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AI 모델 84759

• colpali-v1.2↗

  vidore

  ColPali is a model based on a novel model architecture and training strategy based on Vision Language Models (VLMs) to efficiently index documents from their visual features. It is a PaliGemma-3B extension that generates ColBERT- style multi-vector representations of text and images. It was introduced in the paper ColPali: Efficient Document Retrieval with Vision Language Models and first released in this repository This version is trained with `colpali-engine==0.2.0` but can be loaded for any version `>=0.2.0`. Compared to `vidore/colpali`, this version is trained with right padding for queries to fix unwanted tokens in the query encoding. It also stems from the fixed `vidore/colpaligemma-3b-pt-448-base` to guarantee deterministic projection layer initialization. It was trained for 5 epochs, with in-batch negatives and hard mined negatives and a warmup of 1000 steps (10x longer) to help reduce non-english language collapse.

• Decision-2.0-Nox-4B-GGUF↗

  vllm-sr

• Qwen3-4B-Think-GRPO-MLX↗

  12384-0ashntoi

• 1dvit_base_patch50_5000.ptbxl-form↗

  4dm-labs

• Spark-X2.5-4B-GGUF↗

  abenzerps

  > [!IMPORTANT] > **Compatibility:** These GGUF files require `llama.cpp` **b10828** or later, which includes official support for the Spark-X2.5 (`spark2_5`) architecture. Applications with a bundled runtime must use an equivalent or newer build. llama.cpp support GGUF quantizations of XHToken/Spark-X2.5-4B, a 4B general-purpose language model for reasoning, coding, tool use, and agentic workflows. Native context: **1,048,576 tokens (1M)**. Includes the upstream chat_template.jinja. Checksums: SHA256SUMS.txt.

• GigaChat3.1-10B-A1.8B-GGUF↗

  ai-sage

  `GigaChat 3.1 Lightning` is the compact instruct model of the GigaChat 3.1 family. It is a Mixture-of-Experts (MoE) model with 10B total parameters and 1.8B active parameters, designed for fast multilingual assistant workloads, reasoning, code, function calling, and product-style deployment. For high-performance inference, an fp8 version of the model is available - GigaChat3.1-10B-A1.8B. bf16 version is also avaliable - GigaChat3.1-10B-A1.8B-bf16.

• Wan2.2-Fun-A14B-Control-Camera↗

  alibaba-pai

  Windows 的详细信息： - 操作系统 Windows 10 - python: python3.10 & python3.11 - pytorch: torch2.2.0 - CUDA: 11.8 & 12.1 - CUDNN: 8+ - GPU： Nvidia-3060 12G & Nvidia-3090 24G Linux 的详细信息： - 操作系统 Ubuntu 20.04, CentOS - python: python3.10 & python3.11 - pytorch: torch2.2.0 - CUDA: 11.8 & 12.1 - CUDNN: 8+ - GPU：Nvidia-V100 16G & Nvidia-A10 24G & Nvidia-A100 40G & Nvidia-A100 80G 由于Wan2.2的参数非常大，我们需要考虑显存节省方案，以节省显存适应消费级显卡。我们给每个预测文件都提供了GPU_memory_mode，可以在model_cpu_offload，model_cpu_offload_and_qfloat8，sequential_cpu_offload中进行选择。该方案同样适用于CogVideoX-Fun的生成。

• Qwen3.8-27B-abliterated-ROCmFPX↗

  ArtomYuan

  **ROCmFPX quantized GGUF (7 tiers + vision mmproj)** of huihui-ai/Huihui-Qwen3.8-27B-abliterated — abliterated BF16 weights, locally requantized. This repository uses strix.cpp, a llama.cpp fork **specifically tuned for the AMD Strix Halo `gfx1151` (Ryzen AI MAX+ 395) UMA architecture and ROCm/HIP backend**, with native support for the ROCmFPX quantization format. Requires strix.cpp (this fork supports ROCmFPX; **upstream llama.cpp cannot load these files** — format note at the end).

• smolm2-1.7b-uncensored↗

  arzaan789

• aragpt2-medium↗

  aubmindlab