Gemma 4 12B It Qat W4a16 Ct Alternatives

google/gemma-4-31B-it-qat-w4a16-ct is a quantized, instruction-tuned large language model released by Google for research and development purposes. It supports text generation, multi-turn chat, and can be deployed locally or via API. The model is open source and suitable for AI researchers and developers seeking a high-performance LLM for experimentation or integration.

This is a quantized variant of Google's Gemma 4 12B instruction-tuned model. It is designed for efficient inference while maintaining strong performance on reasoning and tool-use tasks.

This is a quantized (W4A16) version of Google's Gemma 4 model with instruction tuning and custom chat template. It supports efficient text generation and tool use while maintaining high performance. Intended for developers integrating advanced LLMs into applications with constrained hardware.

This is a quantized version of Google's Gemma 4 26B instruction-tuned (it) large language model. Hosted on Hugging Face, it provides open weights for developers to run locally or deploy on their own infrastructure. The model supports advanced features such as tool calling and follows a specific chat template, making it suitable for building AI applications, coding assistants, and conversational agents.

This repository hosts a QAT (Quantization-Aware Trained) 4-bit weights, 16-bit activations version of the Gemma-4 31B instruction-tuned model. It allows users to run this large model more efficiently on available hardware. The model is compatible with the Hugging Face ecosystem and Unsloth tools.

This is a post-training quantized (QAT) version of Google's Gemma 4 model with 4-bit weights and 16-bit activations (w4a16). It is designed for efficient inference while preserving model quality. The model is published on Hugging Face and intended for developers seeking to deploy Gemma 4 with reduced memory footprint.

This is a 4-bit quantized GGUF variant of Google's Gemma-4 12B instruction-tuned (it) model. It enables efficient local inference using tools like llama.cpp or Ollama. The model supports chat and instruction following while fitting on more modest GPUs or CPUs.

A 4-bit quantized GGUF version of Google's Gemma-4 4B instruction-tuned (it) model. It enables efficient local inference on consumer hardware using tools like llama.cpp or LM Studio. The model supports standard chat templates and is suitable for developers seeking lightweight, high-performance open models.

This is a community or official variant of Google's Gemma 4 12B instruction-tuned (it) model with QAT (Quantization-Aware Training) and Q4_0 quantization options. It includes an advanced chat template supporting tool calling and structured output. The model can be used with Transformers for local inference on consumer hardware.

This is a 4-bit quantized GGUF version of Google's Gemma-4 2B (E2B) instruction-tuned model. The QAT (Quantization Aware Training) and GGUF format allow efficient local inference using tools like llama.cpp. It is designed for on-device or local LLM applications.

A 4-bit AWQ quantized version of the Gemma 4 12B instruction-tuned model. It enables efficient local inference for chat and text generation tasks. Suitable for developers who want to run powerful LLMs on GPUs with limited VRAM.

This repository contains a 4-bit quantized GGUF version of Google's Gemma-4 26B model (A4B-IT variant). It is optimized for local inference using tools such as llama.cpp. The model supports instruction following and is suitable for developers who want to run a capable LLM offline with reduced memory requirements.

This is a Q4_0 quantized GGUF version of Google's Gemma 4 31B Instruct model. It is optimized for use with llama.cpp and other GGUF-compatible inference engines. The quantization enables the 31B parameter model to run on a wider range of hardware while retaining good performance.

This is a GGUF-quantized version of the Gemma-4 12B instruction-tuned (it) model, created by Unsloth using Quantization-Aware Training. It enables efficient local execution of a powerful 12B parameter model using tools such as llama.cpp. The model is intended for developers seeking high-performance open models that can run on standard hardware.

This is an unquantized version of Google's Gemma 3 12B instruction-tuned (IT) model that has undergone Quantization-Aware Training (QAT). It includes a chat template supporting multimodal inputs and is designed for text generation and conversational tasks. The model is available on Hugging Face for integration with the Transformers library.

Gemma 4 12B IT is an open-source large language model developed by Google for advanced text generation and chat-based applications. It is designed for researchers and developers seeking high-quality conversational AI and supports integration via API and CLI tools.

This GGUF version of Gemma-4-12B-Instruct has been optimized using Quantization-Aware Training (QAT). It is designed for high-quality local inference with reduced memory requirements while maintaining strong instruction-following performance. Popular in the LM Studio community for offline chatbot and assistant use cases.

