This is a 4-bit W4A16 quantized version based on the MiniMax M2 base model, quantized by @Qubitum of ModelCloud using the GPT-QModel tool. This model is specifically optimized for text generation tasks, significantly reducing the model size and inference resource requirements while maintaining good performance.

Natural Language ProcessingSafetensorsEnglish

This is a high-performance 4-bit quantized version of the Kimi K2 Instruct model, optimized for running on Apple Silicon (M1/M2/M3/M4) Macs using the MLX framework. The model has 671 billion parameters, supports a 128K context window, and achieves an excellent balance between quality and efficiency, making it an ideal choice for most practical deployments.

Natural Language ProcessingMlxMultiple Languages

This model is based on the Transformers library, and its specific purpose and functionality require further information to confirm.

Natural Language ProcessingTransformers

A multilingual translation model based on the m2m transformer architecture, supporting bidirectional translation between Russian, Chinese, and English

Natural Language ProcessingSafetensorsMultiple Languages

M2M100 is a multilingual encoder-decoder model supporting direct translation between 100 languages with 1.2 billion parameters.

A Sanskrit-to-English translation model fine-tuned from M2M100, trained on the Itihasa dataset containing translations of Sanskrit epics.

Natural Language ProcessingTransformersMultiple Languages

M2-BERT-8K is an 80-million-parameter long-context retrieval model based on the architecture proposed in the paper 'Benchmarking and Building Long-Context Retrieval Models with LoCo and M2-BERT'.

Natural Language ProcessingTransformersEnglish

80M-parameter M2-BERT-2k model checkpoint, specifically designed for long-context retrieval tasks, supporting a context length of 2048 tokens.

Natural Language ProcessingTransformersEnglish

A Russian spell checker trained on the M2M100-1.2B model for correcting spelling and typing errors

Natural Language ProcessingTransformersOther

M2-BERT-128 is an 80-million-parameter retrieval model checkpoint proposed in the paper 'Benchmarking and Building Long-Context Retrieval Models with LoCo and M2-BERT'

Natural Language ProcessingTransformersEnglish

This is an 80M parameter M2-BERT pre-trained checkpoint with a sequence length of 2048, fine-tuned for long-context retrieval tasks.

Natural Language ProcessingTransformersEnglish

This is an 80M parameter M2-BERT pre-trained model, supporting sequences up to 32,768 tokens in length, specifically optimized for long-context retrieval tasks.

Natural Language ProcessingTransformersEnglish

This is an 80-million-parameter M2-BERT pre-trained checkpoint with a sequence length of 8192, fine-tuned for long-context retrieval tasks.

Natural Language ProcessingTransformersEnglish

Multilingual translation model fine-tuned on the WMT16 dataset, supporting Russian to English translation tasks

Natural Language ProcessingTransformers

A translation model fine-tuned on Russian-Kabardian datasets based on facebook/m2m100_1.2B

Natural Language ProcessingTransformersOther

A bidirectional translation model for English and Tamil based on the M2M100 architecture, released under the OpenRAIL license

Natural Language ProcessingTransformersMultiple Languages

A Chinese-English game translation model fine-tuned based on Facebook/M2M100, specifically optimized for cultivation/martial arts game content

Natural Language ProcessingTransformersMultiple Languages

A Russian spelling check model trained based on M2M100-1.2B, capable of correcting spelling and typing errors

Natural Language ProcessingTransformersOther

SMaLL-100 is a compact and fast large-scale multilingual machine translation model, covering over 10,000 language pairs, with performance comparable to M2M-100 but smaller in size and faster.

Natural Language ProcessingTransformersMultiple Languages

This model is a fine-tuned English-to-Brazilian Portuguese translation model based on facebook/m2m100_418M on the kde4 dataset

Natural Language ProcessingTransformers