**: Content for this model card has partly been written by the 🤗 Hugging Face team, and partly copied and pasted from the original model card.