# Multilingual Text Generation

This is a quantized version of TheDrummer's Agatha-111B-v1 model, offering multiple quantization type options suitable for different environments and requirements.

GPTQ quantized version based on the Qwen2.5-14B large language model, supporting English and Spanish text generation tasks

Qwen3 is the latest generation of large language models in the Tongyi Qianwen series, offering a complete suite of dense models and Mixture of Experts (MoE) models. Based on large-scale training, Qwen3 has achieved breakthrough progress in reasoning capabilities, instruction following, agent functionalities, and multilingual support.

Qwen3 is the latest generation of large language models in the Tongyi Qianwen series, offering a complete combination of dense models and Mixture of Experts (MoE) models. Based on large-scale training, Qwen3 achieves breakthrough progress in reasoning capabilities, instruction following, agent functions, and multilingual support.

Qwen3 is the latest version of the Tongyi Qianwen series of large language models, offering a range of dense and mixture of experts (MoE) models. Based on large-scale training, Qwen3 has achieved breakthrough progress in reasoning, instruction following, agent capabilities, and multilingual support.

Transformers is an open-source library developed by Hugging Face, providing various pretrained models for natural language processing tasks.

The GGUF quantized version of Meta-Llama-3.1-8B, generated using the llama.cpp tool, supports multilingual text generation tasks.

Compressible version of Qwen2.5-3B provided by the ACIP project, supporting dynamic model size adjustment while maintaining performance

An 8-bit quantized version converted from the Google Gemma 3 12B model, suitable for image-text to text tasks.

Salesforce's 800M parameter Llama-xLAM-2 model quantized version, specialized in text generation tasks

This is a GGUF format model converted from canopylabs/3b-es_it-ft-research_release, supporting Spanish and Italian.

Nemotron-H-56B-Base-8K is a large language model developed by NVIDIA, featuring a hybrid Mamba-Transformer architecture, supporting 8K context length and multilingual text generation.

The NVIDIA Nemotron-H-47B-Base-8K is a large language model (LLM) developed by NVIDIA, designed for text completion tasks. It features a hybrid architecture primarily composed of Mamba-2 and MLP layers, with only five attention layers.

RWKV-7 architecture with 2.9 billion parameters, supporting multilingual text generation tasks

The RWKV-7 model adopts the flash linear attention format, supports multilingual text generation tasks, and has a parameter count of 2.9 billion.

Qwen2.5-14B-it-restore is a model generated by combining the della, ties, and model stock fusion methods based on the Qwen2.5-14B and Qwen2.5-14B-Instruct models, aiming to address the decline in instruction-following capability and weakened mathematical ability.

The RWKV-7 model adopts a flash linear attention architecture and supports multilingual text generation tasks.

Quantized version of teapotllm, supporting multilingual text generation tasks

Llama-3.2-Taiwan-1B is a multilingual text generation model based on Meta's Llama-3.2-1B model, with special support for Chinese (Taiwan region) and multiple other languages.

PaliGemma 2 is a Vision-Language Model (VLM) launched by Google, combining the capabilities of the Gemma 2 language model and SigLIP vision model, supporting image and text inputs to generate text outputs.

PaliGemma 2 is a Vision-Language Model (VLM) launched by Google, integrating Gemma 2 capabilities, supporting image and text input to generate text output

PaliGemma 2 is Google's upgraded vision-language model (VLM) that combines Gemma 2 capabilities, supporting image and text input to generate text output.

PaliGemma 2 is a vision-language model based on Gemma 2, supporting image and text input to generate text output, suitable for various vision-language tasks.

Llama 3.2-Vision is a series of multimodal large language models developed by Meta, available in 11B and 90B scales, supporting image + text input and text output, optimized for visual recognition, image reasoning, image captioning, and visual question answering tasks.

magnum-v2-12b is the fourth model in the series, aiming to replicate the text quality of the Claude 3 series models (especially Sonnet and Opus). It is fine-tuned based on Mistral-Nemo-Base-2407 and has powerful text generation capabilities.

A text generation model fine-tuned based on Qwen1.5 32B, aiming to reproduce the text quality of Claude 3 series models

PaliGemma is a versatile lightweight vision-language model (VLM) developed by Google, built on the SigLIP vision model and Gemma language model, supporting both image and text inputs with text outputs.

A large-scale language model developed by the Japanese collaborative project LLM-jp, supporting Japanese and English, primarily used for text generation tasks.

Mixtral 8x22B is a 176-billion-parameter mixture of experts model released by MistralAI, supporting multilingual text generation tasks.

This model is fine-tuned for Tamil summarization and English-Tamil translation tasks, built on the Hugging Face Transformers library.

GGUF quantized version of Mixtral-8x7B-v0.1, supporting multiple bit quantization, suitable for text generation tasks.

Swallow-MS-7b-v0.1 is a Japanese-enhanced model based on Mistral-7B-v0.1 with continued pretraining, developed by TokyoTech-LLM, demonstrating excellent performance on Japanese tasks.

Mixtral is a multilingual text generation model based on a Mixture of Experts (MoE) architecture, supporting English, French, Italian, German, and Spanish.

CausalLM/14B-DPO-α is a large-scale causal language model supporting Chinese and English text generation tasks, with outstanding performance in MT-Bench evaluations.

The first publicly available commercial German foundation language model based on Mistral 7b, extending Llama-2's German capabilities through large-scale German corpus continual pre-training.

A BLOOM language model with Traditional Chinese enhancement, jointly developed by MediaTek Research, Academia Sinica, and the National Academy for Educational Research