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AI モデル 85003
- Mistral-7B-Instruct-v0.2-code-ft-GGUF↗
TheBloke
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit d0cee0d They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
- Falcon3-7B-Instruct-1.58bit↗
tiiuae
- inception_v4.tf_in1k↗
timm
A Inception-v4 image classification model. Trained on ImageNet-1k paper authors. Ported from Tensorflow via Cadene's pretrained-models.pytorch. - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 42.7 - GMACs: 12.3 - Activations (M): 15.1 - Image size: 299 x 299 - **Papers:** - https://arxiv.org/abs/1602.07261: https://arxiv.org/abs/1602.07261 - **Original:** - https://github.com/tensorflow/models - https://github.com/Cadene/pretrained-models.pytorch - **Dataset:** ImageNet-1k Explore the dataset and runtime metrics of this model in timm model results.
- GPT-OSS-Swallow-20B-RL-v0.1-MXFP4↗
tokyotech-llm
- dofa↗
torchgeo
- functiongemma-270m-it↗
unsloth
> [!Note] > FunctionGemma is intended to be fine-tuned for your specific function-calling task, including multi-turn use cases. FunctionGemma is a lightweight, open model from Google, built as a foundation for creating your own specialized function calling models. FunctionGemma is not intended for use as a direct dialogue model, and is designed to be highly performant after further fine-tuning, as is typical of models this size. Built on the Gemma 3 270M model and with the same research and technology used to create the Gemini models, FunctionGemma has been trained specifically for function calling. The model has the same architecture as Gemma 3, but uses a different chat format. The model is well suited for text-only function calling. The uniquely small size makes it possible to deploy in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. Furthermore, akin to the base Gemma 270M, the model has been optimized to be extremely versatile, performant on a variety of hardware in single turn scenarios, but should be finetuned on single turn or multiturn task specific data to achieve best accuracy in specific domains. To demonstrate how specializing the 270M parameter model can achieve high performance on specific agentic workflows, we have highlighted two use cases in the Google AI Edge Gallery app. The following is a code example of how to use FunctionGemma to generate a function call from a JSON definition using the Hugging Face Transformers library.
- InternVL3-78B-Instruct-GGUF↗
unsloth
- LTX-2.5-FP8↗
unsloth
This is an FP8 / INT8 quantized version of the LTX-2.5 distilled video DiT. - Each file replaces the 38.0 GB bf16 `transformer` of Lightricks/LTX-2.5-Diffusers with a 19.1 to 25.1 GB one. The text encoder, connectors, VAEs, vocoder and latent upsampler are unchanged and come from the Diffusers repo. - Same-seed LPIPS vs the bf16 DiT, averaged over all 121 frames of 4 clips (lower is better): 0.265 INT8-ConvRot, 0.366 FP8, 0.385 INT8. INT8-ConvRot is the recommended file. - FP8 and INT8 are bit-identical to quantizing the bf16 DiT at load with the same recipe, so they add no error over on-the-fly quantization; they save the download and the bf16 staging. Built from the `transformer/` of `Lightricks/LTX-2.5-Diffusers` at revision `da0b690960026eab794b8ceaf841233cf7064478` (tensor-identical to `ltx-2.5-22b-distilled-transformer-bf16.safetensors` in Lightricks/LTX-2.5). FP8 quantizes every linear with at least 512 features. INT8 keeps the audio stream, the audio-video cross attention, the adaLN and timestep projections and the norms in bf16. INT8-ConvRot rotates the inputs of all 480 quantized video linears with a block Hadamard (group 256) before quantizing; the rotation is listed in the file's metadata and applied to the activations at run time. Scores are against the bf16 DiT at the reference distilled settings (1280x704, 121 frames, 24 fps, 8 + 3 two-stage steps, CFG 1), text-to-video and image-to-video, seeds 42 and 7, with the same bf16 text encoder in every run; a repeated bf16 render is bit-identical (LPIPS 0.000). The distilled sampler injects noise every step, so small weight errors move the motion more than the look of a frame: every file keeps the subject, scene and prompt, as the samples show.
- Lens-GGUF-ComfyUI↗
vantagewithai
- Qwen3.8-Flash-Next-GSQ-RCO-IQ3_S-Infernix↗
wallawalla47