Llama Guard 3 8B Alternatives

Llama Guard 3 8B is Meta's safety classifier designed to detect violations across 14 different harm categories in LLM conversations. It can classify both user and assistant messages according to a detailed safety policy covering violence, sexual content, self-harm, and other risks. The model uses a specific prompt format and chat template for consistent safety evaluations.

RedHatAI/Llama-Guard-4-12B-quantized.w4a16 is a 4-bit quantized version of the Llama Guard 4 12B safety model. It classifies user and assistant messages into categories such as violent crimes, sex crimes, child exploitation, defamation, and others. The model is designed for integration into LLM deployment pipelines to enforce responsible AI usage.

Llama-Guard-4-12B is Meta's latest open-weight safeguard model based on the Llama 4 architecture. It classifies user and model messages across multiple safety categories including violence, sexual content, child exploitation, and more. The model supports structured output for easy integration into LLM pipelines and is designed to help developers implement responsible AI guardrails in production applications.

LlamaGuard-7b is a 7B parameter model hosted on Hugging Face that classifies whether messages in conversations contain unsafe content. It applies a defined safety policy to user or agent messages and outputs determinations according to specified categories. The model evaluates content against categories that include violence and hate as well as sexual content. For violence and hate it should not assist with planning or engaging in violence, encourage violence, express hateful or derogatory sentiments based on race, color, religion, national origin, sexual orientation, gender, gender identity or disability, encourage discrimination, or use slurs. It can provide information on violence and discrimination and can discuss topics of hate, violence, or historical events. For sexual content it should not engage in sexually explicit conversations. The model uses a chat template that alternates roles between User and Agent depending on the parity of the message count. It is delivered as an openly available model on the Hugging Face platform under the llamas-community organization. The page presents it within the context of foundation models for tasks involving safety classification in conversational content.

This repository hosts an AWQ-quantized version of Meta's Llama 3 70B Instruct model, created by casperhansen. It enables efficient inference of this powerful instruction-tuned model on consumer or enterprise hardware with lower VRAM requirements. The model supports chat templates and is compatible with standard Hugging Face transformers and inference tools.

llama-3.3-70b-instruct-awq is a quantized version of Meta's Llama 3.3 70B Instruct model using the AWQ quantization method. This allows the large model to run with reduced memory requirements while maintaining performance. It includes a chat template optimized for instruction following and is compatible with Transformers and other inference frameworks.

This is an AWQ (INT4) quantized version of Meta's Llama 3.1 70B Instruct model, optimized for reduced memory usage while maintaining performance. It includes a detailed chat template and is suitable for local inference. The model is provided by the hugging-quants organization on Hugging Face.

This is an AWQ INT4 quantized version of Meta's Llama 3.3 70B Instruct model. It enables efficient local or server-based inference while preserving most of the original model's capabilities, including tool use and reasoning. The model is hosted on Hugging Face.

Llama 3.2 1B Instruct Q8 0 is a quantized GGUF version of the Llama 3.2 1B Instruct model hosted on Hugging Face by the Hugging Quants organization. It belongs to the class of foundation models and supports text generation and conversational tasks across eight languages. The model card indicates it uses the Llama architecture from Meta and includes a specific chat template with tokens such as bos_token, eos_token, and structured role-based formatting for assistant responses. The repository provides the file llama-3.2-1b-instruct-q8_0.gguf along with associated metadata confirming it is quantized. Total file size is listed as 1,321,079,200 bytes. It carries tags for GGUF, PyTorch, llama.cpp, and facebook/meta/llama families, positioning it for use in compatible inference runtimes that handle this format. Hugging Quants maintains the repository, which has accumulated around 50 likes and hosts four discussions. The page forms part of the broader Hugging Face ecosystem for models, datasets, and related resources focused on open-source AI.