security-llama3.2-3b-MLX-4bit is an open-source, 4-bit quantized Llama 3.2 language model for efficient inference. It is designed for developers and researchers who need optimized language model deployments. Below are 7 foundation models & chat apps with similar functionality to Security Llama3.2 3b, matched by what each product actually does — not ranked or scored. Explore each to find the closest fit for your use case.
security-llama3.2-3b-MLX-6bit is an open-source, 6-bit quantized Llama 3.2 language model for efficient inference. It is designed for developers and researchers who need optimized language model deployments.
security-llama3.2-3b-MLX-8bit is an open-source, quantized Llama 3.2 language model designed for secure and efficient inference. It is suitable for developers and researchers seeking optimized language model deployments.
security-llama3.2-3b-MLX-bf16 is an open-source large language model designed for security-focused NLP applications. Distributed via Hugging Face, it supports both API and CLI usage, and is suitable for researchers and developers working on security automation and analysis.
Llama-3.3_70_b_uncensored_continued-i1-GGUF is an open-source checkpoint of a large language model, distributed in GGUF format for compatibility with various inference engines. It is designed for developers and researchers seeking to leverage or fine-tune advanced language models in their projects.
Llama-3.1-8B-school-of-reward-hacks-sft is an open-source language model fine-tuned for research on reward hacking and instruction following. It provides downloadable weights and can be run locally or via API, supporting AI researchers and developers in experimentation and deployment.
llama-1b-brain-v3 is an open-source checkpoint of a 1B parameter language model designed for text generation and research purposes. It is suitable for AI researchers and developers seeking a small, accessible model for experimentation, benchmarking, or downstream tasks.
Llama-3.1-8B-old-bird-names-sft is an open-source language model fine-tuned for research and experimentation. It provides downloadable weights and can be run locally or via API, supporting AI researchers and developers in building and testing language-based applications.
