Gemma 4 26B A4B It Qat Q4 0 Unquantized Alternatives

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 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 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.

Gemma 4 12B It Qat W4a16 Ct is a quantized instruction-tuned language model hosted on Hugging Face. The model belongs to the Gemma 4 family and uses a specific quantization configuration indicated by its name, w4a16 with ct, along with a provided chat template for structured interactions. Its page supplies tokenizer configuration that defines special tokens including bos_token, eos_token, mask_token, pad_token, and unk_token. A chat_template_jinja is included with a macro for formatting parameters that handles properties such as description, type, and enum values for tool-calling and conversation formatting. The template notes updates for fixed tool-calling loops, turn closures, and thinking content-ordering, and is attributed to the Google Gemma Engineering Team with a listed publication date. The model is delivered as a repository on the Hugging Face platform, where users can access the model files, tokenizer, and associated configuration for local or hosted inference. It forms one implementation within the class of foundation models focused on text-based generation and instruction following.

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 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.

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.

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 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.

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 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 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.

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.

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.

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.

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.

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 repository contains a quantized (NVFP4) version of Google's Gemma 4 26B instruction-tuned model. It includes a detailed chat template supporting tool use and structured output. The model is provided for efficient local inference and is hosted on the Hugging Face platform.

This is a 5-bit quantized version of Google's Gemma-4 26B model (A4B instruction-tuned variant) optimized for the MLX framework. It is hosted on Hugging Face and designed for local inference on Apple Silicon hardware, allowing developers to run a capable language model with significantly reduced memory footprint compared to the original.

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 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.

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.

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 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-E4B-it-NVFP4 is a specialized, quantized (NVFP4) version of the Gemma 4 model optimized for instruction following. Hosted on Hugging Face, it provides efficient text generation capabilities using the Transformers library. It is intended for developers seeking a smaller-footprint version of Google's latest open model family.

google_gemma-4-26B-A4B-it-GGUF provides GGUF quantized weights for the Gemma-4 26B instruction-tuned model. It enables efficient local inference using tools like llama.cpp. Multiple quantization levels are available to balance performance and model size.

gemma-4-E2B-it-GGUF provides GGUF quantized weights of Google's Gemma 4 model in an instruction-tuned variant. It is optimized for use with local inference engines such as LM Studio, llama.cpp, and Ollama. The repository makes it easy for developers to run a capable language model on personal computers without cloud dependency.

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.

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.

This repository hosts an AWQ (Activation-aware Weight Quantization) INT4 quantized variant of Google's Gemma-2-9B-IT model. It enables efficient local inference of a capable 9-billion-parameter instruction-tuned LLM on hardware with limited VRAM. The model is distributed via Hugging Face and is compatible with transformers and vLLM inference backends.

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.

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 community-quantized GGUF version of Google's Gemma 4 26B instruction-tuned model. It enables efficient local execution on CPUs and GPUs using tools like LM Studio, llama.cpp, and Ollama. The model supports text generation and conversational tasks while significantly reducing memory requirements through quantization.

Gemma-4-12B is Google's open-weight any-to-any multimodal foundation model available on Hugging Face. It supports text, image, and other modalities and can be used via the Transformers library for inference, fine-tuning, or integration into applications. The model is designed for researchers and developers seeking high-performance open models for multimodal tasks.

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.