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AI モデル 2119
- DeepSeek-V4.1-Flash-ROCMFP23-GGUF↗
Lucebox
Lucebox builds of DeepSeek V4.1 Flash for the Lucebox DS4.1 engine (AMD R9700 + Strix Halo). Routed experts in the engine's ROCMFP block formats; attention, shared experts and head in Q8_0; both Engram tables embedded. Calibrated with the published 262,144-token imatrix (`smalinin/DeepSeek-V4.1-Flash-GGUF`, converted in `imatrix/`). Formats: `Q2_0_ROCMFP2` (ggml type 107, 10 bytes per 32 weights, 2.5 bits, codebook {-1, 0, 1, 2} x a ue4m3 scale per 16 weights), `Q3_0_ROCMFPX` (type 104, 14 bytes per 32, 3.5 bits). **fp2s** files use type 107 with bit 7 of a half-block's scale byte meaning "mirror the codebook"; a reader that ignores that bit produces garbage, so use an engine build that honors it. KL is measured on 8,184 held-out fineweb-edu tokens against the unquantized model through the same reference forward pass (`eval/` holds the teacher log-probs and the tokens). Recipe, converter patches and harness: Lucebox repo, `lucebox_training/quantization`. `DeepSeek-V4.1-Flash-mmproj-BF16.gguf` (0.90 GiB): this checkpoint's vision encoder (32-layer ViT and the aligner into the 5120-wide model), its 266 tensors copied byte for byte in BF16 by the engine's `server/tools/export_ds4v_mmproj.py`, with the checkpoint revision recorded (sha256 in `vision/`). Pass it with `--mmproj` to an engine build with V4.1 image input, next to these September files only: the October 17 checkpoint retrained its encoder and ships its own in `Lucebox/DeepSeek-V4.1-Flash-1017-ROCMFP-GGUF`. The engine refuses an encoder from another checkpoint than the target's.
- PP-OCRv5_server_det↗
PaddlePaddle
PP-OCRv5_server_det is one of the PP-OCRv5_det series, the latest generation of text detection models developed by the PaddleOCR team. Designed for high-performance applications, it supports the detection of text in diverse scenarios—including handwriting, vertical, rotated, and curved text—across multiple languages such as Simplified Chinese, Traditional Chinese, English, and Japanese. Key features include robust handling of complex layouts, varying text sizes, and challenging backgrounds, making it suitable for practical applications like document analysis, license plate recognition, and scene text detection. The key accuracy metrics are as follow: For details about PaddlePaddle installation, please refer to the PaddlePaddle official website. You can also integrate the model inference of the text detection module into your project. Before running the following code, please download the sample image to your local machine.
- Ornith-1.5-35B-A3B-APEX-I-NanoPlus-GGUF↗
IsValorum
If you consider these releases to be of high quality and they help you in your day-to-day workflow, consider supporting on Ko-fi. Every contribution directly sustains future cloud compute hours and helps support my university studies. 1. Optimization History & Transparency Notice 2. Quality Spectrum: APEX-I-NanoPlus vs. Standard Flat Quantizations 3. Model Files & Technical Specifications 4. Surgical Tensor Quantization Map (Audited from GGUF) 5. Inference Quickstart 6. 1. llama-cli (Console Generation) 7. 2. llama-server (OpenAI-Compatible API) 8. CRITICAL: Coding Syntax & Repeat Penalty Advisory (Preventing Character Swapping) 9. Hardened Agentic Chat Template 10. Optional Support > [!NOTE] > ### 🚀 EXPLORE THE ESTABLISHED 35B MoE MINIPLUS & NANOPLUS LINEUP > These are complementary APEX-I releases, not alternate downloads of the same model. Each receives the same surgical tensor-by-tensor approach and a design suitable for full or partial system-RAM inference: > > - **Ornith-1.5-35B-A3B APEX-I-MiniPlus-V2.1** — software-engineering-focused MoE designed for repository-scale coding and autonomous engineering agents (15.23 GB / Q5_K_L tier, bordering Q6_K). > - **Qwen3.6-35B-A3B-MTP APEX-I-NanoPlus** — the approx. 13.0 GB NanoPlus pioneer achieving Q4 quality at 2.93 BPW. > - **Occamy-1.0 APEX-I-NanoPlus** — versatile frontier MoE for deep research and multimodal tasks.
- Cosmos-UpsamplePrompt1-12B-Text2World↗
NVIDIA
- gemma-3-27b-it-bnb-4bit↗
unsloth
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. These models were trained on a dataset of text data that includes a wide variety of sources. The 27B model was trained with 14 trillion tokens, the 12B model was trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens and 1B with 2 trillion tokens. Here are the key components: The combination of these diverse data sources is crucial for training a powerful multimodal model that can handle a wide variety of different tasks and data formats.
- ChatAYT-Lora-Assamble-Marcoroni-GGUF↗
TheBloke
- gemma-4-12B-it↗
Google
Hugging Face | GitHub | Launch Blog | Documentation | Technical Report > [!Note] > This model card is for the Gemma 4 12B Unified model, which is part of the Gemma 4 family of open models. Built with the same multimodal functionality as Gemma 4 E2B and E4B (text, audio, image, and video inputs), it brings native audio and vision understanding directly to local environments without the need for separate encoders. This unified approach to multimodality makes the model encoder-free, offering a deployment size that is perfect for consumer devices and streamlined local execution. Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on E2B, E4B, and 12B) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages.
- roboengine-sam↗
michaelyuanqwq
- stable-audio-3-medium-GGUF↗
thepatch
GGUF conversions of stabilityai/stable-audio-3-medium for **sa3.cpp** — a portable C++/GGML port of Stable Audio 3, no PyTorch in the loop. Runs on CPU, CUDA, Vulkan, or Metal (Apple Silicon). Every component is validated against the PyTorch reference at cosine similarity ~1.0. This is a multi-file model. Grab the **DiT** + **SAME** at your chosen precision and the **conditioner**, plus the shared **encoder + tokenizer** from the t5gemma-b-b-ul2-GGUF repo. **F16** is the production path (~3.5s for 12s of audio on an 8GB laptop GPU); **F32** is for CPU validation. The conditioner + encoder + tokenizer stay F32 (small / quality-critical).
- Yi-1.5-34B-Chat↗
01-ai
Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples. Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension. Model | Context Length | Pre-trained Tokens | :------------: | :------------: | :------------: | | Yi-1.5 | 4K, 16K, 32K | 3.6T