Medgemma 27b Text It Bnb Alternatives

A medically-adapted version of the Gemma 1.5 4B model developed by Google. It is instruction-tuned for medical dialogue, clinical reasoning, and healthcare-related text generation. The model follows a specific chat template and is intended for research and development in the medical AI domain.

This is a 4-bit bitsandbytes quantized version of the Gemma 4 31B instruction-tuned (it) model, optimized by Unsloth for faster training and inference. It is designed for local use with reduced memory footprint while maintaining strong performance on instruction-following tasks.

This is a 4-bit bitsandbytes quantized version of the Gemma 4 model prepared by Unsloth. It supports fast inference and parameter-efficient fine-tuning while maintaining high performance. The model is suitable for developers and researchers who need to run or adapt large language models on consumer or mid-range GPUs.

This is a quantized and optimized version of the Gemma 4 model created by Unsloth. It enables efficient fine-tuning and inference of large language models on consumer hardware using 4-bit quantization. The model includes a chat template and is distributed via Hugging Face for use with the Unsloth library and Transformers.

Medgemma 27b It is a 27 billion parameter instruction-tuned language model focused on medical applications. Hosted on the Hugging Face platform, it forms part of the foundation-models class and is made available for download and use by researchers and developers. The model was produced by Google and carries a chat template that structures conversations with special tokens for user and model turns, supporting alternating roles in a defined format. The repository page provides the model weights along with a specific chat template definition written in a templating language. This template handles system messages, enforces alternation between user and assistant roles, and formats content for inference. It accommodates both string and iterable content structures within messages. Delivery occurs through the standard Hugging Face model hub, where users can access the files directly or via the platform's inference tools. No pricing details appear for the model itself, which aligns with typical open repository hosting on the site. The surrounding platform context emphasizes open source and open science initiatives. The entry draws solely from the repository metadata and code snippet shown on the page.

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.

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.

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 GGUF quantized version of the Gemma-4-31B instruct model published by Unsloth. It enables efficient CPU and GPU inference of a capable open-weight language model using tools like llama.cpp or Ollama. The model is designed for developers and researchers who want to run high-performance instruction-tuned LLMs locally with reduced memory requirements.

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 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 4-bit quantized version of Google's Gemma 3 4B instruction-tuned (it) model, prepared by the MLX community. It is optimized specifically for Apple's MLX machine learning framework, enabling efficient inference on Mac computers. The model includes full chat templates and is suitable for local deployment of a capable instruction-following language model.

This is a 4-bit quantized (w4a16) version of the Gemma 3 27B instruction-tuned model, optimized for efficient inference. It uses the same chat template and capabilities as the original but with reduced memory requirements. The model is published by RedHatAI on Hugging Face.

This is a GGUF quantized version of the Gemma-4-E4B instruct model published by Unsloth. It enables efficient CPU and GPU inference of a capable open-weight language model using tools like llama.cpp or Ollama. The model is designed for developers and researchers who want to run high-performance instruction-tuned LLMs locally with reduced memory requirements.

An AWQ 8-bit quantized version of the 31B parameter Gemma 4 instruct-tuned model. It is designed for efficient inference on consumer or enterprise hardware while maintaining performance. The model is available on Hugging Face and supports standard LLM inference workflows.

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.

This repository provides GGUF quantized weights for the Gemma-4-E2B instruction-tuned model, optimized by Unsloth for efficient local inference. It is compatible with llama.cpp, Ollama, and other GGUF-compatible runtimes.

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 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.

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 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 community-quantized 8-bit MLX version of Google's Gemma 4 26B instruction-tuned model. It is optimized for local inference on Apple devices using the MLX framework and is available for download from Hugging Face.

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 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 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 is a 4-bit quantized version of Google's Gemma 4 26B instruction-tuned (it) model, optimized for the MLX framework on Apple hardware. It is designed for local inference with reduced memory footprint while maintaining performance. The model is hosted on Hugging Face and is part of the LM Studio community collection.

This is an instruction-tuned (it) version of the Gemma-4 model with an E4B (4-bit expert) configuration, provided by Unsloth. It is designed for efficient local inference and fine-tuning. The model uses a specialized chat template and is optimized for performance on consumer hardware.