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AI 모델 2119

• Qwen2.5-VL-7B-Instruct-unsloth-bnb-4bit↗

  unsloth

  In the past five months since Qwen2-VL’s release, numerous developers have built new models on the Qwen2-VL vision-language models, providing us with valuable feedback. During this period, we focused on building more useful vision-language models. Today, we are excited to introduce the latest addition to the Qwen family: Qwen2.5-VL. We extend dynamic resolution to the temporal dimension by adopting dynamic FPS sampling, enabling the model to comprehend videos at various sampling rates. Accordingly, we update mRoPE in the time dimension with IDs and absolute time alignment, enabling the model to learn temporal sequence and speed, and ultimately acquire the ability to pinpoint specific moments. We enhance both training and inference speeds by strategically implementing window attention into the ViT. The ViT architecture is further optimized with SwiGLU and RMSNorm, aligning it with the structure of the Qwen2.5 LLM.

• gemma-3-1b-it-GGUF↗

  ggml-org

  * [Gemma 3 Technical Report][g3-tech-report] * [Responsible Generative AI Toolkit][rai-toolkit] * [Gemma on Kaggle][kaggle-gemma] * [Gemma on Vertex Model Garden][vertex-mg-gemma3] Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them 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. - **Input:** - Text string, such as a question, a prompt, or a document to be summarized - Images, normalized to 896 x 896 resolution and encoded to 256 tokens each - Total input context of 128K tokens for the 4B, 12B, and 27B sizes, and 32K tokens for the 1B size

• InternVL3-1B-Instruct↗

  OpenGVLab

  ***This is the SFT version of InternVL3-1B, which has undergone native multimodal pre-trainin and SFT but has not undergone MPO. If you're unsure which version to use, please use the InternVL3-1B version.*** We introduce InternVL3, an advanced multimodal large language model (MLLM) series that demonstrates superior overall performance. Compared to InternVL 2.5, InternVL3 exhibits superior multimodal perception and reasoning capabilities, while further extending its multimodal capabilities to encompass tool usage, GUI agents, industrial image analysis, 3D vision perception, and more. Additionally, we compare InternVL3 with Qwen2.5 Chat models, whose corresponding pre-trained base models are employed as the initialization of the langauge component in InternVL3. Benefitting from Native Multimodal Pre-Training, the InternVL3 series achieves even better overall text performance than the Qwen2.5 series. As shown in the following figure, InternVL3 retains the same model architecture as InternVL 2.5 and its predecessors, InternVL 1.5 and 2.0, following the "ViT-MLP-LLM" paradigm. In this new version, we integrate a newly incrementally pre-trained InternViT with various pre-trained LLMs, including InternLM 3 and Qwen 2.5, using a randomly initialized MLP projector.

• llama2_7b_chat_uncensored-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. GGUF 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 `other`, and this quantization has therefore used that same license. As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.

• flux-3-image-klein-pup↗

  Black Forest Labs

  FLUX 3 Image Klein Prompt Upsampler is a 9B vision-language model that expands a short image request into the detailed prompt FLUX 3 Image Klein works best with. It takes the request, up to 10 reference images and an optional list of bounding boxes, and returns one prompt. It is a fine-tune of Qwen3.5-9B. FLUX 3 Image Klein is not meant to run without a prompt upsampler in front of it, for text-to-image, editing and layout alike; a short prompt sent to the image model as typed gives generic output. This one is tuned for it, see Training. Any other LLM can do the job with the system prompts in `system_prompts.json`. The inference code in black-forest-labs/flux-3-image-klein loads it by default. With Diffusers, run the Transformers block below and pass the caption to the pipeline as `prompt`. In ComfyUI, the text-to-image templates on flux-3-image-klein-comfy have an `enable_prompt_upsampler` switch. > Full-color outdoor photograph, eye-level medium-wide shot in late afternoon sunlight, showing a man walking his dog along a neighborhood sidewalk. The low sun shines from the upper right, casting long shadows across the pavement and lighting the man's face and clothing with warm side light. Slightly left of center, a white man in his thirties with fair skin, short brown hair, and light stubble walks toward frame-left at an easy pace. He wears a faded olive overshirt over a plain grey T-shirt, dark blue jeans, and worn brown leather shoes. [...]

• PP-OCRv6_medium_rec↗

  PaddlePaddle

  PP-OCRv6: From 1.5M to 34.5M Parameters, Surpassing Billion-Scale VLMs on OCR Tasks **🔥 Official Website** **📝 Technical Report** PP-OCRv6 is a lightweight OCR system that combines architectural innovation with data-centric optimization. It redesigns the backbone, detection neck, and recognition neck around a unified MetaFormer-style building block with structural reparameterization. Three model tiers (medium, small, tiny) share the same block primitives, covering deployment scenarios from server to edge.

• Photon-v1↗

  sam749

• wav2vec2-large-xlsr-53-arabic↗

  elgeish

  Fine-tuned facebook/wav2vec2-large-xlsr-53 on Arabic using the `train` splits of Common Voice and Arabic Speech Corpus. When using this model, make sure that your speech input is sampled at 16kHz. For more details, see Fine-Tuning with Arabic Speech Corpus. This model represents Arabic in a format called Buckwalter transliteration. The Buckwalter format only includes ASCII characters, some of which are non-alpha (e.g., `">"` maps to `"أ"`). The lang-trans package is used to convert (transliterate) Arabic abjad.

• leosamsHelloworldXL_helloworldXL50GPT4V↗

  misri

• Qwen3.5-35B-A3B-Claude-Distilled-APEX-GGUF↗

  mudler

  ⚡ Each donation = another big MoE quantized I host 25+ free APEX MoE quantizations as independent research. My only local hardware is an NVIDIA DGX Spark (122 GB unified memory), enough for ~30-50B-class MoEs, but bigger ones (200B+) require rented compute on H100/H200/Blackwell, typically $20-100 per quant. If APEX quants are useful to you, your support directly funds those bigger runs. **APEX (Adaptive Precision for EXpert Models)** quantizations of Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled. Benchmarks coming soon. For reference APEX benchmarks on the same Qwen3.5-MoE architecture, see mudler/Qwen3.5-35B-A3B-APEX-GGUF.