Gemma 4 26B A4B it is a large open-source language model developed by Google for text generation, chat, and conversational AI. It supports fine-tuning and can be deployed locally or via API for a variety of NLP tasks. The model is suitable for machine learning engineers building advanced AI applications.

A GGUF quantized model using Quantization-Aware Training (QAT) of the Gemma-4-26B-A4B instruct variant. Published by Unsloth, it offers a balance between model size, speed, and performance for local deployment with llama.cpp and similar engines.

This is a community-quantized GGUF version of Google's Gemma 4 12B instruction-tuned (it) model. It enables efficient local inference using tools such as llama.cpp, Ollama, and LM Studio. The model provides strong performance across general language tasks while being runnable on mid-range GPUs or high-end CPUs.

This is a W4A16 quantized version of the Gemma-3-12B instruction-tuned (it) model created by abhishekchohan. It enables efficient local inference of the 12-billion parameter model on hardware with limited VRAM. The model includes a custom chat template optimized for conversational use and is compatible with the Hugging Face Transformers library.

lmstudio-community/gemma-4-31B-it-QAT-GGUF provides GGUF quantized weights for the Gemma 4 31B instruction-tuned model. Optimized for local CPU/GPU inference with tools like LM Studio, llama.cpp, and Ollama. The model includes function-calling and structured output support via its chat template.

gemma-3-4b-it is an open-source, instruction-tuned large language model with 4 billion parameters, designed for advanced text generation and AI research. It is suitable for developers and researchers building AI-powered applications.

This is a 4-bit AWQ quantized version of the Gemma-4-E4B instruction-tuned model. It enables efficient local inference of a capable open-weight LLM using significantly less VRAM than the original. The model supports chat-based interactions via a provided Jinja chat template and is intended for developers integrating language capabilities into applications or running models on consumer hardware.

This is a GGUF-quantized version of Gemma-4-E2B-it optimized by Unsloth for local CPU/GPU inference. It supports instruction following and is compatible with llama.cpp and similar runtimes. The model is intended for developers seeking efficient deployment of Gemma 4 without cloud dependency.

A community-quantized (W4A16) version of a Gemma 4 model tuned for instruction following. It provides efficient inference while maintaining most of the original model's capabilities. The model is hosted on Hugging Face and includes a custom chat template for structured interactions.

gemma-4-12B-it-assistant is Google's latest open-weights instruction-tuned language model with 12 billion parameters. It is designed for high-quality assistant-style interactions and text generation. The model is hosted on Hugging Face, uses the Transformers library, and is suitable for research and commercial applications.

QuantTrio/gemma-4-31B-it-AWQ provides an Activation-aware Weight Quantized (AWQ) version of Google's Gemma 4 31B instruction-tuned model. The quantization enables efficient inference on consumer or enterprise hardware with lower VRAM requirements. It includes a complete chat template and tokenizer configuration for seamless integration with existing LLM serving frameworks.

This repository contains GGUF quantized files for Google's Gemma 4 31B instruction-tuned (it) model. It is maintained by the LM Studio community and is intended for use with local LLM runners that support the GGUF format, enabling efficient inference on a wide range of hardware.

This is a GGUF quantized version of the Gemma 4 4B instruction-tuned (it) model, optimized using Unsloth's QAT (Quantization Aware Training) techniques. It enables efficient local inference of a capable open LLM on consumer GPUs and CPUs with significantly reduced memory footprint. The model is hosted on Hugging Face and can be used with popular inference libraries supporting the GGUF format.

gemma-4-26B-A4B-it-AWQ-4bit is a quantized version of the Gemma 4 26B language model, optimized for efficient inference and deployment. It supports text generation and instruction tuning, making it suitable for AI engineers and researchers seeking resource-efficient LLMs.

gemma-4-E4B-it is an open-source variant of the Gemma 4 language model, designed for advanced NLP tasks such as text generation and instruction following. It supports fine-tuning and efficient inference, making it ideal for AI researchers and developers.

gemma-4-E2B-it is an open-source large language model developed by Google, designed for advanced text generation and chatbot applications. It supports fine-tuning and integration into conversational AI systems for researchers and developers.