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AI 모델 84759
- gmixer_24_224.ra3_in1k↗
timm
- Z-Image-Turbo_fp32-fp16-bf16_full_and_ema-only↗
tsqn
- videomt-dinov2-small-ytvis2019↗
tue-mps
This repository contains the Hugging Face Transformers conversion of the official VidEoMT checkpoint `yt_2019_vit_small_52.8.pth` from tue-mps/VidEoMT. Use `processor.post_process_instance_segmentation`, `processor.post_process_panoptic_segmentation`, or `processor.post_process_semantic_segmentation` depending on the target task.
- llama-3-8b↗
unsloth
We have a Google Colab Tesla T4 notebook for Llama-3 8b here: https://colab.research.google.com/drive/135ced7oHytdxu3N2DNe1Z0kqjyYIkDXp?usp=sharing Built with Meta Llama 3 All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.
- falcon-7b-instruct-sharded↗
vilsonrodrigues
Resharded version of https://huggingface.co/tiiuae/falcon-7b-instruct for low RAM enviroments (e.g. Colab, Kaggle) in safetensors Tutorial: https://medium.com/@vilsonrodrigues/run-your-private-llm-falcon-7b-instruct-with-less-than-6gb-of-gpu-using-4-bit-quantization-ff1d4ffbabcc **Falcon-7B-Instruct is a 7B parameters causal decoder-only model built by TII based on Falcon-7B and finetuned on a mixture of chat/instruct datasets. It is made available under the Apache 2.0 license.**
- Qwen3.8-Flash-Next-GSQ-RCO-IQ3_S-NInfer-v3↗
WaveCut
A NInfer **v3** artifact of ISTA-DASLab's Qwen3.8-Flash-Next GSQ-RCO IQ3_S (3.50 bits per weight over the transformer weights) for **NInfer-all**, the `master` branch of iamwavecut/ninfer-all. Qwen3.8-Flash-Next is a 125B-parameter mixture of experts: 48 layers of 512 routed experts, 10 of them active per token, about 6B active parameters. The GGUF assigns a ggml quantization type to every tensor; this artifact keeps each tensor's ggml blocks byte for byte (the hyper-connection projections excepted: they are re-quantized from BF16 to Q8_0), and the engine multiplies the blocks in place. IQ3_S is the release the GSQ-RCO card recommends: the strongest of the four. Stock NInfer builds refuse these files: the model family and the `gguf_*` formats exist only in that line; build `master` from commit `5395ec30b` on. NInfer serves it with up to eight concurrent requests, prompt-prefix reuse, structured output and its Vision tower; This file includes the MTP block. Enable drafting with `--spec mtp --draft-tokens 4`. One file, `Qwen3.8-Flash-Next-GSQ-RCO-IQ3_S-ninfer-v3.ninfer`, of 57,902,944,256 bytes (53.93 GiB), next to its conversion report, `SHA256SUMS`, `NOTICE` and `LICENSE`. Original text/Vision conversion command, before the MTP attachment:
- R-4B↗
YannQi
In this repo, we present **R-4B**, a multimodal large language model designed for general-purpose auto-thinking, autonomously switching between step-by-step thinking and direct response generation based on task complexity. This capability enables R-4B to deliver high-quality responses while significantly improving inference efficiency and reducing computational costs. The development of R-4B follows a two-stage training paradigm: (1) Bi-mode Annealing, which establishes both thinking and non-thinking capabilities for VQA; and (2) Bi-mode Policy Optimization (BPO), which enables the model to adaptively switch between thinking and non-thinking modes based on input demands. > [!NOTE] > Users can dynamically control the model's response by selecting one of three modes (`auto-thinking`, `thinking`, or `non-thinking`) with `thinking_mode`. `thinking_mode=auto` for `auto-thinking` mode; `thinking_mode=long` for `thinking` mode; `thinking_mode=short` for `non-thinking` mode. > Default is `auto-thinking`.
- Huihui-GLM-5.3-Flash-abliterated-GGUF↗
Yyyssd
- Qwen3.8-27B-Uncensored-YMQ-MTP-GGUF-12GB↗
0xSojalSec
- ruGPT-3.5-13B↗
ai-forever