This is a static quantized version of the yanolja/YanoljaNEXT-Rosetta-27B-2511 model, providing weight files of various quantization types and supporting multilingual translation tasks. The model is specifically optimized for 32 languages, including major languages such as Chinese, English, Japanese, and Korean.

Natural Language ProcessingTransformersMultiple Languages

This is a statically quantized version of the Qwen3-VL-8B-Abliterated-Caption-it model, supporting multilingual image captioning tasks and offering multiple quantization levels to balance performance and resource requirements.

MultimodalTransformersMultiple Languages

Lamapi/next-12b is a large language model based on 12B parameters. It is fine-tuned with a multilingual dataset, supports more than 50 languages, has efficient text generation capabilities, and is suitable for natural language processing tasks in multiple fields such as chemistry, code, biology, finance, and law.

Natural Language ProcessingTransformersMultiple Languages

Lamapi/next-12b is a multilingual large language model with 12 billion parameters, offering multiple quantized versions and supporting various natural language processing tasks such as text generation, question answering, and chatting. This model is trained on multiple domain datasets and features high efficiency and lightweight characteristics.

Natural Language ProcessingTransformersMultiple Languages

Next 12B is a multimodal vision-language model with 12 billion parameters based on Gemma 3. It is the most advanced open-source vision-language model in Turkey. This model performs excellently in text and image understanding, with advanced inference and context-aware multimodal output capabilities. It specifically provides professional-level Turkish language support and also has extensive multilingual capabilities.

MultimodalTransformersMultiple Languages

DeepSeek-OCR-8bit is an MLX format version converted from the DeepSeek-OCR model. It is a visual language model specifically optimized for Apple chips, supporting multilingual OCR recognition and image text understanding tasks.

MultimodalMlxOther

This model is a vision-language model in MLX format converted from DeepSeek-OCR, specifically designed for optical character recognition (OCR) tasks. It supports multilingual text recognition and image understanding.

MultimodalMlxOther

This is a multimodal model that supports multilingual processing, covering multiple fields such as natural language processing, code processing, and audio processing. It can perform various tasks such as automatic speech recognition, speech summarization, speech translation, and visual Q&A.

MultimodalSafetensorsMultiple Languages

Svara-TTS is an open-source multilingual text-to-speech model for Indian languages, supporting 19 languages (18 Indian languages + Indian English). This model is built based on the Orpheus-style discrete audio token method, aiming to achieve clear, expressive, and low-latency speech synthesis on ordinary GPUs/CPUs.

Audio ProcessingTransformersMultiple Languages

This is an MLX format model converted from the DeepSeek-OCR model, supporting multilingual image text recognition and text generation functions, specifically optimized for OCR tasks.

MultimodalMlxOther

A dedicated novel writing model fine-tuned based on the command-a-03-2025-uncut model, trained with a high-quality novel paragraph dataset, supporting multilingual novel writing tasks

Natural Language ProcessingTransformersMultiple Languages

NVIDIA-Nemotron-Nano-VL-12B-V2-FP8 is a quantized vision-language model launched by NVIDIA, which adopts an optimized Transformer architecture and has undergone three-stage training on commercial images. This model supports single-image inference, has multilingual and multimodal processing capabilities, and is suitable for various scenarios such as image summarization and text-image analysis.

MultimodalTransformers

This is a quantized version of the afkfatih/LFM2-8B-A1B-TR model, offering various GGUF quantization formats. The model is a multilingual large language model with 8 billion parameters, specifically optimized for the Turkish language and supporting 9 languages including English, Chinese, and Arabic.

Natural Language ProcessingTransformersMultiple Languages

Granite-4.0-H-Tiny AWQ - INT4 is a 4-bit version of the Granite-4.0-H-Tiny base model processed by AWQ quantization. It significantly reduces resource consumption while maintaining performance. This model has 7 billion parameters, supports multilingual processing, and has rich functions including text generation, code completion, and tool invocation.

Natural Language ProcessingTransformers

Qwen2.5-14B-Instruct is a multilingual large language model based on the Qwen2.5 architecture, with 14 billion parameters. It supports 13 languages and has excellent instruction-following and dialogue capabilities. This model performs well in various language tasks and is suitable for text generation and understanding in multilingual scenarios.

Natural Language ProcessingTransformersMultiple Languages

LFM2-8B-A1B-qx86-hi-mlx is an efficient inference model based on the MLX format, converted from LiquidAI/LFM2-8B-A1B. This model adopts a mixture-of-experts architecture and demonstrates卓越的 efficiency in inference tasks. It is particularly擅长 at complex logical reasoning tasks and supports multilingual processing.

Natural Language ProcessingMlxMultiple Languages

This is an 8-bit quantized version converted from the LiquidAI/LFM2-8B-A1B model, optimized for the Apple MLX framework. This model is a mixture-of-experts model with 8B parameters, supporting multilingual text generation tasks.

Natural Language ProcessingMlxMultiple Languages

Granite-4.0-350M-Base is a lightweight decoder-only language model developed by IBM, designed specifically for resource-constrained devices, supporting multilingual text generation and code completion functions.

Natural Language ProcessingTransformers

Granite-4.0-1B-Base is a lightweight decoder-only language model developed by IBM, designed specifically for resource-constrained devices, supporting multilingual text generation and code completion functions.

Natural Language ProcessingTransformers

Granite-4.0-H-350M is a lightweight instruction model developed by IBM, fine-tuned based on Granite-4.0-H-350M-Base. This model combines open-source instruction datasets and internal synthetic datasets, and is developed using various technologies such as supervised fine-tuning, reinforcement learning, and model merging. It has powerful instruction-following capabilities and multilingual support.

Natural Language ProcessingTransformers