# English-specific

Jedi-3B-1080p is a 3B-parameter model developed by xlangai, quantized using llama.cpp, suitable for image-text generation tasks.

SmolVLM2-2.2B-Instruct is a vision-language model with a parameter scale of 2.2B, focusing on video text-to-text tasks and supporting English.

This is the quantized version of the NVIDIA Llama-3_1-Nemotron-Ultra-253B-v1 model, quantized using llama.cpp, supporting multiple quantization types and suitable for various hardware environments.

This is the GGUF quantized version of the Menlo/Ichigo-llama3.1-s-instruct-v0.4 model, designed for speech and language processing tasks in resource-constrained environments.

Gemma is a lightweight open model series launched by Google, built on the technology used to create Gemini models, suitable for various text generation tasks.

Gemma is a lightweight open-source large language model series launched by Google, built on the same technology used to create Gemini models, suitable for various text generation tasks.

GLiNER is a bidirectional Transformer-based named entity recognition model capable of identifying any entity type, offering greater flexibility than traditional NER models and higher efficiency than large language models.

Gemma is a lightweight open model series launched by Google, built on the same technology as Gemini, suitable for text generation tasks.

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'

Gemma is a lightweight open-source large language model introduced by Google, built on the same technology as Gemini, suitable for various text generation tasks.

Velara 11B v2 is a large language model based on the Mistral architecture, focusing on text generation tasks and utilizing the Alpaca-InstructOnly2 prompt template.

TinyLLama-v0 is a lightweight language model provided in GGUF format, suitable for text generation tasks.

LaMini-Flan-T5-248M is a 248M parameter model fine-tuned on the LaMini-instruction dataset based on google/flan-t5-base, belonging to the LaMini-LM series, specifically designed for responding to natural language instructions.

A vision-language model trained on the English subset of LAION-2B using the OpenCLIP framework, supporting zero-shot image classification and image-text retrieval

This model is a fine-tuned speech recognition model based on facebook/wav2vec2-base on the Common Voice 3.0 English dataset, achieving a word error rate of 0.247 on the test set.