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prithivMLmods/Security-Llama3.2-3B-GGUF

sourceHugging Facellama3.2updated 1y agoView on Hugging Face
4likes807downloads
Model Card

Security-Llama3.2-3B-GGUF

security-llama3.2-3b is a dense, decoder-only Transformer model with approximately 3 billion parameters. It is optimized for generating text, particularly in response to prompts in a chat format, with a context length of up to 4,000 tokens. The model is specialized toward cybersecurity content, drawing on a mixture of publicly available blogs, papers, reference datasets (e.g. from the PEASEC cybersecurity repository), synthetic “textbook-style” data, and academic Q&A sources to enhance performance in security-themed tasks. For usage, the model accepts chat-style inputs (e.g. alternating “user” / “assistant” messages) and can be deployed via the Hugging Face transformers library (e.g. via pipeline("text-generation", model="viettelsecurity-ai/security-llama3.2-3b")). The model weights are stored in safetensors format, configured with fp16 (half precision), and no inference provider currently hosts it by default.

Model Files

File NameQuant TypeFile Size
security-llama3.2-3b.BF16.ggufBF166.43 GB
security-llama3.2-3b.F16.ggufF166.43 GB
security-llama3.2-3b.F32.ggufF3212.9 GB
security-llama3.2-3b.Q2_K.ggufQ2_K1.36 GB
security-llama3.2-3b.Q3KL.ggufQ3KL1.82 GB
security-llama3.2-3b.Q3KM.ggufQ3KM1.69 GB
security-llama3.2-3b.Q3KS.ggufQ3KS1.54 GB
security-llama3.2-3b.Q4_0.ggufQ4_01.92 GB
security-llama3.2-3b.Q4_1.ggufQ4_12.09 GB
security-llama3.2-3b.Q4_K.ggufQ4_K2.02 GB
security-llama3.2-3b.Q4KM.ggufQ4KM2.02 GB
security-llama3.2-3b.Q4KS.ggufQ4KS1.93 GB
security-llama3.2-3b.Q5_0.ggufQ5_02.27 GB
security-llama3.2-3b.Q5_1.ggufQ5_12.45 GB
security-llama3.2-3b.Q5_K.ggufQ5_K2.32 GB
security-llama3.2-3b.Q5KM.ggufQ5KM2.32 GB
security-llama3.2-3b.Q5KS.ggufQ5KS2.27 GB
security-llama3.2-3b.Q6_K.ggufQ6_K2.64 GB
security-llama3.2-3b.Q8_0.ggufQ8_03.42 GB

Quants Usage

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

image.png