This is a text generation model optimized using PEFT (Parameter-Efficient Fine-Tuning) and LoRA techniques based on the Google Gemma-2B-IT base model. The model is optimized for specific tasks while maintaining the capabilities of the base model through parameter-efficient methods.

Natural Language ProcessingPeft

Scout is a professional language model fine-tuned by VANTA Research based on the Google Gemma 3 4B Instruct architecture. It excels in constraint-aware reasoning and adaptive problem solving, and is擅长侦察式情报收集 should be translated as is good at scouting-style intelligence gathering。擅长侦察式情报收集、系统问题分解和自适应解决方案生成。It is good at scouting-style intelligence gathering, system problem decomposition, and adaptive solution generation.

Natural Language ProcessingTransformersEnglish

This is a text generation model that is specifically fine-tuned based on the Google Gemma-3 model. After training, it can use external tools to generate step-by-step plans. This model is trained on the rewoo/planner_instruction_tuning_2k dataset and uses LoRA technology for efficient fine-tuning.

Natural Language ProcessingTransformers

This is a lightweight text summary generation model based on the Google T5 Efficient Tiny architecture. It is trained using the shorecode/summary-collection-200k-rows dataset and is specifically designed for automatic text summarization tasks. It features efficient inference and low resource consumption.

Natural Language ProcessingTransformersEnglish

A version fine-tuned based on google/medgemma-4b-it, specifically designed for binary breast X-ray image classification on the OMAMA 256×256 dataset. This model uses LoRA (Low-Rank Adaptation) for parameter-efficient adjustment and performs excellently in the breast cancer classification task, with the accuracy increasing from 12% of the base model to 95%.

MultimodalTransformersEnglish

Lapa LLM 12B PT is an open-source large language model developed based on Google Gemma 3-12B, specifically optimized for Ukrainian language processing. It is developed by research teams from multiple Ukrainian universities and performs excellently in Ukrainian language processing, with efficient instruction fine-tuning and multimodal capabilities.

MultimodalTransformersOther

VaultGemma is a lightweight language model pre-trained by Google based on differential privacy technology, with powerful privacy protection capabilities. It uses differential privacy stochastic gradient descent (DP-SGD) for pre-training, providing mathematical privacy guarantees for the training data. It has less than 1 billion parameters and is suitable for a variety of natural language processing tasks.

Natural Language ProcessingTransformers

This is a text embedding model converted to the MLX format, derived from Google's EmbeddingGemma-300m model. It is specifically designed for sentence similarity calculation and text embedding tasks. The model uses BF16 precision and is suitable for efficient inference on Apple chip devices.

Natural Language ProcessingSentence-transformers

EmbeddingGemma 300M 4bit is a lightweight text embedding model developed by Google, specifically optimized for the MLX framework. This model can convert text into high-quality vector representations and is suitable for various natural language processing tasks, especially sentence similarity calculation and feature extraction.

Natural Language ProcessingSentence-transformers

embeddinggemma-300m-qat-q8_0 GGUF is a quantized version based on the Google embeddinggemma-300m model, specifically designed for feature extraction and sentence similarity calculation. This model has been quantized and optimized to significantly reduce the model size and inference overhead while maintaining high accuracy.

Natural Language ProcessingSentence-transformers

embeddinggemma-300M is a lightweight text embedding model optimized through quantization. Based on Google's embeddinggemma architecture, it uses QAT (Quantization-Aware Training) and Q4_0 quantization techniques, with 300M parameters. This model is specifically designed to generate high-quality text embedding vectors and supports tasks such as sentence similarity calculation and feature extraction.

Natural Language ProcessingSentence-transformers

This is the FP8 quantized version of the Gemma-3-27B model developed by the PyTorch team. It is based on the google/gemma-3-27b-it model with FP8 quantization processing. This model supports efficient inference through both vLLM and Transformers, significantly reducing memory usage and improving inference speed while maintaining model quality.

Natural Language ProcessingTransformersEnglish

A lightweight Dhivehi pre - trained model built on google/gemma-3-270m, trained on large - scale Dhivehi text data including news articles, Wikipedia content, and general web page texts, providing strong support for Dhivehi - related tasks.

Natural Language ProcessingTransformersOther

Gemma 3 270M is a lightweight multimodal model launched by Google. Based on the same research technology as the Gemini series, it supports text and image input, has a 32K context window, and provides high-quality output in tasks such as question answering, summarization, image understanding, and code generation.

MultimodalTransformersMultiple Languages

gemma3-konkani is a fine-tuned version based on the Google Gemma-3-4B-IT model, specifically optimized for the Konkani language. It is trained using supervised fine-tuning with the TRL framework and is suitable for text generation tasks.

Natural Language ProcessingPeft

This is the GGUF quantized version of the TiTan-Gemma3-0.27B model fine-tuned based on Google Gemma-3-270m-it. It offers multiple precision options, is suitable for inference engines such as llama.cpp, and significantly reduces the file size while maintaining the model's performance.

Natural Language ProcessingGgufEnglish

Gemma 3 270M Instruct is a lightweight instruction-tuned language model developed by Google. Based on a parameter scale of 270M, it is optimized for text generation tasks and supports multiple languages such as Chinese and English.

Natural Language ProcessingTransformers

This is a model specifically fine-tuned for Lao text summarization tasks. It is based on the Google Gemma 3-12B Instruct model and trained with over 10,000 supervised Lao language samples, capable of generating high-quality Lao text summaries.

Natural Language ProcessingFastaiOther

This is the 4-bit DWQ quantized version of the Google Gemma 3 270M Instruct model, optimized for the MLX framework. The model has been fine-tuned with instructions, supports dialogue interaction, and uses 4-bit DWQ quantization technology to reduce memory usage, making it suitable for efficient operation on Apple Silicon devices.

Natural Language ProcessingMlx

This is the GGUF format conversion version of the Google Gemma 3 270M model, converted by NikolayKozloff. This model is a text generation model with 2.7 billion parameters, supporting efficient inference deployment.

Natural Language ProcessingTransformers